World-Scale Construction Detection in Medium-Resolution Satellite Imagery Using a Probabilistic Graphical Model of Space-Time Pixel Class Labels

With the public availability of medium-resolution, high revisit-rate satellite imagery with worldwide coverage, such as Landsat and Sentinel-2, it is now feasible to perform automated, world-scale detection of interesting anthropogenic events. Challenges include disambiguating events from other changes in the scene and sensor, limited image resolution relative to the events to be detected, and difficulty in acquiring large amounts of labeled training data. An effective approach to this problem needs to leverage the latest advances in machine learning, remote sensing, and cloud computing. The IARPA SMART program recently evaluated the performance of state-of-art approaches in real-world test scenarios for the specific case of heavy construction events. Here we describe the approach used by the Systems and Technology Research team to achieve top-performing broad area search (BAS) accuracy in Phase I of SMART, which leverages recent advances in multispectral material classification, deep semantic segmentation, and probabilistic reasoning.