DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
This work addresses the gap in computer vision research for satellite imagery, potentially impacting global urban planning and climate change research, but it is incremental as it builds on existing challenge formats like DAVIS and COCO.
The authors introduced the DeepGlobe 2018 challenge, which includes three competitions for segmentation, detection, and classification tasks on satellite images, aiming to bridge computer vision and remote sensing by providing datasets and baselines.
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.