DOI: 10.5281/zenodo.7944111
title: Represent project UC2 Land Cover Dataset

description: The dataset is composed of Sentinel-2 data, while the ground truths come from Copernicus Land Monitoring Service – Hot Spot Mapping (HSM), that provides high resolution land cover maps over several Natural Protected Areas mainly in Africa. The land cover legend is based on the FAO Land Cover Classification System (LCCS). These labels have been produced and validated mainly through photointerpretation of HR data.<br> The mapped area of interest (AOI) represents a key landscape for conservation area (KLC). The KLC has a total size of almost 14,000,000 140,000 km2 and is covering a vast area in southern Tanzania all the way to northern Mozambique.<br> In Tanzania the AOI is covering the entire Selous game reserve, an area of around 50,000 km2, representing 6% of Tanzania’s land surface. This world heritage site is not only the oldest, but also the largest single protected area in Africa. It is characterised by an extensive area of natural miombo woodlands representing also one of the most extensive forest areas under protection. the reserve contains some of the most important populations of elephants, buffalos, antelopes, lions, wild dogs and other predators in Africa.<br> Located in northern Mozambique, the Niassa reserve is with 42,400 km2 the largest conservation area of the country, containing also the greatest concentration of wildlife of the country. the two reserved are connected by the Selous – Niassa wildlife corridor, an area of approximately 9000 km2 located entirely on the Tanzanian side which represents an important biological link between the two reserves and consequently for the miombo woodland eco-system. the Selous - Niassa ecosystem is one of the largest trans-boundary natural dry forest eco-regions in Africa. It constitutes one of the largest elephant ranges in the world and contains half of the world remaining wild dog population. it enables migration of wildlife and gene flow and contributing to the conservation of biodiversity. The dataset is organised in tiles, thus there is one folder for each of the Sentinel-2 tiles used. For each of these folders, there are the labels (both in vector and raster format) and the acquisitions organised in different acquisitions times.<br> Specifically, the dataset has the following folder structure: LandCover_Train_v2: directory for the training dataset of UC2 00XXX (e.g.: 37MCN): folders with Sentinel-2 tile name. labels: folder with raster and vector labels. The name of the label files in both format is the name of the Sentinel-2 tile to which they refer to (e.g.: 37MCN.tif/37MCN.shp) raster: labels in raster format (.tif) vector: labels in vector format (.shp) AAAAMMDD (e.g.: 20170630): folders with datetime of the Sentinel-2 acquisition. It contains all the 13 bands of the acquisition plus the TCI (True Color Image) in jp2 format. LandCover_Test: directory for the test dataset of UC2 00XXX (e.g.: 37MCN): folders with Sentinel-2 tile name. labels: folder with raster and vector labels. The name of the label files in both format is the name of the Sentinel-2 tile to which they refer to (e.g.: 37MCN.tif/37MCN.shp) raster: labels in raster format (.tif) vector: labels in vector format (.shp) AAAAMMDD (e.g.: 20170630): folders with datetime of the Sentinel-2 acquisition. It contains all the 13 bands of the acquisition plus the TCI (True Color Image) in jp2 format. GT_legend.xlsx: excel file with the association between the classes and the assigned number value in the raster version of the labels