Deep learning for construction monitoring in rapidly urbanizing areas

Zachary J. DeSantis; Matthew D. Reisman; Adam G. Francisco; Latisha R. Konz; Dominic LeDuc; Timothy L. Overman

doi:10.1117/12.2624103

30 May 2022 Deep learning for construction monitoring in rapidly urbanizing areas

Zachary J. DeSantis, Matthew D. Reisman, Adam G. Francisco, Latisha R. Konz, Dominic LeDuc, Timothy L. Overman

Proceedings Volume PC12096, Automatic Target Recognition XXXII; PC1209606 (2022) https://doi.org/10.1117/12.2624103
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

We present a method for monitoring rapidly urbanizing areas with deep learning techniques. This method was generated during participation in the SpaceNet7 deep learning challenge and utilizes a U-Net architecture for semantically labeling each frame in a time series of monthly images that span roughly two years. The image sequences were collected over one hundred rapidly urbanizing regions. We discuss our network architecture and post processing algorithms for combining multiple semantically labeled frames to provide object level change detection.

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Citation Download Citation

Zachary J. DeSantis, Matthew D. Reisman, Adam G. Francisco, Latisha R. Konz, Dominic LeDuc, and Timothy L. Overman "Deep learning for construction monitoring in rapidly urbanizing areas", Proc. SPIE PC12096, Automatic Target Recognition XXXII, PC1209606 (30 May 2022); https://doi.org/10.1117/12.2624103

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