Remote Sensing Change Detection via Weak Temporal Supervision

TL;DR

This paper proposes a weak supervision approach for remote sensing change detection that leverages additional temporal observations without new annotations, improving zero-shot and low-data performance.

Contribution

It introduces a novel weak temporal supervision strategy that extends single-temporal datasets with multiple observations, enabling effective change detection without additional annotations.

Findings

Strong zero-shot performance on extended datasets

Effective low-data regime results

Scalability demonstrated over large areas in France

Abstract

Semantic change detection in remote sensing aims to identify land cover changes between bi-temporal image pairs. Progress in this area has been limited by the scarcity of annotated datasets, as pixel-level annotation is costly and time-consuming. To address this, recent methods leverage synthetic data or generate artificial change pairs, but out-of-domain generalization remains limited. In this work, we introduce a weak temporal supervision strategy that leverages additional temporal observations of existing single-temporal datasets, without requiring any new annotations. Specifically, we extend single-date remote sensing datasets with new observations acquired at different times and train a change detection model by assuming that real bi-temporal pairs mostly contain no change, while pairing images from different locations to generate change examples. To handle the inherent noise in these weak labels, we employ an object-aware change map generation and an iterative refinement process. We validate our approach on extended versions of the FLAIR and IAILD aerial datasets, achieving strong zero-shot and low-data regime performance across different benchmarks. Lastly, we showcase results over large areas in France, highlighting the scalability potential of our method.