# DeepSatData: Building large scale datasets of satellite images for training machine learning models Source: [DeepSatData: Building large scale datasets of satellite images for training machine learning models]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:11:37.721Z Authors: Michail Tarasiou, Stefanos Zafeiriou Published: 2021-04-28T15:13:12.000Z ## Abstract This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models with emphasis placed on dense classification tasks, e.g. semantic segmentation. The implementation presented makes use of freely available Sentinel-2 data which allows generation of large scale datasets required for training deep neural networks. We discuss issues faced from the point of view of deep neural network training and evaluation such as checking the quality of ground truth data and comment on the scalability of the approach. Accompanying code is provided in https://github.com/michaeltrs/DeepSatData. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()