DeepSatData: Building large scale datasets of satellite images for training machine learning models

• URL: http://arxiv.org/abs/2104.13824v1
• Date: Wed, 28 Apr 2021 15:13:12 GMT
• Title: DeepSatData: Building large scale datasets of satellite images for training machine learning models
• Authors: Michail Tarasiou, Stefanos Zafeiriou
• Abstract summary: This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models. We discuss issues faced from the point of view of deep neural network training and evaluation.
• Score: 77.17638664503215
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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