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The full dataset is freely available at the link below (perennial repository) for academic use or other non-commercial use: https://lab.plantnet.org/seafile/d/936fe4298a5a4f4c8dbd/; The difficulty to measure or predict species community composition at fine spatio-temporal resolution and over large spatial scales severely hampers our ability to understand species assemblages and take appropriate conservation measures. Despite the progress in species distribution modeling (SDM) over the past decades, SDM are just beginning to integrate high resolution remote sensing data and their predictions are still entailed by the many biases and heterogeneous quality of the available biodiversity observations, most often opportunistic presence only data. We designed a European scale dataset covering around 10K plant species to calibrate and evaluate SDM predictions of species composition in space and time at high spatial resolution (~10m), and study their spatial transferability. For model training, we extracted and harmonized 5 million heterogeneous presence-only observations from selected GBIF datasets and 5 thousand exhaustive presence-absence surveys both sampled during the 2017-2021. We associated them to various environmental rasters classically used in SDMs, as well as to 10x10m resolution RGB and Near-Infra-Red satellite images and 20 years-time series of climatic variables and satellite point values. The evaluation dataset is based on 20K standardized presence-absence surveys separated from the training set with a spatial block hold out procedure. The GeoLifeCLEF 2023 dataset is open access and the first benchmark for researchers aiming to improve the prediction of plant species composition at a very fine spatial grain and at continental scale. It is also a space to explore new ways of combining massive and diverse species observations with environmental information at various scales. Innovative AI-based approaches, in particular, should be among the most interesting methods to experiment with on the GeoLifeCLEF 2023 dataset.