{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:29:37Z","timestamp":1780352977914,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"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>The detection of buildings in the city is essential in several geospatial domains and for decision-making regarding intelligence for city planning, tax collection, project management, revenue generation, and smart cities, among other areas. In the past, the classical approach used for building detection was by using the imagery and it entailed human\u2013computer interaction, which was a daunting proposition. To tackle this task, a novel network based on an end-to-end deep learning framework is proposed to detect and classify buildings features. The proposed CNN has three parallel stream channels: the first is the high-resolution aerial imagery, while the second stream is the digital surface model (DSM). The third was fixed on extracting deep features using the fusion of channel one and channel two, respectively. Furthermore, the channel has eight group convolution blocks of 2D convolution with three max-pooling layers. The proposed model\u2019s efficiency and dependability were tested on three different categories of complex urban building structures in the study area. Then, morphological operations were applied to the extracted building footprints to increase the uniformity of the building boundaries and produce improved building perimeters. Thus, our approach bridges a significant gap in detecting building objects in diverse environments; the overall accuracy (OA) and kappa coefficient of the proposed method are greater than 80% and 0.605, respectively. The findings support the proposed framework and methodologies\u2019 efficacy and effectiveness at extracting buildings from complex environments.<\/jats:p>","DOI":"10.3390\/rs13234803","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4803","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated Building Detection from Airborne LiDAR and Very High-Resolution Aerial Imagery with Deep Neural Network"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6384-3695","authenticated-orcid":false,"given":"Sani Success","family":"Ojogbane","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shattri","family":"Mansor","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2822-3463","authenticated-orcid":false,"given":"Bahareh","family":"Kalantar","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zailani Bin","family":"Khuzaimah","sequence":"additional","affiliation":[{"name":"Institute of Plantations Studies, University Putra Malaysia, Seri Kembangan 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helmi Zulhaidi Mohd","family":"Shafri","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naonori","family":"Ueda","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Al-Najjar, H.A.H., Kalantar, B., Pradhan, B., and Saeidi, V. 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