GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images
This addresses the problem of detecting heavy construction in satellite imagery for remote sensing applications, but it appears incremental as it builds on existing methods with a novel mechanism.
The paper tackles the challenge of learning from multiple satellite sensors with spatio-temporal misalignment and spectral differences by introducing GeoWATCH, a flexible framework for training models on long sequences of satellite images, which improved performance through continual training and adjustments.
Learning from multiple sensors is challenging due to spatio-temporal misalignment and differences in resolution and captured spectra. To that end, we introduce GeoWATCH, a flexible framework for training models on long sequences of satellite images sourced from multiple sensor platforms, which is designed to handle image classification, activity recognition, object detection, or object tracking tasks. Our system includes a novel partial weight loading mechanism based on sub-graph isomorphism which allows for continually training and modifying a network over many training cycles. This has allowed us to train a lineage of models over a long period of time, which we have observed has improved performance as we adjust configurations while maintaining a core backbone.