{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:59:14Z","timestamp":1780491554510,"version":"3.54.1"},"reference-count":85,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Earth-observation-based mapping plays a critical role in humanitarian responses by providing timely and accurate information in inaccessible areas, or in situations where frequent updates and monitoring are required, such as in internally displaced population (IDP)\/refugee settlements. Manual information extraction pipelines are slow and resource inefficient. Advances in deep learning, especially convolutional neural networks (CNNs), are providing state-of-the-art possibilities for automation in information extraction. This study investigates a deep convolutional neural network-based Mask R-CNN model for dwelling extractions in IDP\/refugee settlements. The study uses a time series of very high-resolution satellite images from WorldView-2 and WorldView-3. The model was trained with transfer learning through domain adaptation from nonremote sensing tasks. The capability of a model trained on historical images to detect dwelling features on completely unseen newly obtained images through temporal transfer was investigated. The results show that transfer learning provides better performance than training the model from scratch, with an MIoU range of 4.5 to 15.3%, and a range of 18.6 to 25.6% for the overall quality of the extracted dwellings, which varied on the bases of the source of the pretrained weight and the input image. Once it was trained on historical images, the model achieved 62.9, 89.3, and 77% for the object-based mean intersection over union (MIoU), completeness, and quality metrics, respectively, on completely unseen images.<\/jats:p>","DOI":"10.3390\/rs14030689","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T22:16:18Z","timestamp":1643753778000},"page":"689","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Mapping of Dwellings in IDP\/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9442-8918","authenticated-orcid":false,"given":"Getachew Workineh","family":"Gella","sequence":"first","affiliation":[{"name":"Christian Doppler Laboratory for Geospatial and EO-Based Humanitarian Technologies (GEOHUM), Department of Geoinformatics\u2014Z_GIS, Paris Lodron University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenz","family":"Wendt","sequence":"additional","affiliation":[{"name":"Christian Doppler Laboratory for Geospatial and EO-Based Humanitarian Technologies (GEOHUM), Department of Geoinformatics\u2014Z_GIS, Paris Lodron University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Lang","sequence":"additional","affiliation":[{"name":"Christian Doppler Laboratory for Geospatial and EO-Based Humanitarian Technologies (GEOHUM), Department of Geoinformatics\u2014Z_GIS, Paris Lodron University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, Paris Lodron University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7078-3766","authenticated-orcid":false,"given":"Barbara","family":"Hofer","sequence":"additional","affiliation":[{"name":"Christian Doppler Laboratory for Geospatial and EO-Based Humanitarian Technologies (GEOHUM), Department of Geoinformatics\u2014Z_GIS, Paris Lodron University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunya","family":"Gao","sequence":"additional","affiliation":[{"name":"Christian Doppler Laboratory for Geospatial and EO-Based Humanitarian Technologies (GEOHUM), Department of Geoinformatics\u2014Z_GIS, Paris Lodron University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8630-1389","authenticated-orcid":false,"given":"Andreas","family":"Braun","sequence":"additional","affiliation":[{"name":"Christian Doppler Laboratory for Geospatial and EO-Based Humanitarian Technologies (GEOHUM), Department of Geoinformatics\u2014Z_GIS, Paris Lodron University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria"},{"name":"Department of Geography, University of Tubingen, R\u00fcmelinstr. 19-23, D-72070 T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,1]]},"reference":[{"key":"ref_1","unstructured":"(2021, June 15). UNHCR Refugee Data Finder. Available online: https:\/\/www.unhcr.org\/refugee-statistics\/."},{"key":"ref_2","unstructured":"Weng, Q. (2014). Mapping and monitoring of refugees and internally displaced people using EO data. Global Urban Monitoring and Assessment: Through Earth Observation, CRC Press Taylor & Francis Group."},{"key":"ref_3","unstructured":"Altan, O., Backhaus, R., Boccardo, P., and Zlatanova, S. (2010). Monitoring Refugee\/IDP camps to Support International Relief Action. Geoinformation for Disaster and Risk Management\u2013Examples and Best Practices, Joint Board of Geospatial Information Societies (JB GIS)."},{"key":"ref_4","first-page":"87","article-title":"Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery","volume":"36","author":"Wang","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"4438","DOI":"10.1109\/JSTARS.2019.2950970","article-title":"Multifractality in Humanitarian Applications: A Case Study of Internally Displaced Persons\/Refugee Camps","volume":"12","author":"Jenerowicz","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.proeng.2016.08.134","article-title":"Infrastructure evolution analysis via remote sensing in an urban refugee camp\u2013Evidence from Za\u2019atari","volume":"159","author":"Tomaszewski","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.9734\/JGEESI\/2016\/22392","article-title":"Impact of Refugee Camps on Their Environment A Case Study Using Multi-Temporal SAR Data","volume":"4","author":"Braun","year":"2016","journal-title":"J. Geogr. Environ. Earth Sci. Int."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Braun, A., Fakhri, F., and Hochschild, V. (2019). Refugee camp monitoring and environmental change assessment of Kutupalong, Bangladesh, based on radar imagery of Sentinel-1 and ALOS-2. Remote Sens., 11.","DOI":"10.3390\/rs11172047"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apgeog.2018.01.013","article-title":"Vegetation monitoring in refugee-hosting areas in South Sudan","volume":"93","author":"Leiterer","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2012.08.010","article-title":"Integrated assessment of the environmental impact of an IDP camp in Sudan based on very high resolution multi-temporal satellite imagery","volume":"126","author":"Hagenlocher","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Benz, S., Park, H., Li, J., Crawl, D., Block, J., Nguyen, M., and Altintas, I. (2019, January 24\u201327). Understanding a rapidly expanding refugee camp using convolutional neural networks and satellite imagery. Proceedings of the IEEE 15th International Conference on eScience (eScience), San Diego, CA, USA.","DOI":"10.1109\/eScience.2019.00034"},{"key":"ref_13","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_14","first-page":"327","article-title":"Modeling ephemeral settlements using VHSR image data and 3D visualization\u2013The example of Goz Amer refugee camp in Chad","volume":"4","author":"Lang","year":"2006","journal-title":"PFG-Photogramm. Fernerkundung, Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1127\/1432-8364\/2013\/0169","article-title":"Automated analysis of satellite imagery to provide information products for humanitarian relief operations in refugee camps -from scientific development towards operational services","volume":"2013","author":"Tiede","year":"2013","journal-title":"Photogramm. Fernerkundung Geoinf."},{"key":"ref_16","first-page":"175","article-title":"Machine learning for predicting landslide risk of rohingya refugee camp infrastructure","volume":"4","author":"Ahmed","year":"2020","journal-title":"J. Inf. Telecommun."},{"key":"ref_17","unstructured":"Hadzic, A., Christie, G., Freeman, J., Dismer, A., Bullard, S., Greiner, A., Jacobs, N., and Mukherjee, R. (October, January 26). Estimating Displaced Populations from Overhead. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Waikoloa, HI, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1142\/S2196888819500246","article-title":"Artificial Neural Network and Machine Learning Based Methods for Population Estimation of Rohingya Refugees: Comparing Data-Driven and Satellite Image-Driven Approaches","volume":"6","author":"Ahmed","year":"2019","journal-title":"Vietnam J. Comput. Sci."},{"key":"ref_19","unstructured":"Green, B., and Blanford, J.I. (2002, January 10). Refugee camp population estimates using automated feature extraction. Proceedings of the Annual Hawaii International Conference on System Sciences, Big Island, HI, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9277","DOI":"10.3390\/rs6109277","article-title":"Earth observation-based dwelling detection approaches in a highly complex refugee camp environment\u2013A comparative study","volume":"6","author":"Tiede","year":"2014","journal-title":"Remote Sens."},{"key":"ref_21","unstructured":"Kahraman, F., Ates, H.F., and Kucur Ergunay, S.S. (2013, January 27\u201330). Automated Detection Of Refugee\/IDP TENTS FROM Satellite Imagery Using Two- Level Graph Cut Segmentation. Proceedings of the CaGIS\/ASPRS Fall Conference, San Antonio, TX, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1109\/JSTARS.2017.2664982","article-title":"Object-Based Analysis and Fusion of Optical and SAR Satellite Data for Dwelling Detection in Refugee Camps","volume":"10","author":"Sprohnle","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","unstructured":"Addink, E.A., and van Coillie, F.M.B. (July, January 29). Transferability of obia rulesets for idp camp analysis in darfur. Proceedings of the GEOBIA 2010: Geographic Object-Based Image Analysis, Ghent, Belgium."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.rse.2018.02.025","article-title":"Multi-sensor feature fusion for very high spatial resolution built-up area extraction in temporary settlements","volume":"209","author":"Schoepfer","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"(2021, May 15). UNHCR Bangladesh Refugee Emergency: Population Factsheet (as of 30 September 2019). Available online: https:\/\/reliefweb.int\/sites\/reliefweb.int\/files\/resources\/68229.pdf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamar\u00eda, J., Fadhel, M.A., Al-Amidie, M., and Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data, 8.","DOI":"10.1186\/s40537-021-00444-8"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lu, Z., Xu, T., Liu, K., Liu, Z., Zhou, F., and Liu, Q. (2019, January 4\u20136). 5M-Building: A large-scale high-resolution building dataset with CNN based detection analysis. Proceedings of the International Conference on Tools with Artificial Intelligence, ICTAI, Portland, OR, USA.","DOI":"10.1109\/ICTAI.2019.00194"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Pasquali, G., Iannelli, G.C., and Dell\u2019Acqua, F. (2019). Building footprint extraction from multispectral, spaceborne earth observation datasets using a structurally optimized U-Net convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11232803"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6169","DOI":"10.1109\/TGRS.2020.3026051","article-title":"MAP-Net: Multiple Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery","volume":"59","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","unstructured":"Sirko, W., Kashubin, S., Ritter, M., Annkah, A., Bouchareb, Y.S.E., Dauphin, Y., Keysers, D., Neumann, M., Cisse, M., and Quinn, J. (2021). Continental-Scale Building Detection from High Resolution Satellite Imagery. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tiecke, T.G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., Kilic, T., Murray, S., Blankespoor, B., and Prydz, E.B. (2017). Mapping the world population one building at a time. arXiv.","DOI":"10.1596\/33700"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Van Den Hoek, J., Friedrich, H.K., Ballasiotes, A., Peters, L.E.R., and Wrathall, D. (2021). Development after displacement: Evaluating the utility of openstreetmap data for monitoring sustainable development goal progress in refugee settlements. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030153"},{"key":"ref_35","unstructured":"(2021, March 20). UNHCR SITE MAP Minawao\/Gawar Cameroon. Available online: https:\/\/im.unhcr.org\/apps\/campmapping\/?site=CMRs004589."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Tiede, D., Wendt, L., Sudmanns, M., and Lang, S. (2021). Transferable instance segmentation of dwellings in a refugee camp-integrating CNN and OBIA. Eur. J. Remote Sens.","DOI":"10.1080\/22797254.2020.1759456"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/LGRS.2020.2999354","article-title":"Deep Learning for Effective Refugee Tent Extraction near Syria-Jordan Border","volume":"18","author":"Lu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Quinn, J.A., Nyhan, M.M., Navarro, C., Coluccia, D., Bromley, L., and Luengo-Oroz, M. (2018). Humanitarian applications of machine learning with remote-sensing data: Review and case study in refugee settlement mapping. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 376.","DOI":"10.1098\/rsta.2017.0363"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"161","DOI":"10.5194\/isprs-archives-XLII-1-161-2018","article-title":"Dwelling extraction in refugee camps using CNN-First experiences and lessons learnt","volume":"42","author":"Ghorbanzadeh","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-ISPRS Arch."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1109\/TKDE.2016.2554549","article-title":"Deep learning of transferable representation for scalable domain adaptation","volume":"28","author":"Long","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_41","unstructured":"(2021, April 12). UNHCR Cameroun\u2013Extr\u00eame Nord Profil de la Population Refugi\u00e9e du Camp de Minawao Cameroun\u2013Extr\u00eame Nord Profil de la Population Refugi\u00e9e du Camp de Minawao. Available online: https:\/\/www.refworld.org\/pdfid\/553f384e4.pdf."},{"key":"ref_42","unstructured":"(2016). UNHCR Shelter Design Catalgue, Shelter and Settlement Section Division of Programme Support and Management United Nations High Commissioner for Refugees."},{"key":"ref_43","unstructured":"(2021, April 03). HHI Satellite Imagery Interpretation Guide Intentional Burning of Tukuls. Available online: https:\/\/hhi.harvard.edu\/files\/humanitarianinitiative\/files\/siig_ii_burned_tukuls_3.pdf?m=1610658910."},{"key":"ref_44","unstructured":"(2021, February 12). World Bank Climate Change Knowledge Portal: For Developmet Practionars and Policy Makers. Available online: https:\/\/climateknowledgeportal.worldbank.org\/country\/cameroon\/climate-data-historical."},{"key":"ref_45","unstructured":"(2021, March 17). Digital Globe DigitalGlobe Core Imagery Products Guide. Available online: https:\/\/www.geosoluciones.cl\/documentos\/worldview\/DigitalGlobe-Core-Imagery-Products-Guide.pdf."},{"key":"ref_46","unstructured":"Laben, C.A., and Brower, B.V. (2000). Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. (6,011,875), U.S. Patent."},{"key":"ref_47","unstructured":"(2021, September 15). USGS Surface Radar Topographic Mission (SRTM) Digital Elevation Model, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e190015","DOI":"10.1148\/ryai.2019190015","article-title":"The Effect of Image Resolution on Deep Learning in Radiography","volume":"2","author":"Sabottke","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_49","unstructured":"Rukundo, O. (2021, March 25). Effects of Image Size on Deep Learning. Available online: http:\/\/arxiv.org\/abs\/2101.11508."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Pawluszek-Filipiak, K., and Borkowski, A. (2020). On the importance of train-test split ratio of datasets in automatic landslide detection by supervised classification. Remote Sens., 12.","DOI":"10.3390\/rs12183054"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Nguyen, Q.H., Ly, H.B., Ho, L.S., Al-Ansari, N., Van Le, H., Tran, V.Q., Prakash, I., and Pham, B.T. (2021). Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Math. Probl. Eng., 2021.","DOI":"10.1155\/2021\/4832864"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"125229","DOI":"10.1109\/ACCESS.2021.3110745","article-title":"Algorithmic Splitting: A Method for Dataset Preparation","volume":"9","author":"Kahloot","year":"2021","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s41664-018-0068-2","article-title":"On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning","volume":"2","author":"Xu","year":"2018","journal-title":"J. Anal. Test."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., and Doll\u00e1r, P. (2015). Microsoft COCO: Common Objects in Context. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_56","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_57","unstructured":"Francois, C., Joe, Y.-H., and De Thibault Main, B. (2021, January 01). Keras Code and Weights Files for Popular Deep Learning Models. Available online: https:\/\/github.com\/fchollet\/deep-learning-models."},{"key":"ref_58","unstructured":"Waleed, A. (2021, January 07). Mask R-CNN for Object Detection and Instance Segmentation on Keras and TensorFlow. Available online: https:\/\/github.com\/matterport\/Mask_RCNN."},{"key":"ref_59","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (October, January 22). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 13\u201316). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2021, January 21). SSD: Single Shot Multibox Detector. Available online: https:\/\/arxiv.org\/abs\/1512.02325.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_64","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hao, S., Zhou, Y., and Guo, Y. (2020). A Brief Survey on Semantic Segmentation with Deep Learning. Neurocomputing.","DOI":"10.1016\/j.neucom.2019.11.118"},{"key":"ref_66","first-page":"397","article-title":"Semantic Segmentation for Remote Sensing based on RGB Images and Lidar Data using Model-Agnostic Meta-Learning and Partical Swarm Optimization","volume":"53","author":"Zhang","year":"2020","journal-title":"IFAC-Pap."},{"key":"ref_67","unstructured":"Ruder, S. (2021, January 03). An Overview of Gradient Descent Optimization Algorithms. Available online: http:\/\/arxiv.org\/abs\/1609.04747."},{"key":"ref_68","unstructured":"Soekhoe, D., Van der Putten, P., and Plaat, A. (2007, January 8\u201311). On the impact of data set size in transfer learning using deep neural networks. Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Redondo Beach, CA, USA."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ghafoorian, M., Mehrtash, A., Kapur, T., Karssemeijer, N., Marchiori, E., Pesteie, M., Guttmann, C.R.G., de Leeuw, F.E., Tempany, C.M., and van Ginneken, B. (2017, January 11\u201313). Transfer learning for domain adaptation in MRI: Application in brain lesion segmentation. Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 20th International Conference, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66179-7_59"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., and Davis, L.S. (2017, January 22\u201329). Soft-NMS\u2013Improving Object Detection with One Line of Code. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.593"},{"key":"ref_71","unstructured":"Sebastien, O. (2021, April 27). Building Segmentation on Satellite Images. Available online: http:\/\/arxiv.org\/abs\/1703.06870."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Zhao, K., Kang, J., Jung, J., and Sohn, G. (2018, January 18\u201322). Building extraction from satellite images using mask R-CNN with building boundary regularization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00045"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Guyon, I., Bennett, K., Cawley, G., Escalante, H.J., Escalera, S., Ho, T.K., Maci\u00e0, N., Ray, B., Saeed, M., and Statnikov, A. (2015, January 12\u201317). Design of the 2015 ChaLearn AutoML challenge. Proceedings of the International Joint Conference on Neural Networks, Killarney, Ireland.","DOI":"10.1109\/IJCNN.2015.7280767"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Taha, A.A., and Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Med. Imaging, 15.","DOI":"10.1186\/s12880-015-0068-x"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Vakalopoulou, M., Karantzalos, K., Komodakis, N., and Paragios, N. (2015, January 26\u201331). Building detection in very high resolution multispectral data with deep learning features. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326158"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Hui, Z., Li, Z., Cheng, P., Ziggah, Y.Y., and Fan, J. (2021). Building extraction from airborne lidar data based on multi-constraints graph segmentation. Remote Sens., 13.","DOI":"10.3390\/rs13183766"},{"key":"ref_77","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, Savannah, GA, USA."},{"key":"ref_78","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8\u201313). How transferable are features in deep neural networks?. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Wen, Y., Chen, L., Deng, Y., and Zhou, C. (2021). Rethinking pre-training on medical imaging. J. Vis. Commun. Image Represent., 78.","DOI":"10.1016\/j.jvcir.2021.103145"},{"key":"ref_80","unstructured":"He, K., Girshick, R., and Dollar, P. (November, January 27). Rethinking imageNet pre-training. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_81","first-page":"242","article-title":"Building extraction from satellite images using mask R-CNN with building boundary regularization","volume":"2018","author":"Zhao","year":"2018","journal-title":"IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Hu, Y., and Guo, F. (2019, January 22\u201324). Building Extraction Using Mask Scoring R-CNN Network. Proceedings of the 3rd International Conference on Computer Science and Application Engineering, Sanya, China.","DOI":"10.1145\/3331453.3361644"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Wen, Q., Jiang, K., Wang, W., Liu, Q., Guo, Q., Li, L., and Wang, P. (2019). Automatic building extraction from google earth images under complex backgrounds based on deep instance segmentation network. Sensors, 19.","DOI":"10.3390\/s19020333"},{"key":"ref_84","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":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Xu, B., Wang, W., Falzon, G., Kwan, P., Guo, L., Chen, G., Tait, A., and Schneider, D. (2020). Automated cattle counting using Mask R-CNN in quadcopter vision system. Comput. Electron. Agric., 171.","DOI":"10.1016\/j.compag.2020.105300"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/689\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:12:26Z","timestamp":1760134346000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/689"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,1]]},"references-count":85,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030689"],"URL":"https:\/\/doi.org\/10.3390\/rs14030689","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,1]]}}}