# GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images

Source: [GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images](<https://www.sota2.com/research/paper/68fa74041e209131cea5f40e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:00:03.231Z

Authors: Jon Crall, Connor Greenwell, David Joy, Matthew Leotta, Aashish Chaudhary, Anthony Hoogs

Published: 2024-07-08T19:09:00.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2407.06337>)

[PDF](<https://arxiv.org/pdf/2407.06337.pdf>)