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Remote sensing data is plentiful, but downloading, organizing, and transforming large amounts of data into a format readily usable by modern machine learning methods is a challenging and labor-intensive task. We present the OpenSentinelMap dataset, which consists of 137,045 unique 3.7 km 2 spatial cells, each containing multiple multispectral Sentinel-2 images captured over a 4 year time period and a set of corresponding per-pixel semantic labels derived from OpenStreetMap data. The labels are not necessarily mutually exclusive, and contain information about roads, buildings, water, and 12 land-use categories. The spatial cells are selected randomly on a global scale over areas of human activity, without regard to OpenStreetMap data availability or quality, making the dataset ideal for both supervised, semi-supervised, and un-supervised experimentation. To demonstrate the effectiveness of the dataset, we a) train an off-the-shelf convolutional neural network with minimal modification to predict land-use and building and road location from multi-spectral Sentinel-2 imagery and b) show that the learned embeddings are useful for downstream fine-grained classification tasks without any fine-tuning. The dataset is publicly available at https://visionsystemsinc.github.io/open-sentinel-map/.
Johnson et al. (Wed,) studied this question.