Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis
Mingshi Li Dusan Grujicic Steven De Saeger Stien Heremans Ben Somers Matthew B. Blaschko
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.