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Articles de revues sur le sujet « DL. Archives »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 31 juillet 2025

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Buyukdemircioglu, M., S. Kocaman, and M. Kada. "DEEP LEARNING FOR 3D BUILDING RECONSTRUCTION: A REVIEW." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 359–66. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-359-2022.

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Abstract. 3D building reconstruction using Earth Observation (EO) data (aerial and satellite imagery, point clouds, etc.) is an important and active research topic in different fields, such as photogrammetry, remote sensing, computer vision and Geographic Information Systems (GIS). Nowadays 3D city models have become an essential part of 3D GIS environments and they can be used in many applications and analyses in urban areas. The conventional 3D building reconstruction methods depend heavily on the data quality and source; and manual efforts are still needed for generating the object models.

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Palma, V. "TOWARDS DEEP LEARNING FOR ARCHITECTURE: A MONUMENT RECOGNITION MOBILE APP." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9 (January 31, 2019): 551–56. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w9-551-2019.

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<p><strong>Abstract.</strong> In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence (AI) algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines. This paper introduces a project aimed at studying CNN techniques in the field of architectural heritage, a still to be developed research stream. The first steps and results in the devel

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Setianingsih, Eva, and Umi Hartati. "MODEL PEMBELAJARAN SEJARAH TIPE JIGSAW BERBASIS ARSIP SEJARAH LOKAL UNTUK MENGUATKAN BERPIKIR HISTORIS PESERTA DIDIK." SWARNADWIPA 4, no. 2 (2022): 104. http://dx.doi.org/10.24127/sd.v4i2.1980.

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The research objectives are (1) to describe the history learning model that has been used in class XI of Muhammadiyah 2 High School in SMA (2) to describe the level of students' historical thinking ability on the local history of the material impact of colonialism and imperialism policies on the development of Metro City class XI of Muhammadiyah 2 Senior High School Metro (3) To obtain a Jigsaw type historical learning model design based on local history archives in the history subjects of class XI Muhammadiyah 2 Metro High School (4) To describe the opinions of experts about the feasibility o

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Comesaña Cebral, L. J., J. Martínez Sánchez, E. Rúa Fernández, and P. Arias Sánchez. "HEURISTIC GENERATION OF MULTISPECTRAL LABELED POINT CLOUD DATASETS FOR DEEP LEARNING MODELS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 571–76. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-571-2022.

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Abstract. Deep Learning (DL) models need big enough datasets for training, especially those that deal with point clouds. Artificial generation of these datasets can complement the real ones by improving the learning rate of DL architectures. Also, Light Detection and Ranging (LiDAR) scanners can be studied by comparing its performing with artificial point clouds. A methodology for simulate LiDAR-based artificial point clouds is presented in this work in order to get train datasets already labelled for DL models. In addition to the geometry design, a spectral simulation will be also performed s

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Cao, Y., and M. Scaioni. "LABEL-EFFICIENT DEEP LEARNING-BASED SEMANTIC SEGMENTATION OF BUILDING POINT CLOUDS AT LOD3 LEVEL." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 449–56. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-449-2021.

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Abstract. In recent research, fully supervised Deep Learning (DL) techniques and large amounts of pointwise labels are employed to train a segmentation network to be applied to buildings’ point clouds. However, fine-labelled buildings’ point clouds are hard to find and manually annotating pointwise labels is time-consuming and expensive. Consequently, the application of fully supervised DL for semantic segmentation of buildings’ point clouds at LoD3 level is severely limited. To address this issue, we propose a novel label-efficient DL network that obtains per-point semantic labels of LoD3 bui

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He, H., K. Gao, W. Tan, et al. "IMPACT OF DEEP LEARNING-BASED SUPER-RESOLUTION ON BUILDING FOOTPRINT EXTRACTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (May 30, 2022): 31–37. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-31-2022.

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Abstract. Automated building footprints extraction from High Spatial Resolution (HSR) remote sensing images plays important roles in urban planning and management, and hazard and disease control. However, HSR images are not always available in practice. In these cases, super-resolution, especially deep learning (DL)-based methods, can provide higher spatial resolution images given lower resolution images. In a variety of remote sensing applications, DL based super-resolution methods are widely used. However, there are few studies focusing on the impact of DL-based super-resolution on building

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Polak, Yuri. "ON SERIOUS AND FUNNY IN SCIENCE (BASED ON MATERIALS OF DIGITAL LIBRARIES)." ЭЛЕКТРОННЫЕ БИБЛИОТЕКИ (Digital Libraries) 27, no. 2 (2024): 215–49. https://doi.org/10.5281/zenodo.11282489.

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Digital libraries (DL) and archives accumulate gigantic volumes of various information. The goal of this work is, without trying to cover the immensity, to try, using a relatively small number of striking examples, to trace how issues of scientific creativity are reflected in DL; discuss and dispel stereotypical ideas about scientists as unsociable, pedantic formalists or eccentric, absent-minded persons; show how the peculiarities of their thought processes, combined with high intelligence, can cause misunderstanding in everyday life. At the same time, these qualities, combined with originali

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Polak, Yuri Evgenievich. "On Serious and Funny in Science (Based on Materials of Digital Libraries)." Russian Digital Libraries Journal 27, no. 2 (2024): 215–49. https://doi.org/10.26907/1562-5419-2024-27-2-215-249.

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Digital libraries (DL) and archives accumulate gigantic volumes of various information. The goal of this work is, without trying to cover the immensity, to try, using a relatively small number of striking examples, to trace how issues of scientific creativity are reflected in DL; discuss and dispel stereotypical ideas about scientists as unsociable, pedantic formalists or eccentric, absent-minded persons; show how the peculiarities of their thought processes, combined with high intelligence, can cause misunderstanding in everyday life. At the same time, these qualities, combined with originali

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Gyamfi, Nana Kwame, and Adam Amril Jaharadak. "Ml/Dl Analytical Approaches to Assist Software Project Managers: Dashboard." International Journal of Membrane Science and Technology 10, no. 1 (2023): 1075–84. http://dx.doi.org/10.15379/ijmst.v10i1.2748.

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Companies frequently turn to project management systems for advice with the ongoing data growth caused by stakeholders throughout a product life cycle. The team will be able to communicate more effectively, plan their next moves, have an overview of the current project state, and act before the projections are delivered with project-oriented business intelligence approaches. These technologies are becoming even more beneficial as agile working mindsets proliferate. It establishes a fundamental concept of how the project should function so that the implementation is simple to use and follow. Te

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Nurunnabi, A., F. N. Teferle, D. F. Laefer, F. Remondino, I. R. Karas, and J. Li. "kCV-B: BOOTSTRAP WITH CROSS-VALIDATION FOR DEEP LEARNING MODEL DEVELOPMENT, ASSESSMENT AND SELECTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 111–18. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-111-2022.

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Abstract. This study investigates the inability of two popular data splitting techniques: train/test split and k-fold cross-validation that are to create training and validation data sets, and to achieve sufficient generality for supervised deep learning (DL) methods. This failure is mainly caused by their limited ability of new data creation. In response, the bootstrap is a computer based statistical resampling method that has been used efficiently for estimating the distribution of a sample estimator and to assess a model without having knowledge about the population. This paper couples cros

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Nurunnabi, A., F. N. Teferle, J. Li, R. C. Lindenbergh, and A. Hunegnaw. "AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2021 (June 28, 2021): 31–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2021-31-2021.

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Abstract. Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of trai

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Rehman, Amir, Muhammad Azhar Iqbal, Huanlai Xing, and Irfan Ahmed. "COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review." Applied Sciences 11, no. 8 (2021): 3414. http://dx.doi.org/10.3390/app11083414.

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COVID-19 has infected 223 countries and caused 2.8 million deaths worldwide (at the time of writing this article), and the death rate is increasing continuously. Early diagnosis of COVID patients is a critical challenge for medical practitioners, governments, organizations, and countries to overcome the rapid spread of the deadly virus in any geographical area. In this situation, the previous epidemic evidence on Machine Learning (ML) and Deep Learning (DL) techniques encouraged the researchers to play a significant role in detecting COVID-19. Similarly, the rising scope of ML/DL methodologies

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Caglar, Ozgur, Erdem Karadeniz, Irem Ates, Sevilay Ozmen, and Mehmet Dumlu Aydin. "Vagosympathetic imbalance induced thyroiditis following subarachnoid hemorrhage: a preliminary study." Journal of Research in Clinical Medicine 8, no. 1 (2020): 17. http://dx.doi.org/10.34172/jrcm.2020.017.

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Introduction: This preliminary study evaluates the possible responsibility of ischemia-induced vagosympathetic imbalances following subarachnoid hemorrhage (SAH), for the onset of autoimmune thyroiditis. Methods: Twenty-two rabbits were chosen from our former experimental animals, five of which were picked from healthy rabbits as control (nG-I=5). Sham group (nG-II=5) and animals with thyroid pathologies (nG-III=12) were also included after a one-month-long experimental SAH follow-up. Thyroid hormone levels were measured weekly, and animals were decapitated. Thyroid glands, superior cervical g

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Song, Shuang, Luca Morelli, Xinyi Wu, Rongjun Qin, Hessah Albanwan, and Fabio Remondino. "Evaluating Learning-based Tie Point Matching for Geometric Processing of Off-Track Satellite Stereo." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 393–400. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-393-2024.

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Abstract. Tie-point matching of off-track stereo images is a very challenging task, which can impact bias compensation and digital surface model (DSM) generation. Compared to in-track stereo images, off-track stereo images are more complex primarily due to the radiometric differences caused by sun illumination, sensor responses, atmospheric conditions, and seasonal land cover variations, and secondly due to the longer baseline and larger intersection angle. These challenges significantly limit the use of the vast number of images in satellite archives for automated geometric processing and map

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Andrade, R. B., G. A. O. P. Costa, G. L. A. Mota, et al. "EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1497–505. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1497-2020.

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Abstract. Deforestation is a wide-reaching problem, responsible for serious environmental issues, such as biodiversity loss and global climate change. Containing approximately ten percent of all biomass on the planet and home to one tenth of the known species, the Amazon biome has faced important deforestation pressure in the last decades. Devising efficient deforestation detection methods is, therefore, key to combat illegal deforestation and to aid in the conception of public policies directed to promote sustainable development in the Amazon. In this work, we implement and evaluate a defores

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Panella, F., J. Boehm, Y. Loo, A. Kaushik, and D. Gonzalez. "DEEP LEARNING AND IMAGE PROCESSING FOR AUTOMATED CRACK DETECTION AND DEFECT MEASUREMENT IN UNDERGROUND STRUCTURES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 829–35. http://dx.doi.org/10.5194/isprs-archives-xlii-2-829-2018.

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This work presents the combination of Deep-Learning (DL) and image processing to produce an automated cracks recognition and defect measurement tool for civil structures. The authors focus on tunnel civil structures and survey and have developed an end to end tool for asset management of underground structures. In order to maintain the serviceability of tunnels, regular inspection is needed to assess their structural status. The traditional method of carrying out the survey is the visual inspection: simple, but slow and relatively expensive and the quality of the output depends on the ability

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Cao, Y., and M. Scaioni. "A 3D INDOOR-OUTDOOR BENCHMARK DATASET FOR LoD3 BUILDING POINT CLOUD SEMANTIC SEGMENTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W3-2023 (October 19, 2023): 31–37. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-31-2023.

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Abstract. Deep learning (DL) algorithms require high quality training samples as well as accurate and thorough annotations to work effectively. Up until now a limited number of datasets are available to train DL techniques for semantic segmentation of 3D building point clouds, except a few ones focusing on specific categories of constructions (e.g., cultural heritage buildings). This paper presents a new 3D Indoor/Outdoor building dataset (BIO dataset), which is aimed to provide a highly accurate, detailed, and comprehensive dataset to be used for applications related to sematic classification

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Tejeswari, B., S. K. Sharma, M. Kumar, and K. Gupta. "BUILDING FOOTPRINT EXTRACTION FROM SPACE-BORNE IMAGERY USING DEEP NEURAL NETWORKS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 641–47. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-641-2022.

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Abstract. One of the important and high-level detailing contained within basemaps is the ‘building feature’. Though pre-trained Deep Learning (DL) models are available for Building Feature Extraction (BFE), they are not efficient in predicting the buildings in other locations. This study explores the need and the major issue of implementing DL models for BFE from Very High Resolution Remote Sensing (VHRS) satellite data for any given area. Though advanced DL models are invented, in order to implement them, huge amount of potential training data is demanded for feed in. the building typologies

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Martin, David R., Joshua A. Hanson, Rama R. Gullapalli, Fred A. Schultz, Aisha Sethi, and Douglas P. Clark. "A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology." Archives of Pathology & Laboratory Medicine 144, no. 3 (2019): 370–78. http://dx.doi.org/10.5858/arpa.2019-0004-oa.

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Context.— Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched. Objective.— To investigate the use of DL for nonneoplastic gastric biopsies. Design.— Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were includ

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Roman, Oscar, Maarten Bassier, Sam De Geyter, Heinder De Winter, Elisa Mariarosaria Farella, and Fabio Remondino. "BIM Module for Deep Learning-driven parametric IFC reconstruction." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W8-2024 (December 14, 2024): 403–10. https://doi.org/10.5194/isprs-archives-xlviii-2-w8-2024-403-2024.

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Abstract. The creation of Building Information Models (BIM) is driven by cutting-edge software applications, plug-ins, and APIs that constitute the backbone of BIM authoring tools. While free tools and APIs offer visualization and customization options, geometric modelling remains largely restricted to interactive work and proprietary platforms, which sometimes limits flexibility and efficiency. There are still only a few comprehensive workflows that fully automate the reconstruction of building elements from reality-based surveyed data. This paper introduces an innovative reconstruction pipel

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Nurunnabi, A., and F. N. Teferle. "RESAMPLING METHODS FOR A RELIABLE VALIDATION SET IN DEEP LEARNING BASED POINT CLOUD CLASSIFICATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 617–24. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-617-2022.

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Abstract. A validation data set plays a pivotal role in tweaking a machine learning model trained in a supervised manner. Many existing algorithms select a part of available data by using random sampling to produce a validation set. However, this approach can be prone to overfitting. One should follow careful data splitting to have reliable training and validation sets that can produce a generalized model with a good performance for the unseen (test) data. Data splitting based on resampling techniques involves repeatedly drawing samples from the available data. Hence, resampling methods can gi

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Can, R., S. Kocaman, and A. O. Ok. "A WEBGIS FRAMEWORK FOR SEMI-AUTOMATED GEODATABASE UPDATING ASSISTED BY DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2021 (June 30, 2021): 13–19. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2021-13-2021.

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Abstract. The automation of geoinformation (GI) collection and interpretation has been a fundamental goal for many researchers. The developments in various sensors, platforms, and algorithms have been contributing to the achievement of this goal. In addition, the contributions of citizen science (CitSci) and volunteered geographical information (VGI) concepts have become evident and extensive for the geodata collection and interpretation in the era where information has the utmost importance to solve societal and environmental problems. The web- and mobile-based Geographical Information System

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Soto Vega, Pedro J., Daliana Lobo Torres, Gustavo X. Andrade-Miranda, Gilson A. O. P. da Costa, and Raul Queiroz Feitosa. "Assessing the Generalization Capacity of Convolutional Neural Networks and Vision Transformers for Deforestation Detection in Tropical Biomes." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-3-2024 (November 7, 2024): 519–25. http://dx.doi.org/10.5194/isprs-archives-xlviii-3-2024-519-2024.

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Abstract. Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have become popular for change detection tasks, including the deforestation mapping application. However, not enough attention has been paid to the domain shift issue, which affects classification performance when pre-trained models are used in areas with different forest covers and deforestation practices. This study compares DL methods for deforestation detection, focusing on assessing how well CNNs and ViTs can adapt to the domain shift. Two different models, namely, DeepLabv3+

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Ortega Adarme, M. X., P. J. Soto Vega, G. A. O. P. Costa, R. Q. Feitosa, and C. Heipke. "A DEBIASING VARIATIONAL AUTOENCODER FOR DEFORESTATION MAPPING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 217–23. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-217-2023.

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Abstract. Deep Learning (DL) algorithms provide numerous benefits in different applications, and they usually yield successful results in scenarios with enough labeled training data and similar class proportions. However, the labeling procedure is a cost and time-consuming task. Furthermore, numerous real-world classification problems present a high level of class imbalance, as the number of samples from the classes of interest differ significantly. In various cases, such conditions tend to promote the creation of biased systems, which negatively impact their performance. Designing unbiased sy

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Adão, T., T. M. Pinho, L. Pádua, et al. "USING VIRTUAL SCENARIOS TO PRODUCE MACHINE LEARNABLE ENVIRONMENTS FOR WILDFIRE DETECTION AND SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 20, 2019): 9–15. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-9-2019.

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<p><strong>Abstract.</strong> Today’s climatic proneness to extreme conditions together with human activity have been triggering a series of wildfire-related events that put at risk ecosystems, as well as animal and vegetal patrimony, while threatening dwellers nearby rural or urban areas. When intervention teams - firefighters, civil protection, police - acknowledge these events, usually they have already escalated to proportions hardly controllable mainly due wind gusts, fuel-like solo conditions, among other conditions that propitiate fire spreading.</p> <p>Cur

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Micheal, A. A., K. Vani, S. Sanjeevi, and C. H. Lin. "A TOOL TO ENHANCE THE CAPACITY FOR DEEP LEARNING BASED OBJECT DETECTION AND TRACKING WITH UAV DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 221–26. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-221-2020.

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Abstract. Currently, deployment of UAV has transformed from crucial to day-to-day scenarios for various purposes such as wastage collection, live entertainment, product delivery, town mapping, etc. Object tracking based UAV applications such as traffic monitoring, wildlife monitoring and surveillance have undergone phenomenal changeover due to deep learning based methodologies. With such transformation, there is also lack of resources to practically explore the UAV images and videos with deep learning methodologies. Hence, a deep learning-based object detection and tracking tool with UAV data

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Sunil, A., V. V. Sajithvariyar, V. Sowmya, R. Sivanpillai, and K. P. Soman. "IDENTIFYING OIL PADS IN HIGH SPATIAL RESOLUTION AERIAL IMAGES USING FASTER R-CNN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 155–61. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-155-2021.

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Abstract. Deep learning (DL) methods are used for identifying objects in aerial and ground-based images. Detecting vehicles, roads, buildings, and crops are examples of object identification applications using DL methods. Identifying complex natural and man-made features continues to be a challenge. Oil pads are an example of complex built features due to their shape, size, and presence of other structures like sheds. This work applies Faster Region-based Convolutional Neural Network (R-CNN), a DL-based object recognition method, for identifying oil pads in high spatial resolution (1m), true-c

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Nurunnabi, A., F. N. Teferle, J. Li, R. C. Lindenbergh, and S. Parvaz. "INVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 397–404. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-397-2021.

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Abstract. Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL algorithms degrades, because point clouds are often sparse and have irregular data format. As a result, point clouds are regularly first transformed into voxel grids or image collections. PointNet was the first promising algorithm that feeds point

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Edpuganti, A., P. Akshaya, J. Gouthami, V. V. Sajith Variyar, V. Sowmya, and R. Sivanpillai. "EFFECT OF DATA QUALITY ON WATER BODY SEGMENTATION WITH DEEPLABV3+ ALGORITHM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-3-2023 (September 5, 2023): 81–85. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-3-2023-81-2023.

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Abstract. Training Deep Learning (DL) algorithms for segmenting features require hundreds to thousands of input data and corresponding labels. Generating thousands of input images and labels requires considerable resources and time. Hence, it is common practice to use opensource imagery data and labels available online. Most of these open-source data have little or no metadata describing their quality or suitability making it problematic for training or evaluating DL models. This study evaluated the effect of data quality on training DeepLabV3+, using Sentinel 2 A/B RGB images and labels obtai

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Reddy, O. C., I. D. Kumar, P. Sathvika, V. V. Sajith Variyar, V. Sowmya, and R. Sivanpillai. "EFFECT OF HYPERPARAMETERS ON DEEPLABV3+ PERFORMANCE TO SEGMENT WATER BODIES IN RGB IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-3-2023 (September 5, 2023): 203–9. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-3-2023-203-2023.

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Abstract. Deep Learning (DL) networks used in image segmentation tasks must be trained with input images and corresponding masks that identify target features in them. DL networks learn by iteratively adjusting the weights of interconnected layers using backpropagation, a process that involves calculating gradients and minimizing a loss function. This allows the network to learn patterns and relationships in the data, enabling it to make predictions or classifications on new, unseen data. Training any DL network requires specifying values of the hyperparameters such as input image size, batch

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Akiyama, T. S., J. Marcato Junior, W. N. Gonçalves, et al. "DEEP LEARNING APPLIED TO WATER SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1189–93. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1189-2020.

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Abstract. The use of deep learning (DL) with convolutional neural networks (CNN) to monitor surface water can be a valuable supplement to costly and labour-intense standard gauging stations. This paper presents the application of a recent CNN semantic segmentation method (SegNet) to automatically segment river water in imagery acquired by RGB sensors. This approach can be used as a new supporting tool because there are only a few studies using DL techniques to monitor water resources. The study area is a medium-scale river (Wesenitz) located in the East of Germany. The captured images reflect

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Tian, Xinji, Jiayi Li, and Xin Huang. "A Scene Unmixing Deep Learning Network for Local Climate Zone Mapping and Analysis Using Very High Resolution Remote Sensing Imagery." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 629–35. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-629-2024.

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Abstract. With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes amixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote

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Cao, Y., and M. Scaioni. "A 3D BUILDING INDOOR-OUTDOOR BENCHMARK FOR SEMANTIC SEGMENTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 147–53. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-147-2023.

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Abstract. Both machine learning (ML) and deep learning (DL) algorithms require high-quality training samples as well as precise and thorough annotations in order to work effectively. The 3D building indoor-outdoor dataset (BIO dataset), which is a highly accurate, high level of detail, and high coverage dataset for 3D building point cloud and mesh semantic segmentation, is established as a canonical benchmark dataset. It contains 100 building models, in which building structural elements are annotated into 11 semantic categories. Each building in this dataset has an average of 75,587 triangula

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De Geyter, S., M. Bassier, and M. Vergauwen. "AUTOMATED TRAINING DATA CREATION FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-5/W1-2022 (February 3, 2022): 59–67. http://dx.doi.org/10.5194/isprs-archives-xlvi-5-w1-2022-59-2022.

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Abstract. The creation of as-built Building Information Modelling (BIM) models currently is mostly manual which makes it time consuming and error prone. A crucial step that remains to be automated is the interpretation of the point clouds and the modelling of the BIM geometry. Research has shown that despite the advancements in semantic segmentation, the Deep Learning (DL) networks that are used in the interpretation do not achieve the necessary accuracy for market adoption. One of the main reasons is a lack of sufficient and representative labelled data to train these models. In this work, th

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Li, Feng, Liu Han, Zhu Liujun, Huang Yinyou, and Guo Song. "URBAN VEGETATION MAPPING BASED ON THE HJ-1 NDVI RECONSTRCTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 867–71. http://dx.doi.org/10.5194/isprs-archives-xli-b8-867-2016.

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HJ-1A/B NDVI (HJ NDVI) time-series data possess relatively high spatio-temporal resolution which is significant for the research on urban areas. However, its application is hindered by noise resulting from the restrictions of imaging quality and limits of the satellite platform. The NDVI noise reduction is necessary. Some noise-reduction techniques including the asymmetric Gaussian filter (AG), the double logistic filter (DL), the Savitzky-Golay (S-G) filter and the harmonic analysis (Hants) of NDVI time-series have been used to carry out the NDVI time series reconstruction, and based on the c

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Buyukdemircioglu, M., R. Can, and S. Kocaman. "DEEP LEARNING BASED ROOF TYPE CLASSIFICATION USING VERY HIGH RESOLUTION AERIAL IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 55–60. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-55-2021.

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Abstract. Automatic detection, segmentation and reconstruction of buildings in urban areas from Earth Observation (EO) data are still challenging for many researchers. Roof is one of the most important element in a building model. The three-dimensional geographical information system (3D GIS) applications generally require the roof type and roof geometry for performing various analyses on the models, such as energy efficiency. The conventional segmentation and classification methods are often based on features like corners, edges and line segments. In parallel to the developments in computer h

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Asma Abdul Qadeer, Rabia Mehmood, Saadia Baraan, Nadia Junaid, Sara Bashir Kant, and Sarah Habib. "Anemia among pregnant women a major concern for achieving universal health coverage." Professional Medical Journal 30, no. 01 (2023): 63–67. http://dx.doi.org/10.29309/tpmj/2023.30.01.7097.

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Objective: To assess the frequency of anemia among pregnant females visiting Rawal Institute of Health Sciences and to find out the risk factors contributing to anemia. Study Design: Cross Sectional Descriptive study. Setting: Rawal Institute of Health Sciences, Islamabad, Pakistan. Period: May to July 2019. Material & Methods: A study was carried out to find the frequency of anemia among 100 pregnant women through non-probability convenient sampling at RIHS using a structured questionnaire. Hemoglobin concentration data in the blood was collected from their antenatal archives. Results: He

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Hasan, A., M. R. Udawalpola, C. Witharana, and A. K. Liljedahl. "COUNTING ICE-WEDGE POLYGONS FROM SPACE: USE OF COMMERCIAL SATELLITE IMAGERY TO MONITOR CHANGING ARCTIC POLYGONAL TUNDRA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 67–72. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-67-2021.

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Abstract. The microtopography associated with ice wedge polygons (IWPs) governs the Arctic ecosystem from local to regional scales due to the impacts on the flow and storage of water and therefore, vegetation and carbon. Increasing subsurface temperatures in Arctic permafrost landscapes cause differential ground settlements followed by a series of adverse microtopographic transitions at sub decadal scale. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. Dramatic microtopographic transformation of low-centered into high-centered IWPs can be identif

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Moumouris, Tilemachos, Vasileios Tsironis, Athena Psalta, and Konstantinos Karantzalos. "Large Scale Mowing Event Detection on Dense Time Series Data Using Deep Learning Methods and Knowledge Distillation." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-7-2025 (May 24, 2025): 43–48. https://doi.org/10.5194/isprs-archives-xlviii-m-7-2025-43-2025.

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Abstract. The intensity of agricultural land use is a critical factor for food security and biodiversity preservation, necessitating effective and scalable monitoring techniques. This study presents a novel approach for large-scale mowing event frequency detection using dense time series data and deep learning (DL) methods. Leveraging Sentinel-2 and Landsat data, we developed a benchmark dataset of over 1,600 annotated parcels in Greece, capturing mowing events through photo-interpretation and Enhanced Vegetation Index (EVI) analysis. Four DL architectures were evaluated, including MLP, ResNet

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Samson, T. K., F. O. Aweda, A. J. Omoliki, G. C. George, O. F. Oladapo, and E. I. Ogunwale. "Prediction of monthly rainfall in selected African stations using deep learning algorithms." IOP Conference Series: Earth and Environmental Science 1428, no. 1 (2024): 012005. https://doi.org/10.1088/1755-1315/1428/1/012005.

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Abstract Precipitation has a huge impact on human life and culture. Agriculture and public infrastructure is dependent on accurate rainfall forecasting, which can aid in water supply planning, reservoir management, and flood mitigation. In view of the climate change, it is very imperative to provide a reliable forecast of rainfall leveraging on Deep Learning algorithms. This study therefore compared the forecasting performance of five different Deep Learning (DL) algorithms in predicting rainfall in five African stations (Abuja, Cairo, Nairobi, Pretoria, and Yaoundé). Deep Learning algorithms

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Belwalkar, A., A. Nath, and O. Dikshit. "SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES USING VARIATIONAL AUTOENCODER AND CONVOLUTION NEURAL NETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 613–20. http://dx.doi.org/10.5194/isprs-archives-xlii-5-613-2018.

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<p><strong>Abstract.</strong> In this paper, we propose a spectral-spatial feature extraction framework based on deep learning (DL) for hyperspectral image (HSI) classification. In this framework, the variational autoencoder (VAE) is used for extraction of spectral features from two widely used hyperspectral datasets- Kennedy Space Centre, Florida and University of Pavia, Italy. Additionally, a convolutional neural network (CNN) is utilized to obtain spatial features. The spatial and spectral feature vectors are then stacked together to form a joint feature vector. Finally, t

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Mirmazloumi, S. M., Á. F. Gambin, Y. Wassie, et al. "INSAR DEFORMATION TIME SERIES CLASSIFICATION USING A CONVOLUTIONAL NEURAL NETWORK." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 307–12. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-307-2022.

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Abstract. Temporal analysis of deformations Time Series (TS) provides detailed information of various natural and humanmade displacements. Interferometric Synthetic Aperture Radar (InSAR) generates millimetre-scale products, indicating the chronicle behaviour of detected targets via TS products. Deep Learning (DL) can handle a massive load of InSAR TS to categorize significant movements from non-moving targets. To this end, we employed a supervised Convolutional Neural Network (CNN) model to distinguish five deformations trends, including Stable, Linear, Quadratic, Bilinear, and Phase Unwrappi

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Teoh, Jeremy Yuen Chun, Ning Hong Chan, Siu Ming Mak, et al. "Inflammatory Myofibroblastic Tumours of the Urinary Bladder: Multi-Centre 18-Year Experience." Urologia Internationalis 94, no. 1 (2014): 31–36. http://dx.doi.org/10.1159/000358732.

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Objective: To review a series of inflammatory myofibroblastic tumours (IMTs) of the urinary bladder in 10 hospitals in Hong Kong. Methods: A database search in the pathology archives of 10 hospitals in Hong Kong from 1995 to 2013 was performed using the key words ‘inflammatory myofibroblastic tumour', ‘inflammatory pseudotumour' and ‘spindle cell lesion'. Patient characteristics, clinical features, histological features, immunohistochemical staining results and treatment outcomes were reviewed. Results: Nine cases of IMT of the urinary bladder were retrieved. The mean age was 45.4 ± 22.8 years

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Hrubý, Robert, Daniel Kvak, Anna Chromcová, and Marek Biroš. "FULLY INTEGRATED DECISION-SUPPORT SYSTEM FOR DETECTION AND SEGMENTATION OF BREAST LESIONS IN DIGITAL MAMMOGRAM." Medsoft 2022 34, no. 1 (2022): 50–54. https://doi.org/10.35191/medsoft_2022_1_34_50_54.

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Breast cancer is one of the most prevalent forms of cancer affecting women. Detection of suspicious lesions on mammographic images is considered a challenging task due to the variability of lesion sizes and shapes, the problematic margins of the findings, and some extremely small lesions that are difficult to localize. With the increasing availability of digitized clinical archives and the development of complex deep learning (DL) methods, we are witnessing a trend towards the integration of robust computer-aided detection (CAD) systems to assist in the automatic segmentation of lesions on mam

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He, Z., H. He, J. Li, M. A. Chapman, and H. Ding. "A SHORT-CUT CONNECTIONS-BASED NEURAL NETWORK FOR BUILDING EXTRACTION FROM HIGH RESOLUTION ORTHOIMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (May 30, 2022): 39–44. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-39-2022.

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Abstract. Extracting building footprints utilizing deep learning-based (DL-based) methods for high-resolution remote sensing images is one of the current research interest areas. However, the extraction results suffer from blurred edges, rounded corners and detail loss in general. Hence, this article presents a detail-oriented deep learning network named eU-Net (enhanced U-Net). The method adopted in this study, imagery send into the pre-module, which consists of the Canny edge detector, Principal Component Analysis (PCA) and the inter-band ratio operations, before feeding them into the networ

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Amri, Mohammed Bilel, Dounia Yedjour, Mohammed El Amin Larabi, and Faouzi Berrichi. "Advancing Hyperspectral Image Classification with Deep Features Learning and Evolutionary Algorithms." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-7-2025 (May 24, 2025): 1–6. https://doi.org/10.5194/isprs-archives-xlviii-m-7-2025-1-2025.

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Abstract. Hyperspectral Images (HSI) reveal the secrets of land cover at a granular level, capturing hundreds of narrow spectral bands rich in detailed information. However, the sheer dimensionality of these data poses significant challenges to traditional Machine Learning (ML) methods. This research tackles the high-dimensional challenge of HSI classification with an advanced hybrid framework, leveraging the power of Deep Learning (DL), ML and Evolutionary Algorithms (EA) to conquer this challenge and achieve accurate HSI classification. We unleash the data's inherent wisdom via deep Features

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Pirotti, F., C. Zanchetta, M. Previtali, and S. Della Torre. "DETECTION OF BUILDING ROOFS AND FACADES FROM AERIAL LASER SCANNING DATA USING DEEP LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W11 (May 5, 2019): 975–80. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w11-975-2019.

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<p><strong>Abstract.</strong> In this work we test the power of prediction of deep learning for detection of buildings from aerial laser scanner point cloud information. Automatic extraction of built features from remote sensing data is of extreme interest for many applications. In particular latest paradigms of 3D mapping of buildings, such as CityGML and BIM, can benefit from an initial determination of building geometries. In this work we used a LiDAR dataset of urban environment from the ISPRS benchmark on urban object detection. The dataset is labelled with eight classes

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Murray, J., I. Sargent, D. Holland, et al. "OPPORTUNITIES FOR MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN NATIONAL MAPPING AGENCIES: ENHANCING ORDNANCE SURVEY WORKFLOW." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 185–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-185-2020.

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Abstract. National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, a

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Yadav, R., A. Nascetti, and Y. Ban. "BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1377–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1377-2022.

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Abstract. Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. Handling such data is tricky and consumes large memory for processing. Most of this information is not necessary when we are looking for a particular type of urban change. In this study, we propose an automat

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El Kohli, S., Y. Jannaj, M. Maanan, and H. Rhinane. "DEEP LEARNING: NEW APPROACH FOR DETECTING SCHOLAR EXAMS FRAUD." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W3-2021 (January 10, 2022): 103–7. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w3-2021-103-2022.

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Abstract. Cheating in exams is a worldwide phenomenon that hinders efforts to assess the skills and growth of students. With scientific and technological progress, it has become possible to develop detection systems in particular a system to monitor the movements and gestures of the candidates during the exam. Individually or collectively. Deep learning (DL) concepts are widely used to investigate image processing and machine learning applications. Our system is based on the advances in artificial intelligence, particularly 3D Convolutional Neural Network (3D CNN), object detector methods, Ope

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