{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T22:32:58Z","timestamp":1766788378080,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI) GmbH, the Austrian Federal Ministry for Digital and Economic Affairs, the Christian Doppler Research Association, and MSF Austria"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The improvement in computer vision, sensor quality, and remote sensing data availability makes satellite imagery increasingly useful for studying human settlements. Several challenges remain to be overcome for some types of settlements, particularly for internally displaced populations (IDPs) and refugee camps. Refugee-dwelling footprints and detailed information derived from satellite imagery are critical for a variety of applications, including humanitarian aid during disasters or conflicts. Nevertheless, extracting dwellings remains difficult due to their differing sizes, shapes, and location variations. In this study, we use U-Net and residual U-Net to deal with dwelling classification in a refugee camp in northern Cameroon, Africa. Specifically, two semantic segmentation networks are adapted and applied. A limited number of randomly divided sample patches is used to train and test the networks based on a single image of the WorldView-3 satellite. Our accuracy assessment was conducted using four different dwelling categories for classification purposes, using metrics such as Precision, Recall, F1, and Kappa coefficient. As a result, F1 ranges from 81% to over 99% and approximately 88.1% to 99.5% based on the U-Net and the residual U-Net, respectively.<\/jats:p>","DOI":"10.3390\/rs14246382","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T08:41:41Z","timestamp":1671439301000},"page":"6382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Mapping Dwellings in IDP\/Refugee Settlements Using Deep Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9664-8770","authenticated-orcid":false,"given":"Omid","family":"Ghorbanzadeh","sequence":"first","affiliation":[{"name":"Christian Doppler Laboratory for Geospatial and EO-based Humanitarian Technologies GEOHUM, Department of Geoinformatics\u2014Z-GIS, University of Salzburg, 5020 Salzburg, Austria"},{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI), Landstra\u00dfer Hauptstra\u00dfe 5, 1030 Vienna, Austria"}]},{"given":"Alessandro","family":"Crivellari","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5473-3344","authenticated-orcid":false,"given":"Dirk","family":"Tiede","sequence":"additional","affiliation":[{"name":"Christian Doppler Laboratory for Geospatial and EO-based Humanitarian Technologies GEOHUM, Department of Geoinformatics\u2014Z-GIS, University of Salzburg, 5020 Salzburg, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-741X","authenticated-orcid":false,"given":"Pedram","family":"Ghamisi","sequence":"additional","affiliation":[{"name":"Institute of Advanced Research in Artificial Intelligence (IARAI), Landstra\u00dfer Hauptstra\u00dfe 5, 1030 Vienna, Austria"},{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Machine Learning Group, Chemnitzer Str. 40, 09599 Freiberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0619-0098","authenticated-orcid":false,"given":"Stefan","family":"Lang","sequence":"additional","affiliation":[{"name":"Christian Doppler Laboratory for Geospatial and EO-based Humanitarian Technologies GEOHUM, Department of Geoinformatics\u2014Z-GIS, University of Salzburg, 5020 Salzburg, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gella, G.W., Wendt, L., Lang, S., Tiede, D., Hofer, B., Gao, Y., and Braun, A. (2022). Mapping of Dwellings in IDP\/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural Network. Remote Sens., 14.","DOI":"10.3390\/rs14030689"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/22797254.2019.1684208","article-title":"Earth observation tools and services to increase the effectiveness of humanitarian assistance","volume":"53","author":"Lang","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Van Den Hoek, J., and Friedrich, H.K. (2021). Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda. Remote Sens., 13.","DOI":"10.20944\/preprints202107.0199.v1"},{"key":"ref_4","first-page":"1","article-title":"Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"1","article-title":"Transferring CNN With Adaptive Learning for Remote Sensing Scene Classification","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L.H., Qiu, C., and Zhu, X.X. (2019). SEN12MS\u2013A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1\/2 Imagery for Deep Learning and Data Fusion. arXiv.","DOI":"10.5194\/isprs-annals-IV-2-W7-153-2019"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sumbul, G., Charfuelan, M., Demir, B., and Markl, V. (August, January 28). Bigearthnet: A large-scale benchmark archive for remote sensing image understanding. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900532"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3215209","article-title":"Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection","volume":"60","author":"Ghorbanzadeh","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","unstructured":"Zhu, X.X., Hu, J., Qiu, C., Shi, Y., Kang, J., Mou, L., Bagheri, H., H\u00e4berle, M., Hua, Y., and Huang, R. (2019). So2Sat LCZ42: A benchmark dataset for global local climate zones classification. arXiv."},{"key":"ref_10","unstructured":"Center for International Earth Science Information Network (CIESIN), Flowminder Foundation, United Nations Population Fund (UNFPA), and WorldPop, University of Southampton (2022, November 10). Mapping and Classifying Settlement Locations 2020. Available online: https:\/\/eprints.soton.ac.uk\/469540\/."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-020-00580-5","article-title":"Outlining where humans live, the World Settlement Footprint 2015","volume":"7","author":"Marconcini","year":"2020","journal-title":"Sci. Data"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1109\/JSTARS.2013.2271445","article-title":"A global human settlement layer from optical HR\/VHR RS data: Concept and first results","volume":"6","author":"Pesaresi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","unstructured":"Nations, U. (2011). World Urbanization Prospects: The 2005 Revision, United Nations Publications."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Freire, S., Haag, F., Halkia, M., Julea, A., Kemper, T., and Soille, P. (2015, January 11\u201315). Global human settlement analysis for disaster risk reduction. Proceedings of the International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Berlin\/Heidelberg, Germany.","DOI":"10.5194\/isprsarchives-XL-7-W3-837-2015"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aguilar, R., and Kuffer, M. (2020). Cloud computation using high-resolution images for improving the SDG indicator on open spaces. Remote Sens., 12.","DOI":"10.3390\/rs12071144"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102285","DOI":"10.1016\/j.ijdrr.2021.102285","article-title":"Agent-based modelling of post-disaster recovery with remote sensing data","volume":"60","author":"Ghaffarian","year":"2021","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Kerle, N., Pasolli, E., and Jokar Arsanjani, J. (2019). Post-disaster building database updating using automated deep learning: An integration of pre-disaster OpenStreetMap and multi-temporal satellite data. Remote Sens., 11.","DOI":"10.3390\/rs11202427"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.1080\/01431161.2015.1035412","article-title":"Remote sensing of violent conflict: Eyes from above","volume":"36","author":"Witmer","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gruca, A., Herruzo, P., R\u00edpodas, P., Kucik, A., Briese, C., Kopp, M.K., Hochreiter, S., Ghamisi, P., and Kreil, D.P. (2021, January 1\u20135). CDCEO\u201921-First Workshop on Complex Data Challenges in Earth Observation. Proceedings of the the 30th ACM International Conference on Information & Knowledge Management, Virtual.","DOI":"10.1145\/3459637.3482044"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rizeei, H.M., and Pradhan, B. (2019). Urban mapping accuracy enhancement in high-rise built-up areas deployed by 3D-orthorectification correction from WorldView-3 and LiDAR imageries. Remote Sens., 11.","DOI":"10.3390\/rs11060692"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1080\/22797254.2020.1759456","article-title":"Transferable instance segmentation of dwellings in a refugee camp-integrating CNN and OBIA","volume":"54","author":"Ghorbanzadeh","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lang, S., Tiede, D., Gella, G.W., and Wendt, L. (2022). Comparing OBIA-Generated Labels and Manually Annotated Labels for Semantic Segmentation in Extracting Refugee-Dwelling Footprints. Appl. Sci., 12.","DOI":"10.3390\/app122111226"},{"key":"ref_23","first-page":"185","article-title":"Automated analysis of satellite imagery to provide information products for humanitarian relief operations in refugee camps\u2013from scientific development towards operational services","volume":"3","author":"Tiede","year":"2013","journal-title":"PFG Photogramm."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/agile-giss-3-36-2022","article-title":"Assessing the Influences of Band Selection and Pretrained Weights on Semantic-Segmentation-Based Refugee Dwelling Extraction from Satellite Imagery","volume":"3","author":"Gao","year":"2022","journal-title":"AGILE GISci. Ser."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1553\/giscience2021_01_s220","article-title":"Testing transferability of deep-learning-based dwelling extraction in refugee camps","volume":"9","author":"Gella","year":"2021","journal-title":"GI_Forum"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5709","DOI":"10.1080\/01431161.2010.496803","article-title":"Earth observation (EO)-based ex post assessment of internally displaced person (IDP) camp evolution and population dynamics in Zam Zam, Darfur","volume":"31","author":"Lang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"L\u00fcthje, F., Tiede, D., and F\u00fcreder, P. (2015, January 7\u201310). Don\u2019t see the dwellings for the trees: Quantifying the effect of tree growth on multi-temporal dwelling extraction in a refugee camp. Proceedings of the GI_Forum, Salzburg, Austria.","DOI":"10.1553\/giscience2015s406"},{"key":"ref_28","unstructured":"Tiede, D., Lang, S., H\u00f6lbling, D., and F\u00fcreder, P. (July, January 29). Transferability of OBIA rulesets for IDP camp analysis in Darfur. Proceedings of the GEOBIA, Ghent, Belgium."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1007\/s10346-021-01843-x","article-title":"Landslide detection using deep learning and object-based image analysis","volume":"19","author":"Ghorbanzadeh","year":"2022","journal-title":"Landslides"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tiede, D., Krafft, P., F\u00fcreder, P., and Lang, S. (2017). Stratified template matching to support refugee camp analysis in OBIA workflows. Remote Sens., 9.","DOI":"10.3390\/rs9040326"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/JSTARS.2010.2053700","article-title":"Enumeration of dwellings in Darfur Camps from GeoEye-1 satellite images using mathematical morphology","volume":"4","author":"Kemper","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Laneve, G., Santilli, G., and Lingenfelder, I. (August, January 31). Development of automatic techniques for refugee camps monitoring using very high spatial resolution (VHSR) satellite imagery. Proceedings of the 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, USA.","DOI":"10.1109\/IGARSS.2006.216"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"20170363","DOI":"10.1098\/rsta.2017.0363","article-title":"Humanitarian applications of machine learning with remote-sensing data: Review and case study in refugee settlement mapping","volume":"376","author":"Quinn","year":"2018","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1111\/tgis.12766","article-title":"Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan","volume":"25","author":"Tiede","year":"2021","journal-title":"Trans. GIS"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Duan, Y., Zhang, W., Huang, P., He, G., and Guo, H. (2021). A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images. Remote Sens., 13.","DOI":"10.3390\/rs13224576"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.isprsjprs.2021.01.008","article-title":"Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images","volume":"173","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-022-16665-7","article-title":"Analysis of environmental factors using AI and ML methods","volume":"12","author":"Haq","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Rahimzad, M., Tavakkoli Piralilou, S., Ghorbanzadeh, O., Homayouni, S., Blaschke, T., Lim, S., and Ghamisi, P. (2021). Unsupervised deep learning for landslide detection from multispectral sentinel-2 imagery. Remote Sens., 13.","DOI":"10.3390\/rs13224698"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2846","DOI":"10.1002\/esp.5427","article-title":"Influence of Orographic Precipitation on Coevolving Landforms and Vegetation in Semi-arid Ecosystems","volume":"47","author":"Srivastava","year":"2022","journal-title":"Earth Surf. Process. Landforms"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jozdani, S.E., Johnson, B.A., and Chen, D. (2019). Comparing deep neural networks, ensemble classifiers, and support vector machine algorithms for object-based urban land use\/land cover classification. Remote Sens., 11.","DOI":"10.3390\/rs11141713"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., and Zhang, Y. (2018). Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071119"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cui, B., Fei, D., Shao, G., Lu, Y., and Chu, J. (2019). Extracting raft aquaculture areas from remote sensing images via an improved U-net with a PSE structure. Remote Sens., 11.","DOI":"10.3390\/rs11172053"},{"key":"ref_44","unstructured":"Sherrah, J. (2016). Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"DeLancey, E.R., Simms, J.F., Mahdianpari, M., Brisco, B., Mahoney, C., and Kariyeva, J. (2019). Comparing deep learning and shallow learning for large-scale wetland classification in Alberta, Canada. Remote Sens., 12.","DOI":"10.3390\/rs12010002"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and cOmputer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"840","DOI":"10.18178\/ijmlc.2019.9.6.881","article-title":"Capsnet, cnn, fcn: Comparative performance evaluation for image classification","volume":"9","author":"Jiang","year":"2019","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/LGRS.2020.2999354","article-title":"Deep Learning for Effective Refugee Tent Extraction Near Syria\u2013Jordan Border","volume":"18","author":"Lu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tang, X., Tu, Z., Wang, Y., Liu, M., Li, D., and Fan, X. (2022). Automatic Detection of Coseismic Landslides Using a New Transformer Method. Remote Sens., 14.","DOI":"10.3390\/rs14122884"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, P., Wei, Y., Wang, Q., Chen, Y., and Xie, J. (2020). Research on post-earthquake landslide extraction algorithm based on improved U-Net model. Remote Sens., 12.","DOI":"10.3390\/rs12050894"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kalantar, B., Ueda, N., Saeidi, V., Janizadeh, S., Shabani, F., Ahmadi, K., and Shabani, F. (2021). Deep neural network utilizing remote sensing datasets for flood hazard susceptibility mapping in Brisbane, Australia. Remote Sens., 13.","DOI":"10.3390\/rs13132638"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Naderpour, M., Rizeei, H.M., and Ramezani, F. (2021). Forest fire risk prediction: A spatial deep neural network-based framework. Remote Sens., 13.","DOI":"10.3390\/rs13132513"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Meng, Z., Li, L., Tang, X., Feng, Z., Jiao, L., and Liang, M. (2019). Multipath residual network for spectral-spatial hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11161896"},{"key":"ref_55","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"9938","DOI":"10.1073\/pnas.1301691110","article-title":"Prefrontal microcircuit underlies contextual learning after hippocampal loss","volume":"110","author":"Zelikowsky","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, L. (2019). Deep residual autoencoder with multiscaling for semantic segmentation of land-use images. Remote Sens., 11.","DOI":"10.3390\/rs11182142"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Rastiveis, H., Kalantar, B., Halin, A.A., and Ueda, N. (2022). BDD-Net: An End-to-End Multiscale Residual CNN for Earthquake-Induced Building Damage Detection. Remote Sens., 14.","DOI":"10.3390\/rs14092214"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s00500-016-2246-3","article-title":"Spectral\u2013spatial multi-feature-based deep learning for hyperspectral remote sensing image classification","volume":"21","author":"Wang","year":"2017","journal-title":"Soft Comput."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Khryashchev, V., and Larionov, R. (2020, January 11\u201313). Wildfire segmentation on satellite images using deep learning. Proceedings of the 2020 Moscow Workshop on Electronic and Networking Technologies (MWENT), Moscow, Russia.","DOI":"10.1109\/MWENT47943.2020.9067475"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Abderrahim, N.Y.Q., Abderrahim, S., and Rida, A. (2020, January 11\u201313). Road segmentation using u-net architecture. Proceedings of the 2020 IEEE International conference of Moroccan Geomatics (Morgeo), Moscow, Russia.","DOI":"10.1109\/Morgeo49228.2020.9121887"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yang, Z., Xu, C., and Li, L. (2022). Landslide Detection Based on ResU-Net with Transformer and CBAM Embedded: Two Examples with Geologically Different Environments. Remote Sens., 14.","DOI":"10.3390\/rs14122885"},{"key":"ref_63","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Kalantar, B., Ueda, N., Saeidi, V., Ahmadi, K., Halin, A.A., and Shabani, F. (2020). Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data. Remote Sens., 12.","DOI":"10.3390\/rs12111737"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"117320","DOI":"10.1016\/j.atmosenv.2020.117320","article-title":"Hybridized neural fuzzy ensembles for dust source modeling and prediction","volume":"224","author":"Rahmati","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"6166","DOI":"10.1109\/JSTARS.2020.3028855","article-title":"A new deep-learning-based approach for earthquake-triggered landslide detection from single-temporal RapidEye satellite imagery","volume":"13","author":"Yi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Kochupillai, M., Kahl, M., Schmitt, M., Taubenb\u00f6ck, H., and Zhu, X.X. (2022). Earth Observation and Artificial Intelligence: Understanding emerging ethical issues and opportunities. IEEE Geosci. Remote Sens. Mag., 2\u201336.","DOI":"10.1109\/MGRS.2022.3208357"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:06Z","timestamp":1760146986000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":67,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246382"],"URL":"https:\/\/doi.org\/10.3390\/rs14246382","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,16]]}}}