WATCH: Wide-Area Terrestrial Change Hypercube

Connor Greenwell, Jon Crall, Matthew Purri, Kristin Dana, Nathan Jacobs, Armin Hadzic, Scott Workman, Matt Leotta; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 8277-8286

Monitoring Earth activity using data collected from multiple satellite imaging platforms in a unified way is a significant challenge, especially with large variability in image resolution, spectral bands, and revisit rates. Further, the availability of sensor data varies across time as new platforms are launched. In this work, we introduce an adaptable framework and network architecture capable of predicting on subsets of the available platforms, bands, or temporal ranges it was trained on. Our system, called WATCH, is highly general and can be applied to a variety of geospatial tasks. In this work, we analyze the performance of WATCH using the recent IARPA SMART public dataset and metrics. We focus primarily on the problem of broad area search for heavy construction sites. Experiments validate the robustness of WATCH during inference to limited sensor availability, as well the the ability to alter inference-time spatial or temporal sampling. WATCH is open source and available for use on this or other remote sensing problems. Code and model weights are available at: https://gitlab.kitware.com/computer-vision/geowatch