Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepSatData: Building large scale datasets of satellite images for
  training machine learning models   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.

20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
9
The categories of this paper are: nlin