Self-Supervision, Remote Sensing and Abstraction: Representation Learning Across 3 Million Locations
This addresses the problem of urban computing for researchers and practitioners by improving generalization in remote sensing, though it is incremental as it adapts existing self-supervised methods to a new domain.
The paper tackled city classification from remote sensing imagery using self-supervised contrastive learning, achieving over 95% accuracy in unseen cities with minimal training from as few as 200 cities.
Self-supervision based deep learning classification approaches have received considerable attention in academic literature. However, the performance of such methods on remote sensing imagery domains remains under-explored. In this work, we explore contrastive representation learning methods on the task of imagery-based city classification, an important problem in urban computing. We use satellite and map imagery across 2 domains, 3 million locations and more than 1500 cities. We show that self-supervised methods can build a generalizable representation from as few as 200 cities, with representations achieving over 95\% accuracy in unseen cities with minimal additional training. We also find that the performance discrepancy of such methods, when compared to supervised methods, induced by the domain discrepancy between natural imagery and abstract imagery is significant for remote sensing imagery. We compare all analysis against existing supervised models from academic literature and open-source our models for broader usage and further criticism.