Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis
This work addresses the lack of regulated datasets for land-use mapping in regions like Flanders, which is important for remote sensing applications, but appears incremental as it formalizes existing approaches rather than introducing new methods.
The paper tackles the challenge of creating accurate land-use maps for the Flanders region by introducing a densely labeled ground truth dataset paired with Sentinel-2 satellite imagery, along with a formalized workflow for semantic segmentation, though specific performance numbers are not provided.
In recent years, machine learning has become crucial in remote sensing analysis, particularly in the domain of Land-use/Land-cover (LULC). The synergy of machine learning and satellite imagery analysis has demonstrated significant productivity in this field, as evidenced by several studies. A notable challenge within this area is the semantic segmentation mapping of land usage over extensive territories, where the accessibility of accurate land-use data and the reliability of ground truth land-use labels pose significant difficulties. For example, providing a detailed and accurate pixel-wise labeled dataset of the Flanders region, a first-level administrative division of Belgium, can be particularly insightful. Yet there is a notable lack of regulated, formalized datasets and workflows for such studies in many regions globally. This paper introduces a comprehensive approach to addressing these gaps. We present a densely labeled ground truth map of Flanders paired with Sentinel-2 satellite imagery. Our methodology includes a formalized dataset division and sampling method, utilizing the topographic map layout 'Kaartbladversnijdingen,' and a detailed semantic segmentation model training pipeline. Preliminary benchmarking results are also provided to demonstrate the efficacy of our approach.