{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T05:31:31Z","timestamp":1780723891354,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T00:00:00Z","timestamp":1746144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Space It Up","award":["2024-5-E.0"],"award-info":[{"award-number":["2024-5-E.0"]}]},{"name":"Space It Up","award":["I53D24000060005"],"award-info":[{"award-number":["I53D24000060005"]}]},{"name":"Italian Space Agency and the Ministry of University and Research","award":["2024-5-E.0"],"award-info":[{"award-number":["2024-5-E.0"]}]},{"name":"Italian Space Agency and the Ministry of University and Research","award":["I53D24000060005"],"award-info":[{"award-number":["I53D24000060005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The analysis of aerial and satellite images for building footprint detection is one of the major challenges in photogrammetry and remote sensing. This information is useful for various applications, such as urban planning, disaster monitoring, and 3D city modeling. However, it has become a significant challenge due to the diverse characteristics of buildings, such as shape, size, and shadow interference. This study investigated the simultaneous use of aerial and satellite images to improve the accuracy of deep learning models in building footprint detection. For this purpose, aerial images with a spatial resolution of 30 cm and Sentinel-2 satellite imagery were employed. Several satellite-derived spectral indices were extracted from the Sentinel-2 image. Then, U-Net models combined with ResNet-18 and ResNet-34 were trained on these data. The results showed that the combination of the U-Net model with ResNet-34, trained on a dataset obtained by integrating aerial images and satellite indices, referred to as RGB\u2013Sentinel\u2013ResNet34, achieved the best performance among the evaluated models. This model attained an accuracy of 96.99%, an F1-score of 90.57%, and an Intersection over Union of 73.86%. Compared to other models, RGB\u2013Sentinel\u2013ResNet34 showed a significant improvement in accuracy and generalization capability. The findings indicated that the simultaneous use of aerial and satellite data can substantially enhance the accuracy of building footprint detection.<\/jats:p>","DOI":"10.3390\/info16050380","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T07:44:23Z","timestamp":1746171863000},"page":"380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Fusion of Aerial and Satellite Images for Automatic Extraction of Building Footprint Information Using Deep Neural Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9315-0639","authenticated-orcid":false,"given":"Ehsan","family":"Haghighi Gashti","sequence":"first","affiliation":[{"name":"School of Surveying and Geospatial Eng, College of Engineering, University of Tehran, 1417935840 Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanieh","family":"Bahiraei","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics Engineering, K. N. Toosi University of Technology, 1969764499 Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4325-8741","authenticated-orcid":false,"given":"Mohammad Javad","family":"Valadan Zoej","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics Engineering, K. N. Toosi University of Technology, 1969764499 Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5165-1773","authenticated-orcid":false,"given":"Ebrahim","family":"Ghaderpour","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, Sapienza University of Rome, P. le Aldo Moro, 5, 00185 Rome, Italy"},{"name":"Earth and Space Inc., Calgary, AB T3A 5B1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s12145-022-00895-4","article-title":"Building footprint extraction and counting on very high-resolution satellite imagery using object detection deep learning framework","volume":"16","author":"Nurkarim","year":"2023","journal-title":"Earth Sci. 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