  We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which
includes three public competitions for segmentation, detection, and
classification tasks on satellite images. Similar to other challenges in
computer vision domain such as DAVIS and COCO, DeepGlobe proposes three
datasets and corresponding evaluation methodologies, coherently bundled in
three competitions with a dedicated workshop co-located with CVPR 2018.
  We observed that satellite imagery is a rich and structured source of
information, yet it is less investigated than everyday images by computer
vision researchers. However, bridging modern computer vision with remote
sensing data analysis could have critical impact to the way we understand our
environment and lead to major breakthroughs in global urban planning or climate
change research. Keeping such bridging objective in mind, DeepGlobe aims to
bring together researchers from different domains to raise awareness of remote
sensing in the computer vision community and vice-versa. We aim to improve and
evaluate state-of-the-art satellite image understanding approaches, which can
hopefully serve as reference benchmarks for future research in the same topic.
In this paper, we analyze characteristics of each dataset, define the
evaluation criteria of the competitions, and provide baselines for each task.
