  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.
