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Li, Zhi, Zhefeng Wang, Zhicheng Wei, et al. "Cross-Oilfield Reservoir Classification via Multi-Scale Sensor Knowledge Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4215–23. http://dx.doi.org/10.1609/aaai.v35i5.16545.
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Reservoir classification is an essential step for the exploration and production process in the oil and gas industry. An appropriate automatic reservoir classification will not only reduce the manual workloads of experts, but also help petroleum companies to make optimal decisions efficiently, which in turn will dramatically reduce the costs. Existing methods mainly focused on generating reservoir classification in a single geological block but failed to work well on a new oilfield block. Indeed, how to transfer the subsurface characteristics and make accurate reservoir classification across the geological oilfields is a very important but challenging problem. To that end, in this paper, we present a focused study on the cross-oilfield reservoir classification task. Specifically, we first propose a Multi-scale Sensor Extraction (MSE) to extract the multi-scale feature representations of geological characteristics from multivariate well logs. Furthermore, we design an encoder-decoder module, Specific Feature Learning (SFL), to take advantage of specific information of both oilfields. Then, we develop a Knowledge-Attentive Transfer (KAT) module to learn the feature-invariant representation and transfer the geological knowledge from a source oilfield to a target oilfield. Finally, we evaluate our approaches by conducting extensive experiments with real-world industrial datasets. The experimental results clearly demonstrate the effectiveness of our proposed approaches to transfer the geological knowledge and generate the cross-oilfield reservoir classifications.
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Zhang, Lilun, Dezhi Wang, Changchun Bao, Yongxian Wang, and Kele Xu. "Large-Scale Whale-Call Classification by Transfer Learning on Multi-Scale Waveforms and Time-Frequency Features." Applied Sciences 9, no. 5 (2019): 1020. http://dx.doi.org/10.3390/app9051020.
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Whale vocal calls contain valuable information and abundant characteristics that are important for classification of whale sub-populations and related biological research. In this study, an effective data-driven approach based on pre-trained Convolutional Neural Networks (CNN) using multi-scale waveforms and time-frequency feature representations is developed in order to perform the classification of whale calls from a large open-source dataset recorded by sensors carried by whales. Specifically, the classification is carried out through a transfer learning approach by using pre-trained state-of-the-art CNN models in the field of computer vision. 1D raw waveforms and 2D log-mel features of the whale-call data are respectively used as the input of CNN models. For raw waveform input, windows are applied to capture multiple sketches of a whale-call clip at different time scales and stack the features from different sketches for classification. When using the log-mel features, the delta and delta-delta features are also calculated to produce a 3-channel feature representation for analysis. In the training, a 4-fold cross-validation technique is employed to reduce the overfitting effect, while the Mix-up technique is also applied to implement data augmentation in order to further improve the system performance. The results show that the proposed method can improve the accuracies by more than 20% in percentage for the classification into 16 whale pods compared with the baseline method using groups of 2D shape descriptors of spectrograms and the Fisher discriminant scores on the same dataset. Moreover, it is shown that classifications based on log-mel features have higher accuracies than those based directly on raw waveforms. The phylogeny graph is also produced to significantly illustrate the relationships among the whale sub-populations.
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Van, Phuong Ngoc Truc, and Vinh Minh Le. "Visualization design for choropleths in multi-scale statistical mapping." Science and Technology Development Journal 19, no. 2 (2016): 51–58. http://dx.doi.org/10.32508/stdj.v19i2.666.
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Multi-scale maps are one of those which are produced and displayed on screens at different levels of scale. Therefore, multi-scale mapping needs new approachs and concepts. This article introduces principles of multi-scale choropleth maps. The principles are based on cartographic principles and the screen environment. The scale ranges are defined by readable smallest area units. There is a changeover to larger administrative units at a reduction of scale. Data classifications and color ramps (symbology) for different scale ranges satisfy traditional rules and be consistent throughout the ranges
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Ahmed, Soaad, Naira Elazab, Mostafa M. El-Gayar, Mohammed Elmogy, and Yasser M. Fouda. "Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification." Diagnostics 15, no. 11 (2025): 1361. https://doi.org/10.3390/diagnostics15111361.
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Background: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for accurate and efficient diagnostic methods. Methods: Traditional deep learning models often struggle with feature redundancy, suboptimal feature fusion, and inefficient selection of discriminative features, leading to limitations in classification performance. To address these challenges, we propose a new deep learning framework that leverages MAX-ViT for multi-scale feature extraction, ensuring robust and hierarchical representation learning. A gated attention fusion module (GAFM) is introduced to dynamically integrate the extracted features, enhancing the discriminative power of the fused representation. Additionally, we employ Harris Hawks optimization (HHO) for feature selection, reducing redundancy and improving classification efficiency. Finally, XGBoost is utilized for classification, taking advantage of its strong generalization capabilities. Results: We evaluate our model on the King Abdulaziz University Mammogram Dataset, categorized based on BI-RADS classifications. Experimental results demonstrate the effectiveness of our approach, achieving 98.2% for accuracy, 98.0% for precision, 98.1% for recall, 98.0% for F1-score, 98.9% for the area under the curve (AUC), and 95% for the Matthews correlation coefficient (MCC), outperforming existing state-of-the-art models. Conclusions: These results validate the robustness of our fusion-based framework in improving breast cancer diagnosis and classification.
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Zhou, Weixun, Yongxin Shi, and Xiao Huang. "Multi-View Scene Classification Based on Feature Integration and Evidence Decision Fusion." Remote Sensing 16, no. 5 (2024): 738. http://dx.doi.org/10.3390/rs16050738.
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Leveraging multi-view remote sensing images in scene classification tasks significantly enhances the precision of such classifications. This approach, however, poses challenges due to the simultaneous use of multi-view images, which often leads to a misalignment between the visual content and semantic labels, thus complicating the classification process. In addition, as the number of image viewpoints increases, the quality problem for remote sensing images further limits the effectiveness of multi-view image classification. Traditional scene classification methods predominantly employ SoftMax deep learning techniques, which lack the capability to assess the quality of remote sensing images or to provide explicit explanations for the network’s predictive outcomes. To address these issues, this paper introduces a novel end-to-end multi-view decision fusion network specifically designed for remote sensing scene classification. The network integrates information from multi-view remote sensing images under the guidance of image credibility and uncertainty, and when the multi-view image fusion process encounters conflicts, it greatly alleviates the conflicts and provides more reasonable and credible predictions for the multi-view scene classification results. Initially, multi-scale features are extracted from the multi-view images using convolutional neural networks (CNNs). Following this, an asymptotic adaptive feature fusion module (AAFFM) is constructed to gradually integrate these multi-scale features. An adaptive spatial fusion method is then applied to assign different spatial weights to the multi-scale feature maps, thereby significantly enhancing the model’s feature discrimination capability. Finally, an evidence decision fusion module (EDFM), utilizing evidence theory and the Dirichlet distribution, is developed. This module quantitatively assesses the uncertainty in the multi-perspective image classification process. Through the fusing of multi-perspective remote sensing image information in this module, a rational explanation for the prediction results is provided. The efficacy of the proposed method was validated through experiments conducted on the AiRound and CV-BrCT datasets. The results show that our method not only improves single-view scene classification results but also advances multi-view remote sensing scene classification results by accurately characterizing the scene and mitigating the conflicting nature of the fusion process.
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Lim, Ee Hui, and David Suter. "3D terrestrial LIDAR classifications with super-voxels and multi-scale Conditional Random Fields." Computer-Aided Design 41, no. 10 (2009): 701–10. http://dx.doi.org/10.1016/j.cad.2009.02.010.
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Du, Yingjie, and Ning Ding. "A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods." ISPRS International Journal of Geo-Information 12, no. 6 (2023): 209. http://dx.doi.org/10.3390/ijgi12060209.
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Crime is always one of the most important social problems, and it poses a great threat to public security and people. Accurate crime prediction can help the government, police, and citizens to carry out effective crime prevention measures. In this paper, the research on crime prediction is systematically reviewed from a variety of temporal and spatial perspectives. We describe the current state of crime prediction research from four perspectives (prediction content, crime types, methods, and evaluation) and focus on the prediction methods. According to various temporal and spatial scales, temporal crime prediction is divided into short-term prediction, medium-term prediction, and long-term prediction, and spatial crime prediction is divided into micro-, meso-, and macro-level prediction. Spatio-temporal crime prediction classification can be a permutation of temporal and spatial crime prediction classifications. A variety of crime prediction methods and evaluation metrics are also summarized, and different prediction methods and models are compared and evaluated. After sorting out the literature, it was found that there are still many limitations in the current research: (i) data sparsity is difficult to deal with effectively; (ii) the practicality, interpretability, and transparency of predictive models are insufficient; (iii) the evaluation system is relatively simple; and (iv) the research on decision-making application is lacking. In this regard, the following suggestions are proposed to solve the above problems: (i) the use of transformer learning technology to deal with sparse data; (ii) the introduction of model interpretation methods, such as Shapley additive explanations (SHAPs), to improve the interpretability of the models; (iii) the establishment of a set of standard evaluation systems for crime prediction at different scales to standardize data use and evaluation metrics; and (iv) the integration of reinforcement learning to achieve more accurate prediction while promoting the transformation of the application results.
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Li, Dengao, Ye Tao, Jumin Zhao, and Hang Wu. "Classification of Congestive Heart Failure from ECG Segments with a Multi-Scale Residual Network." Symmetry 12, no. 12 (2020): 2019. http://dx.doi.org/10.3390/sym12122019.
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Congestive heart failure (CHF) poses a serious threat to human health. Once the diagnosis of CHF is established, clinical experts need to assess the severity of CHF in a timely manner. It is proved that electrocardiogram (ECG) signals are useful for assessing the severity of CHF. However, since the ECG perturbations are subtle, it is difficult for doctors to detect the differences of ECGs. In order to help doctors to make an accurate diagnosis, we proposed a novel multi-scale residual network (ResNet) to automatically classify CHF into four classifications according to the New York Heart Association (NYHA) functional classification system. Furthermore, in order to make the reported results more realistic, we used an inter-patient paradigm to divide the dataset, and segmented the ECG signals into two different intervals. The experimental results show that the proposed multi-scale ResNet-34 has achieved an average positive predictive value, sensitivity and accuracy of 93.49%, 93.44% and 93.60% respectively for two seconds of ECG segments. We have also obtained an average positive predictive value, sensitivity and accuracy of 94.16%, 93.79% and 94.29% respectively for five seconds of ECG segments. The proposed method can be used as an auxiliary tool to help doctors to classify CHF.
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Liu, Yanzhang, Jinqi Cai, and Guirong Tan. "Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network." Atmosphere 13, no. 11 (2022): 1861. http://dx.doi.org/10.3390/atmos13111861.
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Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning CNN (Convolutional Neural Networks) classification model is established to classify the circulation patterns, along with the newly reconstructed dataset of regional persistent historical heavy rain events, daily rainfall data of 2474 observational stations, and the NCEP/NCAR global reanalysis data of daily geopotential height field in 1981–2018. Different from previous classifications, usually with one level variable, here, in addition to 500 hPa heights, 200 hPa zonal winds and 850 hPa meridional winds over the key areas are also considered in the model. The results show that the multi-level circulation pattern classification based on the transfer learning CNN network has a higher accuracy in the independent test than the single-level model, with the accuracy reaching 92.5% (while only 85% for the single-level model). The spatial correlation coefficient of precipitation between each typical mode and related patterns obtained by the multi-level transfer learning CNN classification is greater than that obtained by the single-level transfer learning CNN, and the variance of 500 hPa heights between each typical mode and the associated patterns is also greater than that obtained by the single-level transfer learning CNN. These results show that the performance of the classification by the multi-level transfer learning CNN model is better than that by the single-level transfer learning CNN. The study is helpful to develop circulation classifications related to large-scale weather or climate disaster events and then to provide a physical basis for further improving the forecast effect and extending the valid time of the forecast through combining the numerical model products.
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Kalacska, Margaret, Oliver Lucanus, Leandro Sousa, and J. Pablo Arroyo-Mora. "A New Multi-Temporal Forest Cover Classification for the Xingu River Basin, Brazil." Data 4, no. 3 (2019): 114. http://dx.doi.org/10.3390/data4030114.
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We describe a new multi-temporal classification for forest/non-forest classes for a 1.3 million square kilometer area encompassing the Xingu River basin, Brazil. This region is well known for its exceptionally high biodiversity, especially in terms of the ichthyofauna, with approximately 600 known species, 10% of which are endemic to the river basin. Global and regional scale datasets do not adequately capture the rapidly changing land cover in this region. Accurate forest cover and forest cover change data are important for understanding the anthropogenic pressures on the aquatic ecosystems. We developed the new classifications with a minimum mapping unit of 0.8 ha from cloud free mosaics of Landsat TM5 and OLI 8 imagery in Google Earth Engine using a classification and regression tree (CART) aided by field photographs for the selection of training and validation points.
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Akcay, Ozgun, Emin Avsar, Melis Inalpulat, Levent Genc, and Ahmet Cam. "Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery." ISPRS International Journal of Geo-Information 7, no. 11 (2018): 424. http://dx.doi.org/10.3390/ijgi7110424.
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Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.
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Wang, Shunli, Rui Li, Jie Jiang, and Yao Meng. "Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan." Future Internet 13, no. 10 (2021): 251. http://dx.doi.org/10.3390/fi13100251.
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In the context of rapid urbanization, the refined management of cities is facing higher requirements. In improving urban population management levels and the scientific allocation of resources, fine-scale population data plays an increasingly important role. The current population estimation studies mainly focus on low spatial resolution, such as city-scale and county scale, without considering differences in population distributions within cities. This paper mines and defines the spatial correlations of multi-source data, including urban building data, point of interest (POI) data, census data, and administrative division data. With populations mainly distributed in residential buildings, a population estimation model at a subdistrict scale is established based on building classifications. Composed of spatial information and attribute information, POI data are spaced irregularly. Based on this characteristic, the text classification method, frequency-inverse document frequency (TF-IDF), is applied to obtain functional classifications of buildings. Then we screen out residential buildings, and quantify characteristic variables in subdistricts, including perimeter, area, and total number of floors in residential buildings. To assess the validity of the variables, the random forest method is selected for variable screening and correlation analysis, because this method has clear advantages when dealing with unbalanced data. Under the assumption of linearity, multiple regression analysis is conducted, to obtain a linear model of the number of buildings, their geometric characteristics, and the population in each administrative division. Experiments showed that the urban fine-scale population estimation model established in this study can estimate the population at a subdistrict scale with high accuracy. This method improves the precision and automation of urban population estimation. It allows the accurate estimation of the population at a subdistrict scale, thereby providing important data to support the overall planning of regional energy resource allocation, economic development, social governance, and environmental protection.
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Brown de Colstoun, Eric C., and Charles L. Walthall. "Improving global scale land cover classifications with multi-directional POLDER data and a decision tree classifier." Remote Sensing of Environment 100, no. 4 (2006): 474–85. http://dx.doi.org/10.1016/j.rse.2005.11.003.
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Zhong, Chun, Shihong Zeng, and Hongqiu Zhu. "Adaptive Multimodal Fusion with Cross-Attention for Robust Scene Segmentation and Urban Economic Analysis." Applied Sciences 15, no. 1 (2025): 438. https://doi.org/10.3390/app15010438.
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With the increasing demand for accurate multimodal data analysis in complex scenarios, existing models often struggle to effectively capture and fuse information across diverse modalities, especially when data include varying scales and levels of detail. To address these challenges, this study presents an enhanced Swin Transformer V2-based model designed for robust multimodal data processing. The method analyzes urban economic activities and spatial layout using satellite and street view images, with applications in traffic flow and business activity intensity, highlighting its practical significance. The model incorporates a multi-scale feature extraction module into the window attention mechanism, combining local and global window attention with adaptive pooling to achieve comprehensive multi-scale feature fusion and representation. This approach enables the model to effectively capture information at different scales, enhancing its expressiveness in complex scenes. Additionally, a cross-attention-based multimodal feature fusion mechanism integrates spatial structure information from scene graphs with Swin Transformer’s image classification outputs. By calculating similarities and correlations between scene graph embeddings and image classifications, this mechanism dynamically adjusts each modality’s contribution to the fused representation, leveraging complementary information for a more coherent multimodal understanding. Compared with the baseline method, the proposed bimodal model performs superiorly and the accuracy is improved by 3%, reaching 91.5%, which proves its effectiveness in processing and fusing multimodal information. These results highlight the advantages of combining multi-scale feature extraction and cross-modal alignment to improve performance on complex multimodal tasks.
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Chen, Siting, Bingjie Yu, Guang Shi, Yiping Cai, Yanyu Wang, and Pingge He. "Scale-Dependent Relationships Between Urban Morphology and Noise Perception: A Multi-Scale Spatiotemporal Analysis in New York City." Land 14, no. 3 (2025): 476. https://doi.org/10.3390/land14030476.
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Urban morphology significantly influences residents’ noise perceptions, yet the impact across different spatial and temporal scales remains unclear. This study investigates the scale-dependent relationship between urban morphology and noise perception in New York City using noise complaint rates (NCR) as a proxy for perceived noise levels. A multi-scale analysis framework was applied, including four spatial scales (100 m, 200 m, 500 m, and 1000 m) and three temporal classifications (daytime/nighttime/dawn, weekdays/weekends, and seasonal divisions). Statistical analyses, including Spearman correlation, Moran’s I test, and Geographically Weighted Regression (GWR), examined spatiotemporal heterogeneity. Results show: (1) NCR and urban morphology indicators vary significantly across spatial and temporal aggregations. (2) Correlations between NCR and urban morphology indicators generally strengthen with larger spatial units, revealing a scale effect. Temporal variations, e.g., residential land ratio (RES) and greenery percentage (SVI Green), show stronger correlations with NCR in summer than in winter. (3) The Moran’s I index revealed significant spatial clustering at the 1000 m scale. Multi-temporal GWR analysis revealed spatial variations in urban morphology-noise relationships across different temporal contexts; in residential areas, building density exacerbates complaints more during non-working periods than during working hours. This study enhances understanding of urban sound environments, offering insights required for more precise urban planning policies.
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Räsänen, Aleksi, Terhikki Manninen, Mika Korkiakoski, Annalea Lohila, and Tarmo Virtanen. "Predicting catchment-scale methane fluxes with multi-source remote sensing." Landscape Ecology 36, no. 4 (2021): 1177–95. http://dx.doi.org/10.1007/s10980-021-01194-x.
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Abstract Context Spatial patterns of CH4 fluxes can be modeled with remotely sensed data representing land cover, soil moisture and topography. Spatially extensive CH4 flux measurements conducted with portable analyzers have not been previously upscaled with remote sensing. Objectives How well can the CH4 fluxes be predicted with plot-based vegetation measures and remote sensing? How does the predictive skill of the model change when using different combinations of predictor variables? Methods We measured CH4 fluxes in 279 plots in a 12.4 km2 peatland-forest-mosaic landscape in Pallas area, northern Finland in July 2019. We compared 20 different CH4 flux maps produced with vegetation field data and remote sensing data including Sentinel-1, Sentinel-2 and digital terrain model (DTM). Results The landscape acted as a net source of CH4 (253–502 µg m−2 h−1) and the proportion of source areas varied considerably between maps (12–50%). The amount of explained variance was high in CH4 regressions (59–76%, nRMSE 8–10%). Regressions including remote sensing predictors had better performance than regressions with plot-based vegetation predictors. The most important remote sensing predictors included VH-polarized Sentinel-1 features together with topographic wetness index and other DTM features. Spatial patterns were most accurately predicted when the landscape was divided into sinks and sources with remote sensing-based classifications, and the fluxes were modeled for sinks and sources separately. Conclusions CH4 fluxes can be predicted accurately with multi-source remote sensing in northern boreal peatland landscapes. High spatial resolution remote sensing-based maps constrain uncertainties related to CH4 fluxes and their spatial patterns.
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Waldhoff, G., S. Eichfuss, and G. Bareth. "INTEGRATION OF REMOTE SENSING DATA AND BASIC GEODATA AT DIFFERENT SCALE LEVELS FOR IMPROVED LAND USE ANALYSES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 19, 2015): 85–89. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-85-2015.
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The classification of remote sensing data is a standard method to retrieve up-to-date land use data at various scales. However, through the incorporation of additional data using geographical information systems (GIS) land use analyses can be enriched significantly. In this regard, the Multi-Data Approach (MDA) for the integration of remote sensing classifications and official basic geodata for a regional scale as well as the achievable results are summarised. On this methodological basis, we investigate the enhancement of land use analyses at a very high spatial resolution by combining WorldView-2 remote sensing data and official cadastral data for Germany (the Automated Real Estate Map, ALK). Our first results show that manifold thematic information and the improved geometric delineation of land use classes can be gained even at a high spatial resolution.
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Lu, Lizhen, Yuan Tao, and Liping Di. "Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data." Remote Sensing 10, no. 11 (2018): 1820. http://dx.doi.org/10.3390/rs10111820.
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Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML’s eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new Object-Based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. Based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the—Classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1–3%.
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Lee, Chan, Im-hak Cho, Gi-yoon Heo, et al. "Analysis of the Numeric Rating Scale (NRS) Used in Clinical Studies Based on Randomized Controlled Studies." Journal of Internal Korean Medicine 42, no. 4 (2021): 510–31. http://dx.doi.org/10.22246/jikm.2021.42.4.510.
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Objectives: The purpose of this study was to review the status of numeric rating scale (NRS) usage and suggest the potential for use in multicenter retrospective studies of various diseases.Methods: Articles published from 2011 to 2021 that used the keywords “NRS", “Multi-center", and “RCT" were identified in foreign databases, including EMBASE, PubMed, CENTRAL. The articles were analyzed according to their use of "NRS" by symptoms and by disease group using the major classifications of the Korean Standard Classification of Diseases (KCD-7).Results: Classification by symptom in a total of 288 articles illustrates that the NRS was not only commonly used in pain evaluation but also for non-pain symptoms. In usage with non-pain symptoms, chief complaint of patients was the most common at 79%, and other factors included treatment satisfaction, evaluation of daily life, and sleep quality. In disease classification according to the KCD-7, the NRS was commonly used in connection with musculoskeletal and connective tissue diseases but was also utilized in various other disease groups.Conclusions: This study confirms usage of the NRS in multi-center RCTs, as the NRS was widely used in all types of diseases and symptoms. Considering the result and the advantages of the NRS, it is recommended for use as a daily evaluation tool for the collection of common data in multicenter retrospective studies.
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Elwan, Ehsan, Michel Le Page, Lionel Jarlan, et al. "Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data." Water 14, no. 5 (2022): 804. http://dx.doi.org/10.3390/w14050804.
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This study aims to propose an operational approach to map irrigated areas based on the synergy of Sentinel-1 (S1) and Sentinel-2 (S2) data. An application is proposed at two study sites in Europe—in Spain and in Italy—with two climatic contexts (semiarid and humid, respectively), with the objective of proving the essential role of multi-site training for a robust application of the proposed methodologies. Several classifiers are proposed to separate irrigated and rainfed areas. They are based on statistical variables from Sentinel-1 and Sentinel-2 time series data at the agricultural field scale, as well as on the contrasted behavior between the field scale and the 5 km surroundings. The support vector machine (SVM) classification approach was tested with different options to evaluate the robustness of the proposed methodologies. The optimal number of metrics found is five. These metrics illustrate the importance of optical/radar synergy and the consideration of multi-scale spatial information. The highest accuracy of the classifications, approximately equal to 85%, is based on training dataset with mixed reference fields from the two study sites. In addition, the accuracy is consistent at the two study sites. These results confirm the potential of the proposed approaches towards the most general use on sites with different climatic and agricultural contexts.
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Yang, Zhi, Kang Li, Haitao Gan, Zhongwei Huang, Ming Shi, and Ran Zhou. "An Alzheimer's Disease classification network based on MRI utilizing diffusion maps for multi-scale feature fusion in graph convolution." Mathematical Biosciences and Engineering 21, no. 1 (2023): 1554–72. http://dx.doi.org/10.3934/mbe.2024067.
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<abstract><p>Graph convolutional networks (GCN) have been widely utilized in Alzheimer's disease (AD) classification research due to its ability to automatically learn robust and powerful feature representations. Inter-patient relationships are effectively captured by constructing patients magnetic resonance imaging (MRI) data as graph data, where nodes represent individuals and edges denote the relationships between them. However, the performance of GCNs might be constrained by the construction of the graph adjacency matrix, thereby leading to learned features potentially overlooking intrinsic correlations among patients, which ultimately causes inaccurate disease classifications. To address this issue, we propose an Alzheimer's disease Classification network based on MRI utilizing diffusion maps for multi-scale feature fusion in graph convolution. This method aims to tackle the problem of features neglecting intrinsic relationships among patients while integrating features from diffusion mapping with different neighbor counts to better represent patients and achieve an accurate AD classification. Initially, the diffusion maps method conducts diffusion information in the feature space, thus breaking free from the constraints of diffusion based on the adjacency matrix. Subsequently, the diffusion features with different neighbor counts are merged, and a self-attention mechanism is employed to adaptively adjust the weights of diffusion features at different scales, thereby comprehensively and accurately capturing patient characteristics. Finally, metric learning techniques enhance the similarity of node features within the same category in the graph structure and bring node features of different categories more distant from each other. This study aims to enhance the classification accuracy of AD, by providing an effective tool for early diagnosis and intervention. It offers valuable information for clinical decisions and personalized treatment. Experimentation on the publicly accessible Alzheimer's disease neuroimaging initiative (ADNI) dataset validated our method's competitive performance across various AD-related classification tasks. Compared to existing methodologies, our approach captures patient characteristics more effectively and demonstrates superior generalization capabilities.</p></abstract>
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Mahdianpari, Masoud, Bahram Salehi, Fariba Mohammadimanesh, Saeid Homayouni, and Eric Gill. "The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform." Remote Sensing 11, no. 1 (2018): 43. http://dx.doi.org/10.3390/rs11010043.
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Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.
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Brauchler, Melanie, Johannes Stoffels, and Sascha Nink. "Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2." Remote Sensing 14, no. 3 (2022): 727. http://dx.doi.org/10.3390/rs14030727.
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Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some information needs, spatially very high-resolution imagery (i.e., aerial imagery, ≤2 m) is needed to generate maps at a scale suitable for regional and local applications. In this study, we present the development, implementation, and evaluation of a Geographic Object-Based Image Analysis (GEOBIA) framework to stratify forests (needleleaved, broadleaved, non-forest) in Luxembourg. The framework is exclusively based on open data and free and open-source geospatial software. Although aerial imagery is used to derive image objects with a 0.05 ha minimum size, Sentinel-2 scenes of 2020 are the basis for random forest classifications in different single-date and multi-temporal feature setups. These setups are compared with each other and used to evaluate the framework against classifications based on features derived from aerial imagery. The highest overall accuracies (89.3%) have been achieved with classification on a Sentinel-2-based vegetation index time series (n = 8). Similar accuracies have been achieved with classification based on two (88.9%) or three (89.1%) Sentinel-2 scenes in the greening phase of broadleaved forests. A classification based on color infrared aerial imagery and derived texture measures only achieved an accuracy of 74.5%. The integration of the texture measures into the Sentinel-2-based classification did not improve its accuracy. Our results indicate that high resolution image objects can successfully be stratified based on lower spatial resolution Sentinel-2 single-date and multi-temporal features, and that those setups outperform classifications based on aerial imagery only. The conceptual framework of spatially high-resolution image objects enriched with features from lower resolution imagery facilitates the delivery of frequent and reliable updates due to higher spectral and temporal resolution. The framework additionally holds the potential to derive additional information layers (i.e., forest disturbance) as derivatives of the features attached to the image objects, thus providing up-to-date information on the state of observed forests.
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Li, Hongju, Ying Liu, Yuechang Wang, and Haoran Liao. "Estimation Method of Ideal Fractal Parameters for Multi-Scale Measurement of Polished Surface Topography." Fractal and Fractional 7, no. 1 (2022): 17. http://dx.doi.org/10.3390/fractalfract7010017.
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A surface topography characterization parameter system based on fractal parameters has been established, and several estimation methods for these fractal parameters have been suggested accordingly. Since scale dependence exists in these conventional methods, it is necessary to find an estimation method for characterization parameters with uniqueness. An estimation method for ideal fractal parameters for multi-scale measurement of polished surface topography is proposed in this study. Polished surfaces of two materials, WC-Ni and 9Cr18Mo, are measured under multi-scale for frequency component analysis. This study proposes an estimation method for ideal fractal parameters based on a modified determination method for the scale-free region and the decomposition of frequency components into three classifications. The reasonable results verify the existence of ideal fractal parameters: for the WC-Ni surface, ideal fractal dimension D = 1.3 and scale coefficient G = 2.23×1020 μm; for the 9Cr18Mo surface, ideal fractal dimension D = 1.2 and scale coefficient G = 3.33×1033 μm. Additionally, it is revealed that the scale-dependent components conform to the same regulation on the same instrument by comparing the results of two materials. The conclusions of this study are expected to support tribology research and mechanical engineering related to surface topography.
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Aljwari, Fatima, Wahaj Alkaberi, Areej Alshutayri, Eman Aldhahri, Nahla Aljojo, and Omar Abouola. "Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News." Postmodern Openings 13, no. 1 Sup1 (2022): 01–14. http://dx.doi.org/10.18662/po/13.1sup1/411.
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There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.
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Sarkar, Ovi, Md Robiul Islam, Md Khalid Syfullah, et al. "Multi-Scale CNN: An Explainable AI-Integrated Unique Deep Learning Framework for Lung-Affected Disease Classification." Technologies 11, no. 5 (2023): 134. http://dx.doi.org/10.3390/technologies11050134.
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Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-Ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification.
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Xu, W., B. Hays, R. Fayrer-Hosken, and A. Presotto. "MODELING THE DISTRIBUTION OF AFRICAN SAVANNA ELEPHANTS IN KRUGER NATIONAL PARK: AN APPLICATION OF MULTI-SCALE GLOBELAND30 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1327–34. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1327-2016.
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The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (<i>Loxodonta africana</i>) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency’s Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.
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Li, Zhi, Yi Lu, and Xiaomei Yang. "Multi-Level Dynamic Analysis of Landscape Patterns of Chinese Megacities during the Period of 2016–2021 Based on a Spatiotemporal Land-Cover Classification Model Using High-Resolution Satellite Imagery: A Case Study of Beijing, China." Remote Sensing 15, no. 1 (2022): 74. http://dx.doi.org/10.3390/rs15010074.
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In today’s accelerating urbanization process, timely and effective monitoring of land-cover dynamics, landscape pattern analysis, and evaluation of built-up urban areas (BUAs) have important research significance and practical value for the sustainable development, planning and management, and ecological protection of cities. High-spatial-resolution remote sensing (HRRS) images have the advantages of high-accuracy Earth observations, covering a large area, and having a short playback period, and they can objectively and accurately provide fine dynamic spatial information about the land cover in urban built-up areas. However, the complexity and comprehensiveness of the urban structure have led to a single-scale analysis method, which makes it difficult to accurately and comprehensively reflect the characteristics of the BUA landscape pattern. Therefore, in this study, a joint evaluation method for an urban land-cover spatiotemporal-mapping chain and multi-scale landscape pattern using high-resolution remote sensing imagery was developed. First, a pixel–object–knowledge model with temporal and spatial classifications was proposed for the spatiotemporal mapping of urban land cover. Based on this, a multi-scale district–BUA–city block–land cover type map of the city was established and a joint multi-scale evaluation index was constructed for the multi-scale dynamic analysis of the urban landscape pattern. The accuracies of the land cover in 2016 and 2021 were 91.9% and 90.4%, respectively, and the kappa coefficients were 0.90 and 0.88, respectively, indicating that the method can provide effective and reliable information for spatial mapping and landscape pattern analysis. In addition, the multi-scale analysis of the urban landscape pattern revealed that, during the period of 2016–2021, Beijing maintained the same high urbanization rate in the inner part of the city, while the outer part of the city kept expanding, which also reflects the validity and comprehensiveness of the analysis method developed in this study.
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Kamal, Hesham, and Maggie Mashaly. "Robust Intrusion Detection System Using an Improved Hybrid Deep Learning Model for Binary and Multi-Class Classification in IoT Networks." Technologies 13, no. 3 (2025): 102. https://doi.org/10.3390/technologies13030102.
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The rapid expansion of internet of things (IoT) applications has significantly boosted productivity and streamlined daily activities. However, this widespread adoption has also introduced considerable security challenges, making IoT environments vulnerable to large-scale botnet attacks. These attacks have often succeeded in achieving their malicious goals, highlighting the urgent need for robust detection strategies to secure IoT networks. To overcome these obstacles, this research presents an innovative anomaly-driven intrusion detection approach specifically tailored for IoT networks. The proposed model employs an advanced hybrid architecture that seamlessly integrates convolutional neural networks (CNN) with multilayer perceptron (MLP), enabling precise detection and classification of both binary and multi-class IoT network traffic. The CNN component is responsible for extracting and enhancing features from network traffic data and preparing these features for effective classification by the MLP, which handles the final classification task. To further manage class imbalance, the model incorporates the enhanced hybrid adaptive synthetic sampling-synthetic minority oversampling technique (ADASYN-SMOTE) for binary classification, advanced ADASYN for multiclass classification, and employs edited nearest neighbors (ENN) alongside class weights. The CNN-MLP architecture is meticulously crafted to minimize erroneous classifications, enhance instantaneous threat detection, and precisely recognize previously unseen cyber intrusions. The model’s effectiveness was rigorously tested using the IoT-23 and NF-BoT-IoT-v2 datasets. On the IoT-23 dataset, the model achieved 99.94% accuracy in two-stage binary classification, 99.99% accuracy in multiclass classification excluding the normal class, and 99.91% accuracy in single-phase multiclass classification including the normal class. Utilizing the NF-BoT-IoT-v2 dataset, the model attained an exceptional 99.96% accuracy in the dual-phase binary classification paradigm, 98.02% accuracy in multiclass classification excluding the normal class, and 98.11% accuracy in single-phase multiclass classification including the normal class. The results demonstrate that our model consistently delivers high levels of accuracy, precision, recall, and F1 score across both binary and multiclass classifications, establishing it as a robust solution for securing IoT networks.
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Kamiya, Naoki, Jing Li, Masanori Kume, Hiroshi Fujita, Dinggang Shen, and Guoyan Zheng. "Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications." International Journal of Computer Assisted Radiology and Surgery 13, no. 11 (2018): 1697–706. http://dx.doi.org/10.1007/s11548-018-1852-1.
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Xu, W., B. Hays, R. Fayrer-Hosken, and A. Presotto. "MODELING THE DISTRIBUTION OF AFRICAN SAVANNA ELEPHANTS IN KRUGER NATIONAL PARK: AN APPLICATION OF MULTI-SCALE GLOBELAND30 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1327–34. http://dx.doi.org/10.5194/isprsarchives-xli-b8-1327-2016.
Full textAbstract:
The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (&lt;i&gt;Loxodonta africana&lt;/i&gt;) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency’s Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.
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Zabala-Blanco, David, Ruber Hernández-García, and Ricardo J. Barrientos. "SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images." Electronics 12, no. 17 (2023): 3608. http://dx.doi.org/10.3390/electronics12173608.
Full textAbstract:
Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.
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Li, Xingrong, Chenghai Yang, Hongri Zhang, et al. "Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning." Remote Sensing 13, no. 4 (2021): 801. http://dx.doi.org/10.3390/rs13040801.
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The jujube industry plays a very important role in the agricultural industrial structure of Xinjiang, China. In recent years, the abandonment of jujube fields has gradually emerged. It is critical to inventory the abandoned land soon after it is generated to adjust agricultural production better and prevent the negative impacts from the abandonment (such as outbreaks of diseases, insect pests, and fires). High-resolution multi-temporal satellite remote sensing images can be used to identify subtle differences among crops and provide a good tool for solving this problem. In this research, both field-based and pixel-based classification approaches using field boundaries were used to estimate the percentage of abandoned jujube fields with multi-temporal high spatial resolution satellite images (Gaofen-1 and Gaofen-6) and the Random Forest algorithm. The results showed that both approaches produced good classification results and similar distributions of abandoned fields. The overall accuracy was 91.1% for the field-based classification and 90.0% for the pixel-based classification, and the Kappa was 0.866 and 0.848 for the respective classifications. The areas of abandoned land detected in the field-based and pixel-based classification maps were 806.09 ha and 828.21 ha, respectively, accounting for 8.97% and 9.11% of the study area. In addition, feature importance evaluations of the two approaches showed that the overall importance of texture features was higher than that of vegetation indices and that 31 October and 10 November were important dates for abandoned land detection. The methodology proposed in this study will be useful for identifying abandoned jujube fields and have the potential for large-scale application.
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Warrens, Matthijs J. "On the Equivalence of Multirater Kappas Based on 2-Agreement and 3-Agreement with Binary Scores." ISRN Probability and Statistics 2012 (October 15, 2012): 1–11. http://dx.doi.org/10.5402/2012/656390.
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Cohen’s kappa is a popular descriptive statistic for summarizing agreement between the classifications of two raters on a nominal scale. With m≥3 raters there are several views in the literature on how to define agreement. The concept of g-agreement (g∈{2,3,…,m}) refers to the situation in which it is decided that there is agreement if g out of m raters assign an object to the same category. Given m≥2 raters we can formulate m−1 multirater kappas, one based on 2-agreement, one based on 3-agreement, and so on, and one based on m-agreement. It is shown that if the scale consists of only two categories the multi-rater kappas based on 2-agreement and 3-agreement are identical.
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Martin, François-Marie, Jana Müllerová, Laurent Borgniet, Fanny Dommanget, Vincent Breton, and André Evette. "Using Single- and Multi-Date UAV and Satellite Imagery to Accurately Monitor Invasive Knotweed Species." Remote Sensing 10, no. 10 (2018): 1662. http://dx.doi.org/10.3390/rs10101662.
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Understanding the spatial dynamics of invasive alien plants is a growing concern for many scientists and land managers hoping to effectively tackle invasions or mitigate their impacts. Consequently, there is an urgent need for the development of efficient tools for large scale mapping of invasive plant populations and the monitoring of colonization fronts. Remote sensing using very high resolution satellite and Unmanned Aerial Vehicle (UAV) imagery is increasingly considered for such purposes. Here, we assessed the potential of several single- and multi-date indices derived from satellite and UAV imagery (i.e., UAV-generated Canopy Height Models—CHMs; and Bi-Temporal Band Ratios—BTBRs) for the detection and mapping of the highly problematic Asian knotweeds (Fallopia japonica; Fallopia × bohemica) in two different landscapes (i.e., open vs. highly heterogeneous areas). The idea was to develop a simple classification procedure using the Random Forest classifier in eCognition, usable in various contexts and requiring little training to be used by non-experts. We also rationalized errors of omission by applying simple “buffer” boundaries around knotweed predictions to know if heterogeneity across multi-date images could lead to unfairly harsh accuracy assessment and, therefore, ill-advised decisions. Although our “crisp” satellite results were rather average, our UAV classifications achieved high detection accuracies. Multi-date spectral indices and CHMs consistently improved classification results of both datasets. To the best of our knowledge, it was the first time that UAV-generated CHMs were used to map invasive plants and their use substantially facilitated knotweed detection in heterogeneous vegetation contexts. Additionally, the “buffer” boundary results showed detection rates often exceeding 90–95% for both satellite and UAV images, suggesting that classical accuracy assessments were overly conservative. Considering these results, it seems that knotweed can be satisfactorily mapped and monitored via remote sensing with moderate time and money investment but that the choice of the most appropriate method will depend on the landscape context and the spatial scale of the invaded area.
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Praticò, Salvatore, Francesco Solano, Salvatore Di Fazio, and Giuseppe Modica. "Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation." Remote Sensing 13, no. 4 (2021): 586. http://dx.doi.org/10.3390/rs13040586.
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The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.
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Qin, Ruiru, Chuanzhi Wang, Yongmei Wu, Huafei Du, and Mingyun Lv. "A U-Shaped Convolution-Aided Transformer with Double Attention for Hyperspectral Image Classification." Remote Sensing 16, no. 2 (2024): 288. http://dx.doi.org/10.3390/rs16020288.
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Convolutional neural networks (CNNs) and transformers have achieved great success in hyperspectral image (HSI) classification. However, CNNs are inefficient in establishing long-range dependencies, and transformers may overlook some local information. To overcome these limitations, we propose a U-shaped convolution-aided transformer (UCaT) that incorporates convolutions into a novel transformer architecture to aid classification. The group convolution is employed as parallel local descriptors to extract detailed features, and then the multi-head self-attention recalibrates these features in consistent groups, emphasizing informative features while maintaining the inherent spectral–spatial data structure. Specifically, three components are constructed using particular strategies. First, the spectral groupwise self-attention (spectral-GSA) component is developed for spectral attention, which selectively emphasizes diagnostic spectral features among neighboring bands and reduces the spectral dimension. Then, the spatial dual-scale convolution-aided self-attention (spatial-DCSA) encoder and spatial convolution-aided cross-attention (spatial-CCA) decoder form a U-shaped architecture for per-pixel classifications over HSI patches, where the encoder utilizes a dual-scale strategy to explore information in different scales and the decoder adopts the cross-attention for information fusion. Experimental results on three datasets demonstrate that the proposed UCaT outperforms the competitors. Additionally, a visual explanation of the UCaT is given, showing its ability to build global interactions and capture pixel-level dependencies.
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Zhang, Huiying, Xia Li, Fabiola Ramelli, Robert O. David, Julie Pasquier, and Jan Henneberger. "IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme." Atmospheric Measurement Techniques 17, no. 24 (2024): 7109–28. https://doi.org/10.5194/amt-17-7109-2024.
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Abstract. The shape of ice crystals affects their radiative properties, growth rate, fall speed, and collision efficiency; thus, it plays a significant role in cloud optical properties and precipitation formation. Ambient conditions, like temperature and humidity, determine the basic habit of ice crystals, while microphysical processes, such as riming and aggregation, further shape them, resulting in a diverse set of ice crystal shapes and effective densities. Current classification algorithms face two major challenges: (1) ice crystals are often classified as a whole (at the image scale), necessitating identification of the dominant component of aggregated ice crystals, and (2) single-label classifications lead to information loss because of the compromise between basic habit and microphysical process information. To address these limitations, we present a two-pronged solution here: (1) a rotated object detection algorithm (IceDetectNet) that classifies each component of an aggregated ice crystal individually and (2) a multi-label classification scheme that considers both basic habits and physical processes simultaneously. IceDetectNet was trained and tested on two independent datasets obtained by a holographic imager during the NASCENT campaign in Ny-Ålesund, Svalbard, in November 2019 and April 2020. The algorithm correctly classified 92 % of the ice crystals as either aggregate or non-aggregate and achieved an overall accuracy of 86 % for basic habits and 82 % for microphysical process classification. At the component scale, IceDetectNet demonstrated high detection and classification accuracy across all sizes, indicating its ability to effectively classify individual components of aggregated ice crystals. Furthermore, the algorithm demonstrated a good generalization ability by classifying ice crystals from an independent generalization dataset with overall accuracies above 70 %. IceDetectNet can provide a deeper understanding of ice crystal shapes, leading to better estimates of ice crystal mass, fall velocity, and radiative properties; therefore, it has the potential to improve precipitation forecasts and climate projections.
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Khatami, Reza, Jane Southworth, Carly Muir, et al. "Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study." Remote Sensing 12, no. 6 (2020): 954. http://dx.doi.org/10.3390/rs12060954.
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Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.
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Suhad, Al-Shoukry, Jaber M. Jawad Bushra, Musa Zalili, and H. Sabry Ahmad. "Development of predictive modeling and deep learning classification of taxi trip tolls." Eastern-European Journal of Enterprise Technologies 3, no. 3 (117) (2022): 6–12. https://doi.org/10.15587/1729-4061.2022.259242.
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Several studies discussed the predictive modeling of deep learning in different applications such as classifying tissue features from microstructural data, Crude Oil Prices, mechanical constitutive behavior of materials, microbiome data, and mineral prospectively. Commercial navigation includes a wealth of trip-related data, including distance, expected journey time, and tolls that may be encountered along the way. Using a classification algorithm, it is possible to extract drop-off and pickup locations from taxi trip data and estimate if the tour would incur tolls. In this work, let’s use the classification learner to create classification models, compare their performance, and export the findings for additional study. The workflow for the classification learner is the same as for the regression learner. The purpose is to make predictions based on fresh data in order to see how well the model performs with new data. To train the model, it’s critical to separate the data set. The combined training and validation data is next pre-processed, which involves tasks such as cleaning and developing new features skills. Once the data has been prepared, it’s time to begin the supervised machine learning process and test a number of ways to identify the best model, such as the type of model that should be used, the important features, and the best parameters of the model to find the best fit for the considered data. The results of analyzing different predictive multiclass classification models with taxi trip tolls show that it is possible to use a machine learning-based model when we like to avoid road tolls depending on historical data on taxi trip tolls. The outcome of this study can help to expect road tolls from the drop-off and pickup locations of a taxi data
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Jin, Suming, Collin Homer, Limin Yang, et al. "Overall Methodology Design for the United States National Land Cover Database 2016 Products." Remote Sensing 11, no. 24 (2019): 2971. http://dx.doi.org/10.3390/rs11242971.
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The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and consistent land cover and change information. To accomplish this, we designed a new land cover strategy and developed comprehensive methods, models, and procedures for NLCD 2016 implementation. Major steps in the new procedures consist of data preparation, land cover change detection and classification, theme-based postprocessing, and final integration. Data preparation includes Landsat imagery selection, cloud detection, and cloud filling, as well as compilation and creation of more than 30 national-scale ancillary datasets. Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. The land cover classification includes seven-date training data creation and 14-run classifications. Pools of training data for change and no-change areas were created before classification based on integrated information from ancillary data, change-detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis. In postprocessing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure the spatial and temporal coherence of land cover and land cover changes over 15 years. An initial accuracy assessment on four selected Landsat path/rows classified with this method indicates an overall accuracy of 82.0% at an Anderson Level II classification and 86.6% at the Anderson Level I classification after combining the primary and alternate reference labels. This methodology was used for the operational production of NLCD 2016 for the Conterminous United States, with final produced products available for free download.
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Hussain, Syed Ibrar, and Elena Toscano. "Optimized Deep Learning for Mammography: Augmentation and Tailored Architectures." Information 16, no. 5 (2025): 359. https://doi.org/10.3390/info16050359.
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This paper investigates the categorization of mammogram images into benign, malignant and normal categories, providing novel approaches based on Deep Convolutional Neural Networks to the early identification and classification of breast lesions. Multiple DCNN models were tested to see how well deep learning worked for difficult, multi-class categorization problems. These models were trained on pre-processed datasets with optimized hyperparameters (e.g., the batch size, learning rate, and dropout) which increased the precision of classification. Evaluation measures like confusion matrices, accuracy, and loss demonstrated their great classification efficiency with low overfitting and the validation results well aligned with the training. DenseNet-201 and MobileNet-V3 Large displayed significant generalization skills, whilst EfficientNetV2-B3 and NASNet Mobile struck the optimum mix of accuracy and efficiency, making them suitable for practical applications. The use of data augmentation also improved the management of data imbalances, resulting in more accurate large-scale detection. Unlike prior approaches, the combination of the architectures, pre-processing approaches, and data augmentation improved the system’s accuracy, indicating that these models are suitable for medical imaging tasks that require transfer learning. The results have shown precise and accurate classifications in terms of dealing with class imbalances and dataset poor quality. In particular, we have not defined a new framework for computer-aided diagnosis here, but we have reviewed a variety of promising solutions for future developments in this field.
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Bousbih, Safa, Mehrez Zribi, Mohammad El Hajj, et al. "Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data." Remote Sensing 10, no. 12 (2018): 1953. http://dx.doi.org/10.3390/rs10121953.
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This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.
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Abdulhay, Enas, Maha Alafeef, Hikmat Hadoush, V. Venkataraman, and N. Arunkumar. "EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach." Mathematical Biosciences and Engineering 19, no. 5 (2022): 5031–54. http://dx.doi.org/10.3934/mbe.2022235.
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<abstract> <p><italic>Objective</italic>: Autism spectrum disorder (ASD) is usually characterised by altered social skills, repetitive behaviours, and difficulties in verbal/nonverbal communication. It has been reported that electroencephalograms (EEGs) in ASD are characterised by atypical complexity. The most commonly applied method in studies of ASD EEG complexity is multiscale entropy (MSE), where the sample entropy is evaluated across several scales. However, the accuracy of MSE-based classifications between ASD and neurotypical EEG activities is poor owing to several shortcomings in scale extraction and length, the overlap between amplitude and frequency information, and sensitivity to frequency. The present study proposes a novel, nonlinear, non-stationary, adaptive, data-driven, and accurate method for the classification of ASD and neurotypical groups based on EEG complexity and entropy without the shortcomings of MSE. <italic>Approach</italic>: The proposed method is as follows: (a) each ASD and neurotypical EEG (122 subjects × 64 channels) is decomposed using empirical mode decomposition (EMD) to obtain the intrinsic components (intrinsic mode functions). (b) The extracted components are normalised through the direct quadrature procedure. (c) The Hilbert transforms of the components are computed. (d) The analytic counterparts of components (and normalised components) are found. (e) The instantaneous frequency function of each analytic normalised component is calculated. (f) The instantaneous amplitude function of each analytic component is calculated. (g) The Shannon entropy values of the instantaneous frequency and amplitude vectors are computed. (h) The entropy values are classified using a neural network (NN). (i) The achieved accuracy is compared to that obtained with MSE-based classification. (j) The consistency of the results of entropy 3D mapping with clinical data is assessed. <italic>Main results</italic>: The results demonstrate that the proposed method outperforms MSE (accuracy: 66.4%), with an accuracy of 93.5%. Moreover, the entropy 3D mapping results are more consistent with the available clinical data regarding brain topography in ASD. <italic>Significance</italic>: This study presents a more robust alternative to MSE, which can be used for accurate classification of ASD/neurotypical as well as for the examination of EEG entropy across brain zones in ASD.</p> </abstract>
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Prakash, B. V., and A. Rajiv Kannan. "An Efficient Approach to Detect Meningioma Brain Tumor Using Adaptive Neuro Fuzzy Inference System Method." Journal of Medical Imaging and Health Informatics 12, no. 2 (2022): 123–30. http://dx.doi.org/10.1166/jmihi.2022.3931.
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Detection of tumors in brain on time saves the patient life. The brain tumor detection is usually done in Magnetic Resonance Imaging (MRI) of the human brain. An automated model is framed to identify tumor pixels in method for detecting and image. This proposed method contains the following modules as enhancement, transformation, feature extraction, classifications and segmentation. The Oriented Local Histogram Equalization (OLHE) method is applied on the brain MRI images in order to enhance the pixel intensity in boundary regions. This enhanced brain image is transformed to multi orientation image using Gabor transform with respect to various scale and orientation of pixels. Then, set of features (Higher Order Spectra (HOS), Gradient, Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Curvelet) are extracted from this Gabor transformed image and these features are further trained and classified into benign or malignant using Adaptive Neuro Fuzzy Inference (ANFIS) classification approach. Finally, morphological algorithm is used for segmenting the tumor regions in the classified responses. MATLAB R2018 version is used in this paper to simulate the proposed algorithm for brain tumor detection. This proposed system achieves 98.6% of sensitivity, 99.5% of specificity and 99.4% of segmentation accuracy.
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Mukashyaka, Patience, Ali Foroughi Pour, and Jeffrey H. Chuang. "Abstract 1160: Multi-scale deep learning feature distributions distinguish invasive ductal and lobular breast cancer slides." Cancer Research 82, no. 12_Supplement (2022): 1160. http://dx.doi.org/10.1158/1538-7445.am2022-1160.
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Abstract Advances in digital pathology have streamlined use of deep learning methodologies to connect morphology and phenotype in hematoxylin and eosin(H&E) stained images. Whole slide images (WSIs) are gigantic, necessitating a tile-based analysis: a WSI is broken into smaller images called tiles, each tile is analyzed separately, and tile level information are then combined to get a slide level prediction. Context aware models integrating tiles of different sizes have been used to combine morphological features present at different scales, and attention-based pipelines have successfully been used to combine tile level information. Pipelines that combine attention-based and context aware learning are less studied. While both methodologies have shown utility when used individually, models that combine both tend to suffer from a large number of parameters to estimate, which frustrates reliable training. Here we show percentiles of deep learning features extracted at different scales serve as a crude multiscale attention mechanism. The proposed model separates TCGA-BRCA invasive ductal and lobular carcinoma FFPE WSIs (AUC=0.85±0.02), which improves upon the performance of the fully tile-based model of [1] (AUC=0.80 ±0.04). The proposed approach enjoys higher average AUC and lower standard deviation across multiple random train/test splits of data. Our results suggest percentile-based feature construction is an interesting alternative when reliable training of fully deep learning models is challenging. References: [1] Noorbakhsh, J., Farahmand, S., Foroughi pour, A. et al. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat Commun 11, 6367 (2020). https://doi.org/10.1038/s41467-020-20030-5 Citation Format: Patience Mukashyaka, Ali Foroughi Pour, Jeffrey H. Chuang. Multi-scale deep learning feature distributions distinguish invasive ductal and lobular breast cancer slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1160.
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Wijaya, A., R. A. Sugardiman Budiharto, A. Tosiani, D. Murdiyarso, and L. V. Verchot. "Assessment of Large Scale Land Cover Change Classifications and Drivers of Deforestation in Indonesia." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 29, 2015): 557–62. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-557-2015.
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Indonesia possesses the third largest tropical forests coverage following Brazilian Amazon and Congo Basin regions. This country, however, suffered from the highest deforestation rate surpassing deforestation in the Brazilian Amazon in 2012. National capacity for forest change assessment and monitoring has been well-established in Indonesia and the availability of national forest inventory data could largely assist the country to report their forest carbon stocks and change over more than two decades. This work focuses for refining forest cover change mapping and deforestation estimate at national scale applying over 10,000 scenes of Landsat scenes, acquired in 1990, 1996, 2000, 2003, 2006, 2009, 2011 and 2012. Pre-processing of the data includes, geometric corrections and image mosaicking. The classification of mosaic Landsat data used multi-stage visual observation approaches, verified using ground observations and comparison with other published materials. There are 23 land cover classes identified from land cover data, presenting spatial information of forests, agriculture, plantations, non-vegetated lands and other land use categories. We estimated the magnitude of forest cover change and assessed drivers of forest cover change over time. Forest change trajectories analysis was also conducted to observe dynamics of forest cover across time. This study found that careful interpretations of satellite data can provide reliable information on forest cover and change. Deforestation trend in Indonesia was lower in 2000-2012 compared to 1990-2000 periods. We also found that over 50% of forests loss in 1990 remains unproductive in 2012. Major drivers of forest conversion in Indonesia range from shrubs/open land, subsistence agriculture, oil palm expansion, plantation forest and mining. The results were compared with other available datasets and we obtained that the MOF data yields reliable estimate of deforestation.
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Jochem, Warren C., and Andrew J. Tatem. "Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot." PLOS ONE 16, no. 2 (2021): e0247535. http://dx.doi.org/10.1371/journal.pone.0247535.
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Spatial datasets of building footprint polygons are becoming more widely available and accessible for many areas in the world. These datasets are important inputs for a range of different analyses, such as understanding the development of cities, identifying areas at risk of disasters, and mapping the distribution of populations. The growth of high spatial resolution imagery and computing power is enabling automated procedures to extract and map building footprints for whole countries. These advances are enabling coverage of building footprint datasets for low and middle income countries which might lack other data on urban land uses. While spatially detailed, many building footprints lack information on structure type, local zoning, or land use, limiting their application. However, morphology metrics can be used to describe characteristics of size, shape, spacing, orientation and patterns of the structures and extract additional information which can be correlated with different structure and settlement types or neighbourhoods. We introduce the foot package, a new set of open-source tools in a flexible R package for calculating morphology metrics for building footprints and summarising them in different spatial scales and spatial representations. In particular our tools can create gridded (or raster) representations of morphology summary metrics which have not been widely supported previously. We demonstrate the tools by creating gridded morphology metrics from all building footprints in England, Scotland and Wales, and then use those layers in an unsupervised cluster analysis to derive a pattern-based settlement typology. We compare our mapped settlement types with two existing settlement classifications. The results suggest that building patterns can help distinguish different urban and rural types. However, intra-urban differences were not well-predicted by building morphology alone. More broadly, though, this case study demonstrates the potential of mapping settlement patterns in the absence of a housing census or other urban planning data.
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Bagińska, Irena. "Estimating and verifying soil unit weight determined on the basis of SCPTu tests." Annals of Warsaw University of Life Sciences – SGGW. Land Reclamation 48, no. 3 (2016): 233–42. http://dx.doi.org/10.1515/sggw-2016-0018.
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Abstract The unit weight, as a basic physical feature of soil, is an elementary quantity, and knowledge of this parameter is necessary in each geotechnical and geo-engineering task. Estimation of this quantity can be made with both laboratory and field techniques. The paper comprises a multi-scale evaluation of unit weight of cohesive soil, based on several measurements made in nearby locations using the SCPTu static probe. The procedures used were based on the two classifications and two solutions from literature. The results were referenced to the actual values of unit weight determined with a direct procedure from undisturbed samples. The resulting solutions were the basis for proposing a new formula to determine the soil unit weight from SCPTu measurements, as well as comparative analysis using exemplary values taken from the national Polish standard.
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Yang, Xiaoxi, Yuqi Wen, Xinyu Song, Song He, and Xiaochen Bo. "Exploring the classification of cancer cell lines from multiple omic views." PeerJ 8 (August 18, 2020): e9440. http://dx.doi.org/10.7717/peerj.9440.
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Background Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. Methods In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines. Results Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines. Conclusions The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.