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Journal articles on the topic 'RF mapping'

Author: Grafiati

Published: 9 November 2024

Last updated: 31 July 2025

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1

Schmitz, B., J. Köszegi, K. Alomari, O. Kugeler, and J. Knobloch. "Magnetometric mapping of superconducting RF cavities." Review of Scientific Instruments 89, no. 5 (2018): 054706. http://dx.doi.org/10.1063/1.5030509.

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Ouma, Y., B. Nkwae, D. Moalafhi, et al. "COMPARISON OF MACHINE LEARNING CLASSIFIERS FOR MULTITEMPORAL AND MULTISENSOR MAPPING OF URBAN LULC FEATURES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 681–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-681-2022.

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Abstract. This study compares four machine-learning algorithms comprising of Classification And Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB) and Support Vector Machine (SVM) for the classification of urban land-use and land-cover (LULC) features. Using multitemporal and multisensor Landsat data from 1984-2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, the aim of the study is to determine the performance of the classifiers in the extraction of different urban LULC features as built-up, bare-soil, water, grass, shrubs and forest. T

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Bonagura, V. R., S. E. Artandi, A. Davidson, et al. "Mapping studies reveal unique epitopes on IgG recognized by rheumatoid arthritis-derived monoclonal rheumatoid factors." Journal of Immunology 151, no. 7 (1993): 3840–52. http://dx.doi.org/10.4049/jimmunol.151.7.3840.

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Abstract We have used chimeric IgG antibodies and their genetically engineered variants prepared by a combination of site-directed mutagenesis and exon exchange to define the structure(s) on IgG recognized by monoclonal rheumatoid factor (RF) autoantibodies from rheumatoid arthritis (RA) patients. Nineteen RF produced by EBV-transformed cell lines from the synovium or blood of RA patients were analyzed. Their binding patterns differ significantly from those seen with RF obtained from patients with Waldenstrom's macroglobulinemia (WMac). Half of the RA-derived RF bound IgG1, 2, and 4, but not 3

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Abida, Khouloud, Meriem Barbouchi, Khaoula Boudabbous, et al. "Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas." Agriculture 12, no. 9 (2022): 1429. http://dx.doi.org/10.3390/agriculture12091429.

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Mapping and monitoring land use (LU) changes is one of the most effective ways to understand and manage land transformation. The main objectives of this study were to classify LU using supervised classification methods and to assess the effectiveness of various machine learning methods. The current investigation was conducted in the Nord-Est area of Tunisia, and an optical satellite image covering the study area was acquired from Sentinel-2. For LU mapping, we tested three machine learning models algorithms: Random Forest (RF), K-Dimensional Trees K-Nearest Neighbors (KDTree-KNN) and Minimum D

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Merfeldas, Audrius, Pranas Kuzas, Darius Gailius, et al. "An Improved Near-field Magnetic Probe Radiation Profile Boundaries Assessment for Optimal Radiated Susceptibility Pre-Mapping." Symmetry 12, no. 7 (2020): 1063. http://dx.doi.org/10.3390/sym12071063.

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In this paper, the near-field radiated susceptibility pre-mapping method is proposed using the improved near-field probe power radiation profile assessment. The modelling of the electromagnetic field strength in 80–3000 MHz range in the proximity of the near-field probe was performed. The −6 dB aperture boundaries of the near-field probe and their variation, due to the proximity of the radio frequency (RF) printed circuit board (PCB) components, were determined, while the aperture map distortion, arising from the proximity of the passive RF PCB components were evaluated. The scanning path requ

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Akbari, Elahe, Ali Darvishi Boloorani, Najmeh Neysani Samany, Saeid Hamzeh, Saeid Soufizadeh, and Stefano Pignatti. "Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2." Remote Sensing 12, no. 9 (2020): 1449. http://dx.doi.org/10.3390/rs12091449.

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Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and

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Wicaksono, Pramaditya, Prama Ardha Aryaguna, and Wahyu Lazuardi. "Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms." Remote Sensing 11, no. 11 (2019): 1279. http://dx.doi.org/10.3390/rs11111279.

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This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemu

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Kwak, Geun-Ho, Chan-won Park, Kyung-do Lee, Sang-il Na, Ho-yong Ahn, and No-Wook Park. "Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data." Remote Sensing 13, no. 9 (2021): 1629. http://dx.doi.org/10.3390/rs13091629.

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When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combi

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Pasquali, J. L., A. M. Knapp, A. Farradji, and A. Weryha. "Mapping of four light chain-associated idiotopes of a human monoclonal rheumatoid factor." Journal of Immunology 139, no. 3 (1987): 818–23. http://dx.doi.org/10.4049/jimmunol.139.3.818.

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Abstract In an effort to analyze both IgM rheumatoid factor (RF) repertoire and regulation of RF production in humans, we developed a panel of four mouse monoclonal antibodies (mAb) defining distinct K light chain-associated idiotopes (id) of a human monoclonal IgM RF (Alt). These mAb (A75, AM1, AM2, AM3) had equivalent reactivities with the immunizing RF during classic inhibition of antigen-binding assays. These anti-id reagents were reacting to neither other tested monoclonal IgM RF nor normal polyclonal IgM. It was possible to distinguish the id defined by the mAb from the results of four s

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Chen, Wei, Zhao Wang, Guirong Wang, et al. "Optimizing Rotation Forest-Based Decision Tree Algorithms for Groundwater Potential Mapping." Water 15, no. 12 (2023): 2287. http://dx.doi.org/10.3390/w15122287.

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Groundwater potential mapping is an important prerequisite for evaluating the exploitation, utilization, and recharge of groundwater. The study uses BFT (best-first decision tree classifier), CART (classification and regression tree), FT (functional trees), EBF (evidential belief function) benchmark models, and RF-BFTree, RF-CART, and RF-FT ensemble models to map the groundwater potential of Wuqi County, China. Firstly, select sixteen groundwater spring-related variables, such as altitude, plan curvature, profile curvature, curvature, slope angle, slope aspect, stream power index, topographic

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Khalighi, Mohammad Mehdi, Brian K. Rutt, and Adam B. Kerr. "Adiabatic RF pulse design for Bloch-SiegertB+ mapping." Magnetic Resonance in Medicine 70, no. 3 (2012): 829–35. http://dx.doi.org/10.1002/mrm.24507.

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Pipa, Gordon, Zhe Chen, Sergio Neuenschwander, Bruss Lima, and Emery N. Brown. "Mapping of Visual Receptive Fields by Tomographic Reconstruction." Neural Computation 24, no. 10 (2012): 2543–78. http://dx.doi.org/10.1162/neco_a_00334.

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The moving bar experiment is a classic paradigm for characterizing the receptive field (RF) properties of neurons in primary visual cortex (V1). Current approaches for analyzing neural spiking activity recorded from these experiments do not take into account the point-process nature of these data and the circular geometry of the stimulus presentation. We present a novel analysis approach to mapping V1 receptive fields that combines point-process generalized linear models (PPGLM) with tomographic reconstruction computed by filtered-back projection. We use the method to map the RF sizes and orie

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Sriprom, Chatyapa, Supaluck Kanjanauthai та Anon Jantanukul. "การสร้างภาพสามมิติ (3D Mapping) ในกลุ่มผู้ป่วยภาวะหัวใจเต้นผิดจังหวะชนิดเร็วที่ได้รับการรักษาโดยการจี้ด้วยกระแสไฟฟ้า (Ablation)". Siriraj Medical Bulletin 14, № 3 (2021): 53–60. http://dx.doi.org/10.33192/smb.v14i3.249578.

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ในปัจจุบันการสร้างภาพสามมิติ (3D Mapping) ในกลุ่มผู้ป่วยภาวะหัวใจเต้นผิดจังหวะชนิดเร็วที่ได้รับการรักษาโดยการจี้ด้วยกระแสไฟฟ้า (RF Ablation) ได้รับความนิยมอย่างแพร่หลายมากขึ้น เนื่องจากการสร้างภาพสามมิติ มีการนำเทคโนโลยีสมัยใหม่เข้ามาช่วยในการสร้างภาพได้แก่ Magnetic Technology, Current-based technology, Hybrid technology ทำให้สามารถสร้างภาพสามมิติออกมาได้หลายรูปแบบอย่างเช่น Anatomical mapping, Local Activation Time mapping (LAT), Bipolar Voltage mapping, Complex Fractionated Atrial Electrogram (CFAEs) Map, Pace map, Merge หรือ Fusion, Reentrant map เป็นต้นโดยภาพที่ได้นอกจากจะแสดงเป็นภาพนิ่งแล้

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Zhao, Zhi-Dong, Ming-Song Zhao, Hong-Liang Lu, Shi-Hang Wang, and Yuan-Yuan Lu. "Digital Mapping of Soil pH Based on Machine Learning Combined with Feature Selection Methods in East China." Sustainability 15, no. 17 (2023): 12874. http://dx.doi.org/10.3390/su151712874.

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This study aimed to evaluate and compare the performances of the random forest (RF) and support vector regression (SVR) models combined with different feature selection methods, including recursive feature elimination (RFE), simulated annealing feature selection (SAFS), and selection by filtering (SBF) in predicting soil pH in Anhui Province, East China. We also used the ALL original features to build the RF and SVR models as a comparison. A total of 140 samples were selected, following the principles of randomness, uniformity, and representativeness, to consider the combination of landscape e

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Wang, Ming, Dehua Mao, Yeqiao Wang, et al. "Annual Wetland Mapping in Metropolis by Temporal Sample Migration and Random Forest Classification with Time Series Landsat Data and Google Earth Engine." Remote Sensing 14, no. 13 (2022): 3191. http://dx.doi.org/10.3390/rs14133191.

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Wetlands provide various ecosystem services to urban areas, which are crucial for sustainable urban management. With intensified urbanization, there has been marked loss of urban natural wetland, degradation, and related urban disasters in the past several decades. Rapid and accurate mapping of urban wetland extent and change is thus critical for improving urban planning toward sustainability. Here, we have developed a rapid method for continuous mapping of urban wetlands (MUW) by combining automatic sample migration and the random forest algorithm (SM&RF), the so-called MUW_SM&RF. Usi

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Arianpouya, Maryam, Benson Yang, Fred Tam, Clare E. McElcheran, and Simon J. Graham. "Optimized radiofrequency shimming using low-heating B1+-mapping in the presence of deep brain stimulation implants: Proof of concept." PLOS ONE 19, no. 12 (2024): e0316002. https://doi.org/10.1371/journal.pone.0316002.

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MRI of patients with Deep Brain Stimulation (DBS) implants is constrained due to radiofrequency (RF) heating of the implant lead. However, “RF-shimming” parallel transmission (PTX) has the potential to reduce DBS heating during MRI. As part of using PTX in such a “safe mode”, maps of the RF transmission field (B1+) are typically acquired for calibration purposes, with each transmit coil excited individually. These maps often have large zones of low signal intensity distant from the specific coil that is being excited, raising concerns that low signal-to-noise ratio (SNR) in these zones might n

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Eghrari, Z., M. R. Delavar, M. Zare, A. Beitollahi, and B. Nazari. "LAND SUBSIDENCE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING ALGORITHMS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 129–36. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-129-2023.

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Abstract. Land subsidence (LS) is one of the most challenging natural disasters that has potential consequences such as damage to infrastructures and buildings, creating sinkholes, and leading to soil destruction. To mitigate the damages caused by LS, it is necessary to determine the LS-prone areas. In this paper, LS susceptibility was assessed for Kashan Plain in Iran using Random Forest (RF) and XGBoost machine learning algorithms. For the susceptibility analysis, twelve influential factors including elevation, slope, aspect, curvature, topographic wetness index (TWI), groundwater drawdown (

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18

Jiang, Fugen, Andrew R. Smith, Mykola Kutia, Guangxing Wang, Hua Liu, and Hua Sun. "A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China." Remote Sensing 12, no. 11 (2020): 1884. http://dx.doi.org/10.3390/rs12111884.

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As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of

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Huang, He, Yaolin Liu, Yanfang Liu, Zhaomin Tong, Zhouqiao Ren, and Yifan Xie. "Digital Mapping of Soil pH and Driving Factor Analysis Based on Environmental Variable Screening." Sustainability 17, no. 7 (2025): 3173. https://doi.org/10.3390/su17073173.

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This study comprehensively considers soil formation factors such as land use types, soil types, depths, and geographical conditions in Lanxi City, China. Using multi-source public data, three environmental variable screening methods, the Boruta algorithm, Recursive Feature Elimination (RFE), and Particle Swarm Optimization (PSO), were used to optimize and combine 47 environmental variables for the modeling of soil pH based on the data collected from farmland in the study area in 2022, and their effects were evaluated. A Random Forest (RF) model was used to predict soil pH in the study area. At

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Tsogtbaatar, Undrakhtsetseg, Sainbayar Dalantai, and Bayartungalag Batsaikhan. "Soil moisture mapping using machine learning technique." Mongolian Journal of Geography and Geoecology 60, no. 44 (2023): 222–30. http://dx.doi.org/10.5564/mjgg.v60i44.3062.

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Soil moisture is an essential component in the energy cycle, water resource, hydrological regime, and processes of the land surface. Mapping and monitoring of soil moisture are crucial for the prevention of flood and drought, estimation of evapotranspiration, and water resource management. Using remote sensing to create soil moisture mapping at large scale has become one of the most energy and time-efficient methods in soil study. Thus, we aimed to map the soil moisture for Mongolia based on downscaled Soil Moisture Active Passive (SMAP) data by combining it with the Normalized Difference Vege

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Yang, Zhicheng, Andrea D’Alpaos, Marco Marani, and Sonia Silvestri. "Assessing the Fractional Abundance of Highly Mixed Salt-Marsh Vegetation Using Random Forest Soft Classification." Remote Sensing 12, no. 19 (2020): 3224. http://dx.doi.org/10.3390/rs12193224.

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Coastal salt marshes are valuable and critical components of tidal landscapes, currently threatened by increasing rates of sea level rise, wave-induced lateral erosion, decreasing sediment supply, and human pressure. Halophytic vegetation plays an important role in salt-marsh erosional and depositional patterns and marsh survival. Mapping salt-marsh halophytic vegetation species and their fractional abundance within plant associations can provide important information on marsh vulnerability and coastal management. Remote sensing has often provided valuable methods for salt-marsh vegetation map

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Wang, Tiebin, Kaiyuan Huang, Min Liu, and Ranran He. "Sparse Space Shift Keying Modulation with Enhanced Constellation Mapping." Sensors 22, no. 15 (2022): 5895. http://dx.doi.org/10.3390/s22155895.

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For reducing the switching frequency between the radio frequency (RF) chain and transmit antennas, a class of new sparse space shift keying modulation (SSSK) schemes are presented. This new class is proposed to simplify hardware implementation, through carefully designing the spatial constellation mapping pattern. Specifically, different from traditional space shift keying modulation (SSK), the proposed SSSK scheme utilizes more time slots to construct a joint design of time and spatial domain SSK modulation, while maintaining the special structure of single RF chain. Since part of the multi-d

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Ganjirad, M., and M. R. Delavar. "FLOOD RISK MAPPING USING RANDOM FOREST AND SUPPORT VECTOR MACHINE." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 201–8. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-201-2023.

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Abstract. Floods are among the natural disasters that cause financial and human losses all over the world every year. By production of a flood risk map and determination of potential flood risk areas, the possible damages of this phenomenon can be reduced. To map the flood extend in Calcasieu Parish, Louisiana, US, conditioning factors affecting the flood occurrence including elevation, slope, plan curvature, land use, distance from rivers, density of rivers, rainfall, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and normalized difference b

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Han, Hyangsun, Taewook Kim, and Seohyeon Kim. "River Ice Mapping from Landsat-8 OLI Top of Atmosphere Reflectance Data by Addressing Atmospheric Influences with Random Forest: A Case Study on the Han River in South Korea." Remote Sensing 16, no. 17 (2024): 3187. http://dx.doi.org/10.3390/rs16173187.

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Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping mo

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Xi, Jing, Qigang Jiang, Huaxin Liu, and Xin Gao. "Lithological Mapping Research Based on Feature Selection Model of ReliefF-RF." Applied Sciences 13, no. 20 (2023): 11225. http://dx.doi.org/10.3390/app132011225.

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Multispectral satellite data allow experts to discriminate rock units based on their spectral signature characteristics. Here, Sentinel-2, ASTER and the Landsat-8 Operational Land Imager (OLI) were assessed for lithological mapping by using a random forest (RF) classifier for a study area located in Xitieshan, Northwest China. The classification accuracy of Sentinel-2 was 60.71%, which was 5.24% and 4.77% higher than the accuracies for ASTER and the Landsat-8 OLI, respectively. Three image enhancement techniques, namely, principal component analysis (PCA), independent component analysis (ICA)

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Tan, X. L. "Mapping of Rice Rf Gene by Bulked Line Analysis." DNA Research 5, no. 1 (1998): 15–18. http://dx.doi.org/10.1093/dnares/5.1.15.

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Shapiro, Erik M., Arijitt Borthakur, Michael J. Shapiro, Ravinder Reddy, and John S. Leigh. "Fast MRI of RF heating via phase difference mapping." Magnetic Resonance in Medicine 47, no. 3 (2002): 492–98. http://dx.doi.org/10.1002/mrm.10067.

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Khalighi, Mohammad Mehdi, Brian K. Rutt, and Adam B. Kerr. "RF pulse optimization for Bloch-Siegert B +1 mapping." Magnetic Resonance in Medicine 68, no. 3 (2011): 857–62. http://dx.doi.org/10.1002/mrm.23271.

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Okoh, Supreme Ayewoh, Stanley Tio Andeobu, John-Davis Chukwuemeka Oyedum, Nuhu Sadiq, and Elizabeth Nonye Onwuka. "RF Coverage Mapping of Mobile Phone Networks in Nigeria." International Journal of Computing and Digital Systems 12, no. 1 (2022): 417–25. http://dx.doi.org/10.12785/ijcds/120133.

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Pavanelli, João Arthur Pompeu, João Roberto dos Santos, Lênio Soares Galvão, Maristela Xaud, and Haron Abrahim Magalhães Xaud. "PALSAR-2/ALOS-2 AND OLI/LANDSAT-8 DATA INTEGRATION FOR LAND USE AND LAND COVER MAPPING IN NORTHERN BRAZILIAN AMAZON." Boletim de Ciências Geodésicas 24, no. 2 (2018): 250–69. http://dx.doi.org/10.1590/s1982-21702018000200017.

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Abstract: In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS-2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the ref

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Lin, Chinsu, and Nova D. Doyog. "Challenges of Retrieving LULC Information in Rural-Forest Mosaic Landscapes Using Random Forest Technique." Forests 14, no. 4 (2023): 816. http://dx.doi.org/10.3390/f14040816.

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Land use and land cover (LULC) information plays a crucial role in determining the trend of the global carbon cycle in various fields, such as urban land planning, agriculture, rural management, and sustainable development, and serves as an up-to-date indicator of forest changes. Accurate and reliable LULC information is needed to address the detailed changes in conservation-based and development-based classes. This study integrates Sentinel-2 multispectral surface reflectance and vegetation indices, and lidar-based canopy height and slope to generate a random forest model for 3-level LULC cla

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Cetinkaya, S., and S. Kocaman. "SNOW AVALANCHE SUSCEPTIBILITY MAPPING FOR DAVOS, SWITZERLAND." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 1083–90. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1083-2022.

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Abstract. Snow avalanches are among destructive hazards occurring in mountainous regions and spatial distribution (susceptibility) of their occurrences needs to be considered for spatial planning and disaster risk mitigation efforts. The susceptibility assessment is the first step in avalanche disaster management and can be carried out using high resolution geospatial data and machine learning (ML) algorithms. In this study, we have assessed the snow avalanche susceptibility in Davos, Switzerland using an inventory delineated on satellite imagery in a previous study. The conditioning factors u

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Sarkar, Showmitra Kumar, Swapan Talukdar, Atiqur Rahman, Shahfahad, and Sujit Kumar Roy. "Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management." Frontiers in Engineering and Built Environment 2, no. 1 (2021): 43–54. http://dx.doi.org/10.1108/febe-09-2021-0044.

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PurposeThe present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and random subspace (RSS).Design/methodology/approachThe RF and RSS models have been implemented for integrating 14 selected groundwater condition parametres with groundwater inventories for generating GPMs. The GPM were then validated using the empirical and bionormal receiver operating characteristics (ROC) curve.FindingsThe very high (831–1200 km2) and high groundwater potential areas (521–680 k

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Alzahrani, Ali, and Awos Kanan. "Machine Learning Approaches for Developing Land Cover Mapping." Applied Bionics and Biomechanics 2022 (June 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/5190193.

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In remote sensing data processing, cover classification on decimeter-level data is a well-studied but tough subject that has been well-documented. The majority of currently existent works make use of orthographic photographs or orthophotos and digital surface models that go with them (DSMs). Urban land cover classification plays a significant role in the field of remote sensing to enhance the quality of different applications including environment protection, sustainable development, and resource management and planning. Novelty of the research done in this area is focused on extracting featur

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Wu, Fan, Yufen Ren, and Xiaoke Wang. "Application of Multi-Source Data for Mapping Plantation Based on Random Forest Algorithm in North China." Remote Sensing 14, no. 19 (2022): 4946. http://dx.doi.org/10.3390/rs14194946.

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The expansion of plantation poses new challenges for mapping forest, especially in mountainous regions. Using multi-source data, this study explored the capability of the random forest (RF) algorithm for the extraction and mapping of five forest types located in Yanqing, north China. The Google Earth imagery, forest inventory data, GaoFen-1 wide-field-of-view (GF-1 WFV) images and DEM were applied for obtaining 125 features in total. The recursive feature elimination (RFE) method selected 32 features for mapping five forest types. The results attained overall accuracy of 87.06%, with a Kappa c

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Van Huynh, Chuong, Tung Gia Pham, Linh Hoang Khanh Nguyen, et al. "Application GIS and remote sensing for soil organic carbon mapping in a farm-scale in the hilly area of central Vietnam." Air, Soil and Water Research 15 (January 2022): 117862212211147. http://dx.doi.org/10.1177/11786221221114777.

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Soil Organic Carbon (SOC) influences many soil properties including nutrient and water holding capacity, nutrient cycling and stability, improved water infiltration and aeration. It also is an essential parameter in the assessment of soil quality, especially for agricultural production. However, SOC mapping is a complicated process that is costly and time-consuming due to the physical challenges of the natural conditions that is being surveyed. The best model for SOC mapping is still in debate among many researchers. Recently, the development of machine learning and Geographical Information Sy

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Shao, Yakui, Zhongke Feng, Meng Cao, et al. "An Ensemble Model for Forest Fire Occurrence Mapping in China." Forests 14, no. 4 (2023): 704. http://dx.doi.org/10.3390/f14040704.

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Assessing and predicting forest fires has long been an arduous task. Nowadays, the rapid advancement of artificial intelligence and machine learning technologies have provided a novel solution to forest fire occurrence assessment and prediction. In this research, we developed a novel hybrid machine-learning-technique algorithm to improve forest fire prediction based on random forest (RF), gradient-boosting decision tree (GBDT), support vector machine (SVM), and other machine learning models. The dataset we employed was satellite fire point data from 2010 to 2018 from the Chinese Department of

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Ouma, Yashon O., Thabiso G. Gabasiane, and Nyaladzani Nkhwanana. "Mapping Prosopis L. (Mesquites) Using Sentinel-2 MSI Satellite Data, NDVI and SVI Spectral Indices with Maximum-Likelihood and Random Forest Classifiers." Journal of Sensors 2023 (July 4, 2023): 1–18. http://dx.doi.org/10.1155/2023/8882730.

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Mapping of invasive alien plants (IAPs) is important for developing informed initiatives to assist environmentalists in managing the spread and impacts of IAPs. The Prosopis plant species is an aggressive IAP that has been considered a scourge in different regions of the globe. The aim of this study is to map the spatial distribution of the invasive alien Prosopis plant in southwestern Botswana using the higher spatial and spectral resolution Sentinel-2A (S2A) MultiSpectral Instrument (MSI) satellite sensor data. Supervised parametric maximum likelihood classification (MLC) was compared with t

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Simarmata, Nirmawana, Ketut Wikantika, Soni Darmawan, and Agung Budi Harto. "Advanced Machine Learning Techniques for Tidal Marsh Classification: A Random Forest Approach using Sentinel-2A." Geosfera Indonesia 9, no. 3 (2024): 276–94. https://doi.org/10.19184/geosi.v9i3.4263.

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Tidal marshes play a vital role in coastal ecosystems, functioning in climate change mitigation, water filtration, and protection from coastal erosion. However, mapping and monitoring of these ecosystems is often hampered by difficult accessibility and dynamic environmental conditions. This study aims to improve tidal marsh classification accuracy by applying a Random Forest (RF) algorithm supported by Sentinel-2A satellite imagery. This image provides various spectral parameters and vegetation indices, including B1, GNDVI, BSI, and NDWI. Three RF models with varying parameters were tested to

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Simarmata, Nirmawana, Ketut Wikantika, Soni Darmawan, and Agung Budi Harto. "Advanced Machine Learning Techniques for Tidal Marsh Classification: A Random Forest Approach using Sentinel-2A." Geosfera Indonesia 9, no. 3 (2024): 276–94. https://doi.org/10.19184/geosi.v9i3.52186.

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Tidal marshes play a vital role in coastal ecosystems, functioning in climate change mitigation, water filtration, and protection from coastal erosion. However, mapping and monitoring of these ecosystems is often hampered by difficult accessibility and dynamic environmental conditions. This research aims to improve tidal marsh classification accuracy by applying a Random Forest (RF) algorithm supported by Sentinel-2A satellite imagery. This image provides various spectral parameters and vegetation indices, including B1, GNDVI, BSI, and NDWI. Three RF models with varying parameters were tested

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Berangi, Mostafa, Andre Kuehne, Helmar Waiczies, and Thoralf Niendorf. "MRI of Implantation Sites Using Parallel Transmission of an Optimized Radiofrequency Excitation Vector." Tomography 9, no. 2 (2023): 603–20. http://dx.doi.org/10.3390/tomography9020049.

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Postoperative care of orthopedic implants is aided by imaging to assess the healing process and the implant status. MRI of implantation sites might be compromised by radiofrequency (RF) heating and RF transmission field (B1+) inhomogeneities induced by electrically conducting implants. This study examines the applicability of safe and B1+-distortion-free MRI of implantation sites using optimized parallel RF field transmission (pTx) based on a multi-objective genetic algorithm (GA). Electromagnetic field simulations were performed for eight eight-channel RF array configurations (f = 297.2 MHz),

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Lachaud, Alix, Marcus Adam, and Ilija Mišković. "Comparative Study of Random Forest and Support Vector Machine Algorithms in Mineral Prospectivity Mapping with Limited Training Data." Minerals 13, no. 8 (2023): 1073. http://dx.doi.org/10.3390/min13081073.

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This paper employs two data-driven methods, Random Forest (RF) and Support Vector Machines (SVM), to develop mineral prospectivity models for an epithermal Au deposit. Four distinct models are presented for comparison: one employing RF and three using SVM with different kernel functions—namely linear, Radial Basis Function (RBF), and polynomial. The analysis leverages a compact training dataset, encompassing just 20 deposits, with deposit and non-deposit locations chosen from known mineral occurrences. Fourteen predictor maps are constructed based on the available data and the exploration mode

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Wang, Changpeng, Yangchun Lin, Zhiwen Tao, Jiayin Zhan, Wenkai Li, and Huabing Huang. "An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping." Remote Sensing 15, no. 22 (2023): 5384. http://dx.doi.org/10.3390/rs15225384.

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Data-driven flood susceptibility modeling is an efficient way to map the spatial distribution of flood likelihood. The quality of the flood susceptibility model relies on the learning technique and the data used for learning. The performance of learning techniques has been extensively examined. However, to date, the impact of data sampling strategies has received limited attention. Random sampling is widely favored because of its ease of use. It treats flood-related data as tabular and excludes their spatial dimensions. Flood occurrence is typically uneven over space. Therefore, non-flood samp

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Silva, Sérgio Henrique Godinho, Anita Fernanda dos Santos Teixeira, Michele Duarte de Menezes, Luiz Roberto Guimarães Guilherme, Fatima Maria de Souza Moreira, and Nilton Curi. "Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)." Ciência e Agrotecnologia 41, no. 6 (2017): 648–64. http://dx.doi.org/10.1590/1413-70542017416010317.

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ABSTRACT Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and sub

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Song, Weicheng, Aiqing Feng, Guojie Wang, et al. "Bi-Objective Crop Mapping from Sentinel-2 Images Based on Multiple Deep Learning Networks." Remote Sensing 15, no. 13 (2023): 3417. http://dx.doi.org/10.3390/rs15133417.

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Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (M

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Kumar, Suchit, Kyu Chan Lee, Jong-Min Kim, Jeung-Hoon Seo, Chulhyun Lee, and Chang-Hyun Oh. "Multislice B1 Mapping Method Using Magnetic Resonance Composite Spin Echo Sequences and Simultaneous Echo Refocusing." Concepts in Magnetic Resonance Part A 2023 (April 13, 2023): 1–9. http://dx.doi.org/10.1155/2023/7642095.

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Radiofrequency (RF) transmit field (B1) mapping is a promising method in mitigating the B1 inhomogeneity in various magnetic resonance imaging (MRI) applications. Although several phase- or magnitude-based B1 mapping methods have been proposed, these methods often require complex modeling, long acquisition time, or specialized MRI sequences. A recently introduced simultaneous echo refocusing (SER) technique can be applied in the B1 mapping method to extend the three-dimensional (3D) spatial coverage only without long data acquisition. Therefore, in this study, a multislice B1 mapping method us

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Forsey, D., B. Leblon, A. LaRocque, M. Skinner, and A. Douglas. "EELGRASS MAPPING IN ATLANTIC CANADA USING WORLDVIEW-2 IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 685–92. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-685-2020.

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Abstract. Eelgrass (Zostera marina L.) is a marine angiosperm plant that grows throughout coastal areas in Atlantic Canada. Eelgrass meadows provide numerous ecosystem services, and while they have been acknowledged as important habitats, their location, extent, and health in Atlantic Canada are poorly understood. This study examined the effectiveness of WorldView-2 optical satellite imagery to map eelgrass presence in Tabusintac Bay, New Brunswick (Canada), an estuarine lagoon with extensive eelgrass coverage. The imagery was classified using two supervised classifiers: the parametric Maximum

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Zhou, Tao, Yajun Geng, Jie Chen, Chuanliang Sun, Dagmar Haase, and Angela Lausch. "Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables." Remote Sensing 11, no. 24 (2019): 2934. http://dx.doi.org/10.3390/rs11242934.

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Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector mac

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Bazzi, Hassan, Nicolas Baghdadi, Dino Ienco, et al. "Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain." Remote Sensing 11, no. 15 (2019): 1836. http://dx.doi.org/10.3390/rs11151836.

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Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations o

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Kuhn, Stephen, Matthew J. Cracknell, Anya M. Reading, and Stephanie Sykora. "Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifier." GEOPHYSICS 85, no. 6 (2020): B249—B258. http://dx.doi.org/10.1190/geo2019-0461.1.

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Identifying the location of intrusions is a key component in exploration for porphyry Cu ± Mo ± Au deposits. In typical porphyry terrains, in the absence of outcrop, intrusions can be difficult to discriminate from the compositionally similar volcanic and volcanoclastic sedimentary rocks in which they are emplaced. The ability to produce lithological maps at an early exploration stage can significantly reduce costs by assisting in planning and prioritization of detailed mapping and sampling. Additionally, a data-driven strategy provides opportunity for the discovery of intrusions not identifie

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