DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

TLDR

Satellite imagery offers rich, structured data yet remains underexplored by computer vision, and integrating remote sensing with CV could advance environmental understanding, urban planning, and climate research. DeepGlobe seeks to unite computer vision and remote sensing researchers, raise awareness of satellite imagery, and establish benchmark datasets and evaluation methods for future research. The challenge offers three public competitions—segmentation, detection, and classification—alongside dataset analyses, evaluation criteria, and baseline methods.

Abstract

We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images (Figure 1). Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], 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.