{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T16:03:13Z","timestamp":1774281793142,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["42301533"],"award-info":[{"award-number":["42301533"]}]},{"name":"the National Natural Science Foundation of China","award":["42101384"],"award-info":[{"award-number":["42101384"]}]},{"name":"the National Natural Science Foundation of China","award":["BK20210043"],"award-info":[{"award-number":["BK20210043"]}]},{"name":"the National Natural Science Foundation of China","award":["YK323011"],"award-info":[{"award-number":["YK323011"]}]},{"name":"the Natural Science Foundation of Jiangsu Province","award":["42301533"],"award-info":[{"award-number":["42301533"]}]},{"name":"the Natural Science Foundation of Jiangsu Province","award":["42101384"],"award-info":[{"award-number":["42101384"]}]},{"name":"the Natural Science Foundation of Jiangsu Province","award":["BK20210043"],"award-info":[{"award-number":["BK20210043"]}]},{"name":"the Natural Science Foundation of Jiangsu Province","award":["YK323011"],"award-info":[{"award-number":["YK323011"]}]},{"name":"the Open Research Fund of Key Laboratory of Reservoir and Dam Safety Ministry of Water Resources","award":["42301533"],"award-info":[{"award-number":["42301533"]}]},{"name":"the Open Research Fund of Key Laboratory of Reservoir and Dam Safety Ministry of Water Resources","award":["42101384"],"award-info":[{"award-number":["42101384"]}]},{"name":"the Open Research Fund of Key Laboratory of Reservoir and Dam Safety Ministry of Water Resources","award":["BK20210043"],"award-info":[{"award-number":["BK20210043"]}]},{"name":"the Open Research Fund of Key Laboratory of Reservoir and Dam Safety Ministry of Water Resources","award":["YK323011"],"award-info":[{"award-number":["YK323011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Oblique photography is a regional digital surface model generation technique that can be widely used for building 3D model construction. However, due to the lack of geometric and semantic information about the building, these models make it difficult to differentiate more detailed components in the building, such as roofs and balconies. This paper proposes a deep learning-based method (U-NET) for constructing 3D models of low-rise buildings that address the issues. The method ensures complete geometric and semantic information and conforms to the LOD2 level. First, digital orthophotos are used to perform building extraction based on U-NET, and then a contour optimization method based on the main direction of the building and the center of gravity of the contour is used to obtain the regular building contour. Second, the pure building point cloud model representing a single building is extracted from the whole point cloud scene based on the acquired building contour. Finally, the multi-decision RANSAC algorithm is used to segment the building detail point cloud and construct a triangular mesh of building components, followed by a triangular mesh fusion and splicing method to achieve monolithic building components. The paper presents experimental evidence that the building contour extraction algorithm can achieve a 90.3% success rate and that the resulting single building 3D model contains LOD2 building components, which contain detailed geometric and semantic information.<\/jats:p>","DOI":"10.3390\/rs16132404","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T08:17:29Z","timestamp":1719821849000},"page":"2404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["LOD2-Level+ Low-Rise Building Model Extraction Method for Oblique Photography Data Using U-NET and a Multi-Decision RANSAC Segmentation Algorithm"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7411-5981","authenticated-orcid":false,"given":"Yufeng","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Poyang Lake Wetland and Watershed Research of Ministry of Education, Jiangxi Normal University, Nanchang 330022, China"},{"name":"Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province, Nanchang 330022, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, School of Geography, Nanjing Normal University, Nanjing 210023, China"}]},{"given":"Xiaobian","family":"Wu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China"}]},{"given":"Weibin","family":"Pan","sequence":"additional","affiliation":[{"name":"North Information Control Research Academy Group Co., Ltd., Nanjing 211106, China"}]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}]},{"given":"Songshan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Jiangxi Institute of Land Space Survey and Planning, Technology Innovation Center for Land Spatial Ecological Protection and Restoration in Great Lakes Basin, Ministry of Natural Resources, Nanchang 330029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4138-0850","authenticated-orcid":false,"given":"Shaohua","family":"Lei","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China"}]},{"given":"Xiaoran","family":"Gong","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China"}]},{"given":"Hanzeyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China"}]},{"given":"Yehua","family":"Sheng","sequence":"additional","affiliation":[{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographical Environment of Ministry of Education, School of Geography, Nanjing Normal University, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,30]]},"reference":[{"key":"ref_1","unstructured":"Florent, L. 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