A Self-Supervised Approach for Producing A Land Use Map for Southern Quebec

Accurate and up-to-date land cover (LC) maps are critical for informed decision-making and resource management. While global products may not include region-specific classes or have optimal accuracy, this paper presents a method to produce a 10 m resolution LC map for Southern Quebec, Canada with 10 classes related to soil artificialization. To overcome annotation effort, a DeepResUNet model is trained on 400 simulated Sentinel-2 scene mosaics instead of real images. Scene realism is enhanced through the use of data augmentation and linear mixing of pure samples. Preliminary results demonstrate improved spatial detail compared to other products, with better delineation of roads and urban areas. A preliminary quantitative analysis based on a visual interpretation of 400 locations across 4 areas indicates that our land cover map aligns with ground conditions three times more often than a recent global land cover product, where they disagree.