{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T20:39:04Z","timestamp":1769200744380,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006959","name":"US Forest Service","doi-asserted-by":"publisher","award":["GR40533"],"award-info":[{"award-number":["GR40533"]}],"id":[{"id":"10.13039\/100006959","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We describe the production of maps of buildings on Hawai\u2019i Island, based on complementary information contained in two different types of remote sensing data. The maps cover 3200 km2 over a highly varied set of landscape types and building densities. A convolutional neural network was first trained to identify building candidates in LiDAR data. To better differentiate between true buildings and false positives, the CNN-based building probability map was then used, together with 400\u20132400 nm imaging spectroscopy, as input to a gradient boosting model. Simple vector operations were then employed to further refine the final maps. This stepwise approach resulted in detection of 84%, 100%, and 97% of manually labeled buildings, at the 0.25, 0.5, and 0.75 percentiles of true building size, respectively, with very few false positives. The median absolute error in modeled building areas was 15%. This novel integration of deep learning, machine learning, and multi-modal remote sensing data was thus effective in detecting buildings over large scales and diverse landscapes, with potential applications in urban planning, resource management, and disaster response. The adaptable method presented here expands the range of techniques available for object detection in multi-modal remote sensing data and can be tailored to various kinds of input data, landscape types, and mapping goals.<\/jats:p>","DOI":"10.3390\/rs15184389","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T10:23:42Z","timestamp":1693995822000},"page":"4389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mapping Buildings across Heterogeneous Landscapes: Machine Learning and Deep Learning Applied to Multi-Modal Remote Sensing Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4805-0990","authenticated-orcid":false,"given":"Rachel E.","family":"Mason","sequence":"first","affiliation":[{"name":"Center for Global Discovery and Conservation Science, 60 Nowelo Street, Hilo, HI 96720, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0428-2909","authenticated-orcid":false,"given":"Nicholas R.","family":"Vaughn","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, 60 Nowelo Street, Hilo, HI 96720, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7893-6421","authenticated-orcid":false,"given":"Gregory P.","family":"Asner","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, 60 Nowelo Street, Hilo, HI 96720, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"ref_1","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_2","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_3","doi-asserted-by":"crossref","unstructured":"Laverdiere, M., Yang, L., Tuttle, M., and Vaughan, C. (October, January 26). Rapid Structure Detection in Support of Disaster Response: A Case Study of the 2018 Kilauea Volcano Eruption. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324160"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1890\/05-5221","article-title":"Effects of Exurban Development on Biodiversity: Patterns, Mechanisms, and Research Needs","volume":"15","author":"Hansen","year":"2005","journal-title":"Ecol. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100947","DOI":"10.1016\/j.ejrh.2021.100947","article-title":"Identifying Locations of Sewage Pollution within a Hawaiian Watershed for Coastal Water Quality Management Actions","volume":"38","author":"Wiegner","year":"2021","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.marpolbul.2016.01.002","article-title":"Linking Sewage Pollution and Water Quality to Spatial Patterns of Porites Lobata Growth Anomalies in Puako, Hawaii","volume":"104","author":"Yoshioka","year":"2016","journal-title":"Mar. Pollut. Bull."},{"key":"ref_7","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_8","unstructured":"(2022, December 16). Microsoft USBuildingFootprints. Available online: https:\/\/github.com\/microsoft\/USBuildingFootprints."},{"key":"ref_9","unstructured":"(2022, October 17). Microsoft GlobalMLBuildingFootprints. Available online: https:\/\/github.com\/microsoft\/GlobalMLBuildingFootprints."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kuras, A., Brell, M., Rizzi, J., and Burud, I. (2021). Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review. Remote Sens., 13.","DOI":"10.3390\/rs13173393"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.aei.2018.04.002","article-title":"Automated Residential Building Detection from Airborne LiDAR Data with Deep Neural Networks","volume":"36","author":"Zhou","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/LGRS.2018.2867736","article-title":"Building Extraction from LiDAR Data Applying Deep Convolutional Neural Networks","volume":"16","author":"Maltezos","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s40537-020-00374-x","article-title":"Automatic LIDAR Building Segmentation Based on DGCNN and Euclidean Clustering","volume":"7","author":"Gamal","year":"2020","journal-title":"J. Big Data"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"114417","DOI":"10.1016\/j.eswa.2020.114417","article-title":"A Review of Deep Learning Methods for Semantic Segmentation of Remote Sensing Imagery","volume":"169","author":"Yuan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"140301","DOI":"10.1007\/s11432-022-3588-0","article-title":"From Single- to Multi-Modal Remote Sensing Imagery Interpretation: A Survey and Taxonomy","volume":"66","author":"Sun","year":"2023","journal-title":"Sci. China Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Morchhale, S., Pauca, V.P., Plemmons, R.J., and Torgersen, T.C. (2016, January 21\u201324). Classification of Pixel-Level Fused Hyperspectral and Lidar Data Using Deep Convolutional Neural Networks. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071715"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2016.2616418","article-title":"Advanced Spectral Classifiers for Hyperspectral Images: A Review","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2019.02.019","article-title":"Automatic Building Extraction from High-Resolution Aerial Images and LiDAR Data Using Gated Residual Refinement Network","volume":"151","author":"Huang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.isprsjprs.2021.12.007","article-title":"CMGFNet: A Deep Cross-Modal Gated Fusion Network for Building Extraction from Very High-Resolution Remote Sensing Images","volume":"184","author":"Hosseinpour","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Feng, Q., Zhu, D., Yang, J., and Li, B. (2019). Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping Based on a Modified Two-Branch Convolutional Neural Network. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010028"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7179477","DOI":"10.1155\/2022\/7179477","article-title":"Land Resource Use Classification Using Deep Learning in Ecological Remote Sensing Images","volume":"2022","author":"Xia","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Niemann, K.O., Frazer, G., Loos, R., and Visintini, F. (2009, January 26\u201328). LiDAR-Guided Analysis of Airborne Hyperspectral Data. Proceedings of the 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Grenoble, France.","DOI":"10.1109\/WHISPERS.2009.5289029"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.rse.2012.06.012","article-title":"Carnegie Airborne Observatory-2: Increasing Science Data Dimensionality via High-Fidelity Multi-Sensor Fusion","volume":"124","author":"Asner","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"Butler, H., Bell, A., Gerlek, M.P., Gadomski, P., Manning, C., \u0141oskot, M., Ramsey, P., Couwenberg, B., and Chaulet, N. (2023, May 30). PDAL\/PDAL: 2.0.1. Available online: https:\/\/zenodo.org\/record\/3375526."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113143","DOI":"10.1016\/j.marpolbul.2021.113143","article-title":"Spatial Distribution and Sources of Nutrients at Two Coastal Developments in South Kohala, Hawai\u2019i","volume":"174","author":"Panelo","year":"2022","journal-title":"Mar. Pollut. Bull."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"114662","DOI":"10.1016\/j.marpolbul.2023.114662","article-title":"Detection and Impact of Sewage Pollution on South Kohala\u2019s Coral Reefs, Hawai\u2018I","volume":"188","author":"Aguiar","year":"2023","journal-title":"Mar. Pollut. Bull."},{"key":"ref_28","unstructured":"Brodrick, P.G., and Fabina, N.S. (2023, August 28). Big Friendly Geospatial Networks (Bfgn). Available online: https:\/\/github.com\/pgbrodrick\/bfg-nets."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"094038","DOI":"10.1088\/1748-9326\/aba0ff","article-title":"Resistance of Mound-Building Termites to Anthropogenic Land-Use Change: Supporting Information","volume":"15","author":"Davies","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1016\/j.tree.2019.03.006","article-title":"Uncovering Ecological Patterns with Convolutional Neural Networks","volume":"34","author":"Brodrick","year":"2019","journal-title":"Trends Ecol. Evol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the KDD \u201816: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_33","unstructured":"(2023, May 25). XGBoost Documentation. Available online: https:\/\/xgboost.readthedocs.io\/en\/stable\/index.html."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.solmat.2004.11.013","article-title":"Solar Spectral Optical Properties of Pigments\u2014Part II: Survey of Common Colorants","volume":"89","author":"Levinson","year":"2005","journal-title":"Sol. Energy Mater. Sol. Cells"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.solmat.2006.06.062","article-title":"Methods of Creating Solar-Reflective Nonwhite Surfaces and Their Application to Residential Roofing Materials","volume":"91","author":"Levinson","year":"2007","journal-title":"Sol. Energy Mater. Sol. Cells"},{"key":"ref_36","unstructured":"Levinson, R., Berdahl, P., and Akbari, H. (2023, August 28). Lawrence Berkeley National Laboratory Pigment Database, Available online: https:\/\/coolcolors.lbl.gov\/LBNL-Pigment-Database\/database.html."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.rse.2004.02.013","article-title":"Spectrometry for Urban Area Remote Sensing\u2014Development and Analysis of a Spectral Library from 350 to 2400 Nm","volume":"91","author":"Herold","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1038\/s41597-020-0542-3","article-title":"A Rasterized Building Footprint Dataset for the United States","volume":"7","author":"Heris","year":"2020","journal-title":"Sci. Data"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in Vegetation Remote Sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"241","DOI":"10.3847\/1538-4357\/ab54d0","article-title":"An Efficient Spectral Selection of M Giants Using XGBoost","volume":"887","author":"Yi","year":"2019","journal-title":"Astrophys. J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1111\/j.1936-704X.2020.03339.x","article-title":"Hawai\u2019i\u2019s Cesspool Problem: Review and Recommendations for Water Resources and Human Health","volume":"170","author":"Mezzacapo","year":"2020","journal-title":"J. Contemp. Water Res. Educ."},{"key":"ref_43","unstructured":"Carollo Engineers (2021). Cesspool Conversion Technologies Research Summary Report, Carollo Engineers."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.applthermaleng.2013.12.056","article-title":"Impact of Using Cool Paints on Energy Demand and Thermal Comfort of a Residential Building","volume":"65","author":"Dias","year":"2014","journal-title":"Appl. Therm. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"835","DOI":"10.2175\/106143015X14362865226437","article-title":"Roofing Materials Assessment: Investigation of Five Metals in Runoff from Roofing Materials","volume":"87","author":"Winters","year":"2015","journal-title":"Water Environ. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"148632","DOI":"10.1016\/j.scitotenv.2021.148632","article-title":"Water Quality Thresholds for Coastal Contaminant Impacts on Corals: A Systematic Review and Meta-Analysis","volume":"794","author":"Nalley","year":"2021","journal-title":"Sci. Total Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4389\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:19Z","timestamp":1760129179000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,6]]},"references-count":46,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184389"],"URL":"https:\/\/doi.org\/10.3390\/rs15184389","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,6]]}}}