Automated semantic labeling of complex urban scenes in remotely sensed 2D and 3D data is one of the most challenging steps in producing realistic 3D scene models and maps. Recent large-scale public benchmark data sets and challenges for semantic labeling with 2D imagery have been instrumental in identifying state of the art methods and enabling new research. 3D data from lidar and multi-view stereo have also been shown to provide valuable additional information to enable improved semantic labeling accuracy. In this work, we describe the development of a new large-scale data set combining public lidar and multi-view satellite imagery with pixel-level truth for ground labels and instance-level truth for building labels. We demonstrate the use of this data set to evaluate methods for ground and building labeling tasks to establish performance expectations and identify areas for improvement. We also discuss initial steps toward further leveraging this data set to enable machine learning for more complex semantic and instance segmentation and 3D reconstruction tasks. All software developed to produce this public data set and to enable metric scoring are also released as open source code.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.