SpaceNet: A Remote Sensing Dataset and Challenge Series

TL;DR

SpaceNet introduces a large labeled satellite imagery dataset and a series of public challenges to advance machine learning methods for rapid, automated map updating in dynamic scenarios like natural disasters.

Contribution

The paper presents the SpaceNet dataset and challenge series, fostering development of automated mapping techniques using satellite imagery and machine learning.

Findings

Public prize competitions improved building footprint extraction algorithms.

Road network extraction challenges advanced the state of the art.

The dataset enables rapid map updates in disaster scenarios.

Abstract

Foundational mapping remains a challenge in many parts of the world, particularly in dynamic scenarios such as natural disasters when timely updates are critical. Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Services (AWS) called SpaceNet. The SpaceNet partners also launched a series of public prize competitions to encourage improvement of remote sensing machine learning algorithms. The first two of these competitions focused on automated building footprint extraction, and the most recent challenge focused on road network extraction. In this paper we discuss the SpaceNet imagery, labels, evaluation metrics, prize challenge results to date, and future plans for the SpaceNet challenge series.