Remote Sensing Change Detection via Weak Temporal Supervision

📝 Original Info

• Title: Remote Sensing Change Detection via Weak Temporal Supervision
• ArXiv ID: 2601.02126
• Date: 2026-01-05
• Authors: Xavier Bou, Elliot Vincent, Gabriele Facciolo, Rafael Grompone von Gioi, Jean-Michel Morel, Thibaud Ehret

📝 Abstract

Figure 1. Large-scale building change detection comparison on BD ORTHO [29] data (Lille metropolitan area in France, approximately 55.3 km²). Availability of annotated datasets has always been a challenge for semantic change detection. To avoid this problem, we propose a novel weak temporal supervision strategy that leverages additional temporal observations of existing single-date annotated data. This allows us to train robust and scalable models, without requiring any new annotations. Left: map presenting the reference building changes (demolitions and constructions) derived from the French IGN's OCS GE data product [30]. Middle: result of a Dual UNet trained with our methodology, which closely aligns with the reference. Right: output of a Dual UNet trained on FSC-180k [4], which produces numerous false positives.

📄 Full Content

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