COLD-CI: A large-scale very high-resolution label polygon dataset for cocoa and non-cocoa classification in Cote d'Ivoire

Spatially explicit information on cocoa cultivation is essential for land-use planning, deforestation monitoring, environmental assessment, and supply-chain analysis. Although several cocoa map products exist, their underlying reference data are often not publicly available, limiting transparency and methodological benchmarking. Here, we present a large-scale, very high-resolution cocoa and non-cocoa label polygon dataset for Cote d'Ivoire (COLD-CI), covering the main cocoa-producing regions as well as contrasting non-cocoa landscapes. COLD-CI consists of 123,736 vector polygons corresponding to a total labelled area of 5,996 km^2, including 58,107 cocoa polygons (1,788 km^2) and 65,629 background polygons (4,208 km^2). Polygon label candidates were first generated through conservative automated filtering of polygons from the West Africa Cocoa dataset and the combination of multiple external thematic datasets. These candidates were subsequently refined and complemented through systematic visual interpretation, manual correction, and digitisation using very high-resolution (0.5 m) satellite imagery. The resulting label polygons capture cocoa planted areas and associated fine-scale internal heterogeneity, as well as a wide range of non-cocoa land-cover types. Independent validation using field-based and expert photointerpreted reference data from the Copernicus4GEOGLAM validation dataset indicated an overall agreement of 99%, with producer's and user's accuracy exceeding 98% for both cocoa and background classes. COLD-CI is released as a vector dataset with associated metadata to support transparent benchmarking, model development, and validation across a wide range of spatial resolutions.