A Framework for Semi-automatic Collection of Temporal Satellite Imagery for Analysis of Dynamic Regions

Abstract

Analyzing natural and anthropogenic activities using re-mote sensing data has become a problem of increasing interest. However, this generally involves tediously labeling extensive imagery, perhaps on a global scale. The lack of a streamlined method to collect and label imagery over time makes it challenging to tackle these problems using popular, supervised deep learning approaches. We address this need by presenting a framework to semi-automatically collect and label dynamic regions in satellite imagery using crowd-sourced OpenStreetMap data and available satellite imagery resources. The generated labels can be quickly verified to ease the burden of full manual labeling. We leverage this framework for the ability to gather image sequences of areas that have label reclassification over time. One possible application of our framework is demonstrated to collect and classify construction vs. non-construction sites. Overall, the proposed framework can be adapted for similar change detection or classification tasks in various re-mote sensing applications.