One challenging problem in many remote sensing applications is identifying building footprints in 2D and/or 3D imagery. Existing solutions to this problem use a variety of sensing modalities as input. Recent public challenges have yielded high quality building footprint detection algorithms using high-resolution 2D and 3D imaging modalities as input. However, performance of many of these algorithms is typically degraded as the fidelity and post spacing of the input imagery is reduced. Other challenges use lower resolution 2D satellite imagery alone. The United States Special Operations Command (USSOCOM) sponsored a public prize challenge aimed at identifying building footprints using 2D RGB orthorectified imagery and coincident 3D Digital Surface Models (DSMs) created from commercial satellite imagery. The top 6 winning solutions have been made publicly available as open source software. This paper summarizes the public challenge and provides results and data analysis. In addition, we provide lessons learned and hope to encourage additional research by publicly releasing the benchmark dataset to the community.
Hirsh R. Goldberg, Sean Wang, Gordon A. Christie, and Myron Z. Brown, "Urban 3D challenge: building footprint detection using orthorectified imagery and digital surface models from commercial satellites," Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 1064503 (Presented at SPIE Defense + Security: April 16, 2018; Published: 27 April 2018); https://doi.org/10.1117/12.2304682.
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