{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:22:09Z","timestamp":1775326929096,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Network Security and Information Program of the Chinese Academy of Sciences","award":["CAS-WX2021SF-0106-02"],"award-info":[{"award-number":["CAS-WX2021SF-0106-02"]}]},{"name":"Network Security and Information Program of the Chinese Academy of Sciences","award":["20190ZKK1006"],"award-info":[{"award-number":["20190ZKK1006"]}]},{"name":"Network Security and Information Program of the Chinese Academy of Sciences","award":["42130508"],"award-info":[{"award-number":["42130508"]}]},{"name":"Network Security and Information Program of the Chinese Academy of Sciences","award":["42071389"],"award-info":[{"award-number":["42071389"]}]},{"name":"Network Security and Information Program of the Chinese Academy of Sciences","award":["KPI011"],"award-info":[{"award-number":["KPI011"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["CAS-WX2021SF-0106-02"],"award-info":[{"award-number":["CAS-WX2021SF-0106-02"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["20190ZKK1006"],"award-info":[{"award-number":["20190ZKK1006"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["42130508"],"award-info":[{"award-number":["42130508"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["42071389"],"award-info":[{"award-number":["42071389"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["KPI011"],"award-info":[{"award-number":["KPI011"]}]},{"name":"National Natural Science Foundation of China","award":["CAS-WX2021SF-0106-02"],"award-info":[{"award-number":["CAS-WX2021SF-0106-02"]}]},{"name":"National Natural Science Foundation of China","award":["20190ZKK1006"],"award-info":[{"award-number":["20190ZKK1006"]}]},{"name":"National Natural Science Foundation of China","award":["42130508"],"award-info":[{"award-number":["42130508"]}]},{"name":"National Natural Science Foundation of China","award":["42071389"],"award-info":[{"award-number":["42071389"]}]},{"name":"National Natural Science Foundation of China","award":["KPI011"],"award-info":[{"award-number":["KPI011"]}]},{"name":"Key Project of Innovation LREIS","award":["CAS-WX2021SF-0106-02"],"award-info":[{"award-number":["CAS-WX2021SF-0106-02"]}]},{"name":"Key Project of Innovation LREIS","award":["20190ZKK1006"],"award-info":[{"award-number":["20190ZKK1006"]}]},{"name":"Key Project of Innovation LREIS","award":["42130508"],"award-info":[{"award-number":["42130508"]}]},{"name":"Key Project of Innovation LREIS","award":["42071389"],"award-info":[{"award-number":["42071389"]}]},{"name":"Key Project of Innovation LREIS","award":["KPI011"],"award-info":[{"award-number":["KPI011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Approximately 1 billion people worldwide currently inhabit slum areas. The UN Sustainable Development Goal (SDG 11.1) underscores the imperative of upgrading all slums by 2030 to ensure adequate housing for everyone. Geo-locations of slums help local governments with upgrading slums and alleviating urban poverty. Remote sensing (RS) technology, with its excellent Earth observation capabilities, can play an important role in slum mapping. Deep learning (DL)-based RS information extraction methods have attracted a lot of attention. Currently, DL-based slum mapping studies typically uses three optical bands to adapt to existing models, neglecting essential geo-scientific information, such as spectral and textural characteristics, which are beneficial for slum mapping. Inspired by the geoscience-aware DL paradigm, we propose the Geoscience-Aware Network for slum mapping (GASlumNet), aiming to improve slum mapping accuracies via incorporating the DL model with geoscientific prior knowledge. GASlumNet employs a two-stream architecture, combining ConvNeXt and UNet. One stream concentrates on optical feature representation, while the other emphasizes geo-scientific features. Further, the feature-level and decision-level fusion mechanisms are applied to optimize deep features and enhance model performance. We used Jilin-1 Spectrum 01 and Sentinel-2 images to perform experiments in Mumbai, India. The results demonstrate that GASlumNet achieves higher slum mapping accuracy than the comparison models, with an intersection over union (IoU) of 58.41%. Specifically, GASlumNet improves the IoU by 4.60~5.97% over the baseline models, i.e., UNet and ConvNeXt-UNet, which exclusively utilize optical bands. Furthermore, GASlumNet enhances the IoU by 10.97% compared to FuseNet, a model that combines optical bands and geo-scientific features. Our method presents a new technical solution to achieve accurate slum mapping, offering potential benefits for regional and global slum mapping and upgrading initiatives.<\/jats:p>","DOI":"10.3390\/rs16020260","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T05:47:21Z","timestamp":1704865641000},"page":"260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping"],"prefix":"10.3390","volume":"16","author":[{"given":"Wei","family":"Lu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6219-6251","authenticated-orcid":false,"given":"Yunfeng","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Feifei","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China"},{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}]},{"given":"Zhiming","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yanzhao","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"ref_1","unstructured":"UN-Habitat (2020). World Cities Report 2020: The Value of Sustainable Urbanization, United Nations Human Settlements Programme."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102765","DOI":"10.1016\/j.habitatint.2023.102765","article-title":"The Connection between Slums and COVID-19 Cases in Jakarta, Indonesia: A Case Study of Kapuk Urban Village","volume":"134","author":"Wirastri","year":"2023","journal-title":"Habitat Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106392","DOI":"10.1016\/j.landusepol.2022.106392","article-title":"Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia","volume":"123","author":"Thomson","year":"2022","journal-title":"Land Use Policy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102768","DOI":"10.1016\/j.habitatint.2023.102768","article-title":"Spatial and Temporal Impacts on Socio-Economic Conditions in the Yangon Slums","volume":"134","author":"Maung","year":"2023","journal-title":"Habitat Int."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"UN-Habitat (2003). The Challenge of Slums: Global Report on Human Settlements, 2003, Routledge.","DOI":"10.1108\/meq.2004.15.3.337.3"},{"key":"ref_6","unstructured":"UN-Habitat (2023, November 27). Slum Almanac 2015\u20132016: Tracking Improvement in the Lives of Slum Dwellers. Participatory Slum Upgrading Programme. Available online: https:\/\/unhabitat.org\/sites\/default\/files\/documents\/2019-05\/slum_almanac_2015-2016_psup.pdf."},{"key":"ref_7","unstructured":"United Nations (2015). Transforming Our World: The 2030 Agenda for Sustainable Development, United Nations."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106253","DOI":"10.1016\/j.worlddev.2023.106253","article-title":"Identifying Deprived \u201cSlum\u201d Neighbourhoods in the Greater Accra Metropolitan Area of Ghana Using Census and Remote Sensing Data","volume":"167","author":"MacTavish","year":"2023","journal-title":"World Dev."},{"key":"ref_9","unstructured":"Kuffer, M., Abascal, A., Vanhuysse, S., Georganos, S., Wang, J., Thomson, D.R., Boanada, A., and Roca, P. (2023). Advanced Remote Sensing for Urban and Landscape Ecology, Springer."},{"key":"ref_10","unstructured":"UN-Habitat (2023, November 27). Metadata on SDGs Indicator 11.1. 1 Indicator Category: Tier I. UN Human Settlements Program, Nairobi. Available online: http:\/\/unhabitat.org\/sites\/default\/files\/2020\/06\/metadata_on_sdg_indicator_11.1.1.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.compenvurbsys.2011.11.001","article-title":"An Ontology of Slums for Image-Based Classification","volume":"36","author":"Kohli","year":"2012","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_12","first-page":"3","article-title":"Local Ontologies for Object-Based Slum Identification and Classification","volume":"3","author":"Kohli","year":"2012","journal-title":"Environs"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1080\/14498596.2016.1138247","article-title":"Urban Slum Detection Using Texture and Spatial Metrics Derived from Satellite Imagery","volume":"61","author":"Kohli","year":"2016","journal-title":"J. Spat. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Badmos, O.S., Rienow, A., Callo-Concha, D., Greve, K., and J\u00fcrgens, C. (2018). Urban Development in West Africa\u2014Monitoring and Intensity Analysis of Slum Growth in Lagos: Linking Pattern and Process. Remote Sens., 10.","DOI":"10.3390\/rs10071044"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1109\/JSTARS.2016.2538563","article-title":"Extraction of Slum Areas from VHR Imagery Using GLCM Variance","volume":"9","author":"Kuffer","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mudau, N., and Mhangara, P. (2023). Mapping and Assessment of Housing Informality Using Object-Based Image Analysis: A Review. Urban Sci., 7.","DOI":"10.3390\/urbansci7030098"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s43762-023-00106-w","article-title":"Urban Upgrading of Slums: Baghdad and London Slums as Study Models for Urban Rehabilitation","volume":"3","author":"Abed","year":"2023","journal-title":"Comput. Urban Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mahabir, R., Croitoru, A., Crooks, A.T., Agouris, P., and Stefanidis, A. (2018). A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities. Urban Sci., 2.","DOI":"10.3390\/urbansci2010008"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kuffer, M., Wang, J., Nagenborg, M., Pfeffer, K., Kohli, D., Sliuzas, R., and Persello, C. (2018). The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7110428"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2214690","DOI":"10.1080\/22797254.2023.2214690","article-title":"Capturing Deprived Areas Using Unsupervised Machine Learning and Open Data: A Case Study in S\u00e3o Paulo, Brazil","volume":"56","author":"Kuffer","year":"2023","journal-title":"Eur. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dewan, A., Alrasheedi, K., and El-Mowafy, A. (2023, January 16\u201321). Mapping Informal Settings Using Machine Learning Techniques, Object-Based Image Analysis and Local Knowledge. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Pasadena, CA, USA.","DOI":"10.1109\/IGARSS52108.2023.10283462"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Duque, J.C., Patino, J.E., and Betancourt, A. (2017). Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9090895"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1080\/01431161.2020.1834167","article-title":"Slum Extraction from High Resolution Satellite Data Using Mathematical Morphology Based Approach","volume":"42","author":"Prabhu","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1007\/s10994-023-06327-8","article-title":"Interpreting Machine-Learning Models in Transformed Feature Space with an Application to Remote-Sensing Classification","volume":"112","author":"Brenning","year":"2023","journal-title":"Mach. Learn."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep Learning in Environmental Remote Sensing: Achievements and Challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4340","DOI":"10.1109\/TGRS.2020.3016820","article-title":"More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"102926","article-title":"Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1109\/JSTARS.2023.3243396","article-title":"A Dual-Branch Deep Learning Architecture for Multisensor and Multitemporal Remote Sensing Semantic Segmentation","volume":"16","author":"Bergamasco","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.isprsjprs.2019.02.006","article-title":"Semantic Segmentation of Slums in Satellite Images Using Transfer Learning on Fully Convolutional Neural Networks","volume":"150","author":"Wurm","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"101981","DOI":"10.1016\/j.habitatint.2019.04.008","article-title":"Transfer Learning Approach to Map Urban Slums Using High and Medium Resolution Satellite Imagery","volume":"88","author":"Verma","year":"2019","journal-title":"Habitat Int."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5251","DOI":"10.1109\/JSTARS.2020.3018862","article-title":"Satellite-Based Mapping of Urban Poverty with Transfer-Learned Slum Morphologies","volume":"13","author":"Stark","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3512805","DOI":"10.1109\/LGRS.2022.3180162","article-title":"Mapping Temporary Slums from Satellite Imagery Using a Semi-Supervised Approach","volume":"19","author":"Rehman","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","first-page":"3299","article-title":"Slum Image Detection and Localization Using Transfer Learning: A Case Study in Northern Morocco","volume":"13","author":"Dahmani","year":"2023","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100047","DOI":"10.1016\/j.srs.2022.100047","article-title":"Geoscience-Aware Deep Learning: A New Paradigm for Remote Sensing","volume":"5","author":"Ge","year":"2022","journal-title":"Sci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1007\/s10346-023-02089-5","article-title":"A Dual-Encoder U-Net for Landslide Detection Using Sentinel-2 and DEM Data","volume":"20","author":"Lu","year":"2023","journal-title":"Landslides"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very High Resolution Urban Remote Sensing with Multimodal Deep Networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","unstructured":"Hazirbas, C., Ma, L., Domokos, C., and Cremers, D. (2017). Part I 13, Proceedings of the Computer Vision\u2013ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, 20\u201324 November 2016, Springer. Revised Selected Papers."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1109\/TIP.2023.3245324","article-title":"Multimodal Remote Sensing Image Segmentation with Intuition-Inspired Hypergraph Modeling","volume":"32","author":"He","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","unstructured":"Xiong, Z., Chen, S., Wang, Y., Mou, L., and Zhu, X.X. (2023). GAMUS: A Geometry-Aware Multi-Modal Semantic Segmentation Benchmark for Remote Sensing Data. arXiv."},{"key":"ref_40","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). Part III 18, Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, 5\u20139 October 2015, Springer."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A Convnet for the 2020s. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"112622","DOI":"10.1016\/j.rse.2021.112622","article-title":"ND-Space: Normalized Difference Spectral Mapping","volume":"264","author":"Philpot","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Peng, F., Lu, W., Hu, Y., and Jiang, L. (2023). Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs). Remote Sens., 15.","DOI":"10.3390\/rs15194671"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wurm, M., Weigand, M., Schmitt, A., Gei\u00df, C., and Taubenb\u00f6ck, H. (2017, January 6\u20138). Exploitation of Textural and Morphological Image Features in Sentinel-2A Data for Slum Mapping. Proceedings of the 2017 Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates.","DOI":"10.1109\/JURSE.2017.7924586"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.isprsjprs.2014.05.005","article-title":"Derivation of an Urban Materials Spectral Library through Emittance and Reflectance Spectroscopy","volume":"94","author":"Kotthaus","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image Is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. (2021, January 3\u20138). Attentional Feature Fusion. Proceedings of the 2021 IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.-A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_53","unstructured":"Phan, T.H., and Yamamoto, K. (2020). Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses. arXiv."},{"key":"ref_54","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gram-Hansen, B.J., Helber, P., Varatharajan, I., Azam, F., Coca-Castro, A., Kopackova, V., and Bilinski, P. (2019, January 27\u201328). Mapping Informal Settlements in Developing Countries Using Machine Learning and Low Resolution Multi-Spectral Data. Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society, Honolulu, HI, USA.","DOI":"10.1145\/3306618.3314253"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1080\/17538947.2023.2210311","article-title":"GMTS: GNN-Based Multi-Scale Transformer Siamese Network for Remote Sensing Building Change Detection","volume":"16","author":"Song","year":"2023","journal-title":"Int. J. Digit. Earth"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/260\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:43:17Z","timestamp":1760103797000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":56,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020260"],"URL":"https:\/\/doi.org\/10.3390\/rs16020260","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]}}}