Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis

• URL: http://arxiv.org/abs/2401.15223v1
• Date: Fri, 26 Jan 2024 22:21:39 GMT
• Title: Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis
• Authors: Mingshi Li, Dusan Grujicic, Steven De Saeger, Stien Heremans, Ben Somers, Matthew B. Blaschko
• Abstract summary: 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.
• Score: 12.025312586542318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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