Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution
This work addresses the challenge of scalable and accessible building construction tracking in areas with limited high-resolution data, though it is incremental in leveraging existing super-resolution techniques.
The paper tackles the problem of tracking urban development in developing regions by generating high-resolution time series from low-resolution remote sensing images, achieving significant improvement over baselines.
Automated tracking of urban development in areas where construction information is not available became possible with recent advancements in machine learning and remote sensing. Unfortunately, these solutions perform best on high-resolution imagery, which is expensive to acquire and infrequently available, making it difficult to scale over long time spans and across large geographies. In this work, we propose a pipeline that leverages a single high-resolution image and a time series of publicly available low-resolution images to generate accurate high-resolution time series for object tracking in urban construction. Our method achieves significant improvement in comparison to baselines using single image super-resolution, and can assist in extending the accessibility and scalability of building construction tracking across the developing world.