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

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.
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.
DeepSatData: Building large scale datasets of satellite images for training machine learning models
http://arxiv.org/abs/2104.13824v2