Wetland Land-Cover Segmentation and Classification in the Netherlands (Sentinel-2 satellite imagery and Dynamic World labels)

This dataset contains preprocessed Sentinel-2 imagery and corresponding Dynamic World land-cover labels for six wetland areas in the Netherlands. It was created to support land-cover classification and segmentation tasks in ecologically dynamic floodplain environments. The data covers the period January 2017 to November 2024 and includes only scenes with less than 5% cloud cover. Sentinel-2 imagery was retrieved using the Google Earth Engine (GEE) API from the COPERNICUS/S2 SR HARMONIZED collection, which provides Harmonized Level-2A data at 10 m spatial resolution. From the 26 available bands, 9 were selected based on their relevance for wetland delineation: RGB, Red Edge 1–3, Near-Infrared (NIR), and Shortwave Infrared (SWIR 1–2). The imagery was tiled into 256×256 pixel patches and filtered for quality (e.g., excluding patches with >10% black pixels). Dynamic World land-cover labels (Brown et al., 2022) were used to generate pixel-wise semantic segmentation masks by selecting the most probable class (out of 9 land-cover types) for each pixel. The resulting masks are single-band images where pixel values 0–8 represent land-cover classes as follows: 0: Water 1: Trees 2: Grass 3: Flooded Vegetation 4: Crops 5: Shrub & Scrub 6: Built 7: Bare 8: Snow & Ice The dataset includes the following splits: Training set: Gelderse Poort, Oostvaardersplassen, Loosdrechtse Plassen, Land van Saeftinghe (1,701 images) Validation set: Lauwersmeer (948 images) Test set: Biesbosch (1,140 images) This resource enables benchmarking of supervised and self-supervised learning methods for wetland classification in medium-resolution optical satellite data. Reference:Brown, C.F., Brumby, S.P., Guzder-Williams, B., Birch, T., Hyde, S.B., Mazzariello, J., Czerwinski, W., Pasquarella, V.J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., & Tait, A.M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01307-4