足迹
卫星图像
计算机科学
正射影像
水准点(测量)
地理空间分析
卫星
遥感
模式
RGB颜色模型
人工智能
数据科学
计算机视觉
地理
工程类
地图学
社会学
航空航天工程
考古
社会科学
作者
Hirsh Goldberg,Sean Wang,Gordon Christie,Myron Brown
摘要
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.
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