{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:13:08Z","timestamp":1776082388095,"version":"3.50.1"},"reference-count":85,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA Land Cover and Land Use Change Program","award":["80NSSC21K0295"],"award-info":[{"award-number":["80NSSC21K0295"]}]},{"name":"NASA Land Cover and Land Use Change Program","award":["AV18-CA-01"],"award-info":[{"award-number":["AV18-CA-01"]}]},{"name":"USGS\u2019s AmericaView grant to CaliforniaView","award":["80NSSC21K0295"],"award-info":[{"award-number":["80NSSC21K0295"]}]},{"name":"USGS\u2019s AmericaView grant to CaliforniaView","award":["AV18-CA-01"],"award-info":[{"award-number":["AV18-CA-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Human encroachment into wildlands has resulted in a rapid increase in wildland\u2013urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland\u2013urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction.<\/jats:p>","DOI":"10.3390\/rs14153622","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T22:43:26Z","timestamp":1659048206000},"page":"3622","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Aerial Imagery-Based Building Footprint Detection with an Integrated Deep Learning Framework: Applications for Fine Scale Wildland\u2013Urban Interface Mapping"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7478-1442","authenticated-orcid":false,"given":"Yuhan","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA"}]},{"given":"Yufang","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","first-page":"751","article-title":"Urban wildland interface communities within the vicinity of federal lands that are at high risk from wildfire","volume":"66","author":"Glickman","year":"2001","journal-title":"Fed. Regist."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Manzello, S.L., Almand, K., Guillaume, E., Vallerent, S., Hameury, S., and Hakkarainen, T. (2018). FORUM Position Paper1 The Growing Global Wildland Urban Interface (WUI) Fire Dilemma: Priority Needs for Research. Fire Saf. J., 100.","DOI":"10.1016\/j.firesaf.2018.07.003"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1890\/04-1413","article-title":"The wildland\u2013urban interface in the united states","volume":"15","author":"Radeloff","year":"2005","journal-title":"Ecol. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1071\/WF18097","article-title":"Rapid WUI growth in a natural amenity-rich region in central-western Patagonia, Argentina","volume":"28","author":"Godoy","year":"2019","journal-title":"Int. J. Wildland Fire"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s11113-005-4479-1","article-title":"Population trends in metropolitan and nonmetropolitan America: Selective deconcentration and the rural rebound","volume":"24","author":"Johnson","year":"2005","journal-title":"Popul. Res. Policy Rev."},{"key":"ref_6","unstructured":"Martinuzzi, S., Stewart, S.I., Helmers, D.P., Mockrin, M.H., Hammer, R.B., and Radeloff, V.C. (2018). The 2010 Wildland-Urban Interface of the Conterminous United States, US Department of Agriculture, Forest Service, Northern Research Station. Available online: https:\/\/www.fs.fed.us\/nrs\/pubs\/rmap\/rmap_nrs8.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3314","DOI":"10.1073\/pnas.1718850115","article-title":"Rapid growth of the US wildland-urban interface raises wildfire risk","volume":"115","author":"Radeloff","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1071\/WF13136","article-title":"Controls on the spatial pattern of wildfire ignitions in Southern California","volume":"23","author":"Faivre","year":"2014","journal-title":"Int. J. Wildland Fire"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Manzello, S.L. (2020). Wildfires and WUI fire fatalities. Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, Springer.","DOI":"10.1007\/978-3-319-52090-2"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s11069-020-04197-0","article-title":"Wildfire impacts on schools and hospitals following the 2018 California Camp Fire","volume":"104","author":"Schulze","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.jenvman.2013.06.021","article-title":"Using structure locations as a basis for mapping the wildland urban interface","volume":"128","author":"Stewart","year":"2013","journal-title":"J. Environ. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1071\/WF16221","article-title":"Mapping Canadian wildland fire interface areas","volume":"27","author":"Johnston","year":"2017","journal-title":"Int. J. Wildland Fire"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nguyen, M.H., Block, J., Crawl, D., Siu, V., Bhatnagar, A., Rodriguez, F., Kwan, A., Baru, N., and Altintas, I. (2018, January 10\u201313). Land cover classification at the wildland urban interface using high-resolution satellite imagery and deep learning. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8621883"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"094069","DOI":"10.1088\/1748-9326\/ab9be5","article-title":"Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfires","volume":"15","author":"Miranda","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_15","first-page":"201","article-title":"Defining the wildland\u2013urban interface","volume":"105","author":"Stewart","year":"2007","journal-title":"J. For."},{"key":"ref_16","unstructured":"U.S. Census Bureau (2022, May 06). Available online: https:\/\/www2.census.gov\/geo\/tiger\/TIGER2021\/TRACT\/."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.compenvurbsys.2015.02.003","article-title":"Geospatial approach for defining the Wildland-Urban Interface in the Alpine environment","volume":"52","author":"Conedera","year":"2015","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1080\/2150704X.2016.1235299","article-title":"SatCNN: Satellite image dataset classification using agile convolutional neural networks","volume":"8","author":"Zhong","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s12145-019-00383-2","article-title":"Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation","volume":"12","author":"Chen","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bischke, B., Helber, P., Folz, J., Borth, D., and Dengel, A. (2019, January 22\u201325). Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803050"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1080\/10106049.2020.1778100","article-title":"Automatic building footprint extraction from very high-resolution imagery using deep learning techniques","volume":"37","author":"Rastogi","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10091","DOI":"10.1109\/JSTARS.2021.3109237","article-title":"Scale-Robust Deep-Supervision Network for Mapping Building Footprints From High-Resolution Remote Sensing Images","volume":"14","author":"Guo","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Touzani, S., and Granderson, J. (2021). Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints. Remote Sens., 13.","DOI":"10.3390\/rs13132578"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ekim, B., and Sertel, E. (2021, January 11\u201316). A Multi-Task Deep Learning Framework for Building Footprint Segmentation. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554766"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, T., and Yang, L. (October, January 26). A Fully Automatic Method for Rapidly Mapping Impacted Area by Natural Disaster. Proceedings of the InIGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323634"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112308","DOI":"10.1016\/j.rse.2021.112308","article-title":"Change detection using deep learning approach with object-based image analysis","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pepe, M., Costantino, D., Alfio, V.S., Vozza, G., and Cartellino, E. (2021). A Novel Method Based on Deep Learning, GIS and Geomatics Software for Building a 3D City Model from VHR Satellite Stereo Imagery. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10100697"},{"key":"ref_28","first-page":"359","article-title":"DEEP LEARNING FOR 3D BUILDING RECONSTRUCTION: A REVIEW","volume":"XLIII-B2-2","author":"Buyukdemircioglu","year":"2022","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","unstructured":"Microsoft U.S. (2021, October 04). Building Footprints. Available online: https:\/\/github.com\/microsoft\/USBuildingFootprints."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1080\/01431161.2020.1804090","article-title":"Two novel benchmark datasets from ArcGIS and bing world imagery for remote sensing image retrieval","volume":"42","author":"Hou","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","first-page":"1","article-title":"Mapping the wildland-urban interface in California using remote sensing data","volume":"12","author":"Li","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","article-title":"One Pixel Attack for Fooling Deep Neural Networks","volume":"23","author":"Su","year":"2019","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dechesne, C., Lassalle, P., and Lef\u00e8vre, S. (2021). Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images. Remote Sens., 13.","DOI":"10.3390\/rs13193836"},{"key":"ref_34","unstructured":"(2021, November 10). NAIP Information Sheet, Available online: https:\/\/www.fsa.usda.gov\/Internet\/FSA_File\/naip_info_sheet_2015.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.compenvurbsys.2007.10.001","article-title":"Classification of the wildland\u2013urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography","volume":"32","author":"Cleve","year":"2008","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1080\/13658816.2019.1624761","article-title":"A locally-constrained YOLO framework for detecting small and densely-distributed building footprints","volume":"34","author":"Xie","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2600","DOI":"10.1109\/JSTARS.2018.2835377","article-title":"Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States","volume":"11","author":"Yang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kusz, M., Peters, J., Huber, L., Davis, J., and Michael, S. (2021, January 17). Building Detection with Deep Learning. Proceedings of the Practice and Experience in Advanced Research Computing, Portland, OR, USA.","DOI":"10.1145\/3437359.3465573"},{"key":"ref_39","unstructured":"Yu, K., Frank, H., and Wilson, D. (2021). Points 2 Polygons: Context-Based Segmentation from Weak Labels Using Adversarial Networks. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.landurbplan.2007.06.002","article-title":"Expansion of the US wildland\u2013urban interface","volume":"83","author":"Theobald","year":"2007","journal-title":"Landsc. Urban Plan."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1071\/WF18108","article-title":"High wildfire damage in interface communities in California","volume":"28","author":"Kramer","year":"2019","journal-title":"Int. J. Wildland Fire"},{"key":"ref_42","unstructured":"(2022, April 15). Napa County Building Footprints, Available online: http:\/\/gis.napa.ca.gov\/giscatalog\/catalog_xml.asp."},{"key":"ref_43","unstructured":"(2022, April 15). Shasta County Building Footprints. Available online: https:\/\/data-shasta.opendata.arcgis.com\/datasets\/Shasta:buildingfootprints\/about."},{"key":"ref_44","unstructured":"(2022, April 15). San Luis Obispo County Building Footprints, Available online: https:\/\/opendata.slocounty.ca.gov\/datasets\/building-footprints\/explore."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, D.-Y., Peng, L., Li, W.-C., and Wang, Y.-D. (2021). Building Extraction and Number Statistics in WUI Areas Based on UNet Structure and Ensemble Learning. Remote Sens., 13.","DOI":"10.3390\/rs13061172"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.inffus.2017.10.007","article-title":"Deep learning for pixel-level image fusion: Recent advances and future prospects","volume":"42","author":"Liu","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1177\/0040517520928604","article-title":"Mobile-Unet: An efficient convolutional neural network for fabric defect detection","volume":"92","author":"Jing","year":"2022","journal-title":"Text. Res. J."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical image computing and computer-assisted intervention, Cham, Switzerland.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1002\/rse2.111","article-title":"Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images","volume":"5","author":"Wagner","year":"2019","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"7209","DOI":"10.1109\/TGRS.2019.2912301","article-title":"Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ivanovsky, L., Khryashchev, V., Pavlov, V., and Ostrovskaya, A. (2019, January 8). Building detection on aerial images using U-NET neural networks. Proceedings of the 2019 24th Conference of Open Innovations Association (FRUCT), Moscow, Russia.","DOI":"10.23919\/FRUCT.2019.8711930"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_57","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative Adversarial Networks: An Overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_59","first-page":"2","article-title":"Conditional generative adversarial nets for convolutional face generation","volume":"2014","author":"Gauthier","year":"2014","journal-title":"Winter Semester"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_61","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Li, C., and Wand, M. (2016, January 11\u201314). Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"ref_63","unstructured":"Demir, U., and Unal, G. (2018). Patch-based image inpainting with generative adversarial networks. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1007\/s10916-009-9316-3","article-title":"A Wavelet-Based Mammographic Image Denoising and Enhancement with Homomorphic Filtering","volume":"34","author":"Gorgel","year":"2009","journal-title":"J. Med. Syst."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Bayer, T. (2009). Automated Building Simplification Using a Recursive Approach. Cartography in Central and Eastern Europe, Springer.","DOI":"10.1007\/978-3-642-03294-3_8"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.isprsjprs.2020.07.011","article-title":"A novel deep learning instance segmentation model for automated marine oil spill detection","volume":"167","author":"Yekeen","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Goutte, C., and Gaussier, E. (2005, January 21). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. Proceedings of the European Conference on Information Retrieval, Santiago de Compostela, Spain.","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Haunert, J.H., and Wolff, A. (2010, January 2). Optimal and topologically safe simplification of building footprints. Proceedings of the 18th Sigspatial International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869819"},{"key":"ref_70","unstructured":"Guercke, R., and Sester, M. (2011, January 1). Building footprint simplification based on hough transform and least squares adjustment. Proceedings of the 14th Workshop of the ICA commission on Generalisation and Multiple Representation, Paris, France."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.isprsjprs.2020.02.019","article-title":"Conterminous United States land cover change patterns 2001\u20132016 from the 2016 National Land Cover Database","volume":"162","author":"Homer","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_72","unstructured":"(2022, May 06). Multi-Resolution Land Characteristics Consortium Data, Available online: https:\/\/www.mrlc.gov\/data."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1080\/08941920.2015.1014596","article-title":"Adapting to Wildfire: Rebuilding After Home Loss","volume":"28","author":"Mockrin","year":"2015","journal-title":"Soc. Nat. Resour."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"105502","DOI":"10.1016\/j.landusepol.2021.105502","article-title":"Post-wildfire rebuilding and new development in California indicates minimal adaptation to fire risk","volume":"107","author":"Kramer","year":"2021","journal-title":"Land Use Policy"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1177\/1532673X211023926","article-title":"Baptism by Wildfire? Wildfire Experiences and Public Support for Wildfire Adaptation Policies","volume":"50","author":"Hui","year":"2022","journal-title":"Am. Politics Res."},{"key":"ref_76","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_77","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (June, January 27). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ueki, W., Nishii, T., Umehara, K., Ota, J., Higuchi, S., Ohta, Y., Nagai, Y., Murakawa, K., Ishida, T., and Fukuda, T. (2022). Generative adversarial network-based post-processed image super-resolution technology for accelerating brain MRI: Comparison with compressed sensing. Acta Radiol.","DOI":"10.1177\/02841851221076330"},{"key":"ref_79","unstructured":"Khalel, A., and El-Saban, M. (2018). Automatic pixelwise object labeling for aerial imagery using stacked u-nets. arXiv."},{"key":"ref_80","unstructured":"Van Hoorick, B. (2019). Image outpainting and harmonization using generative adversarial networks. arXiv."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.jvcir.2018.11.020","article-title":"A novel framework for semantic segmentation with generative adversarial network","volume":"58","author":"Zhu","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e00174","DOI":"10.1016\/j.heliyon.2016.e00174","article-title":"High resolution mapping of development in the wildland-urban interface using object based image extraction","volume":"2","author":"Caggiano","year":"2016","journal-title":"Heliyon"},{"key":"ref_83","unstructured":"Caggiano, M. (2020). Mapping Values at Risk, Assessing Building Loss and Evaluating Stakeholder Expectations of Wildfire Mitigation in the Wildland-Urban Interface. [Ph.D. Thesis, Colorado State University]."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Yang, H.L., Lunga, D., and Yuan, J. (2017, January 23\u201328). Toward country scale building detection with convolutional neural network using aerial images. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127091"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"e2597","DOI":"10.1002\/eap.2597","article-title":"The wildland\u2013urban interface in the United States based on 125 million building locations","volume":"32","author":"Carlson","year":"2022","journal-title":"Ecol. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3622\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:58:32Z","timestamp":1760140712000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3622"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,28]]},"references-count":85,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153622"],"URL":"https:\/\/doi.org\/10.3390\/rs14153622","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,28]]}}}