Reuse Out-of-Year Data to Enhance Land Cover Mapping via Feature Disentanglement and Contrastive Learning

Abstract

Given the systematic acquisition of satellite data, it is possible to generate up-to-date land cover (LC) maps, essential for effective agricultural territory management, environmental monitoring, and informed decision-making. Typically, creating a LC map requires collecting high-quality labeled data, a process that is both costly and time-consuming. To mitigate the need to collect large volume of labeled data, we propose a deep learning framework called REFeD (data Reuse with Effective Feature Disentanglement for land cover mapping), which leverages already available out-of-year reference data to enhance the production of up-to-date LC maps. To this end, REFeD integrates remote sensing and reference data from different domains (e.g., historical and recent data) utilizing a disentanglement strategy based on contrastive learning. By separating domain-invariant and domain-specific features, REFeD isolates useful information associated to the downstream LC mapping task and mitigates distribution shifts between domains. Moreover, REFeD incorporates an effective supervision scheme to reinforce feature disentanglement through multiple levels of supervision at different granularities. Experimental evaluation on study areas characterized by diverse landscapes, including Koumbia (West Africa, Burkina Faso) and Centre-Val de Loire (central Europe, France), demonstrates the effectiveness of the proposed approach.

Keywords

• Contrastive learning
• data-centric artificial intelligence (data-centric AI)
• domain adaptation
• land cover (LC) mapping
• satellite image time series (SITS)