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SpaceNet 6: Multi-Sensor All Weather Mapping Dataset by Shermeyer, Jacob; Hogan, Daniel; Brown, Jason; Van Etten, Adam; Weir, Nicholas; Pacifici, Fabio; Haensch, Ronny; Bastidas, Alexei; Soenen, Scott; Bacastow, Todd; Lewis, Ryan is a scholarly article available to read on EtoBox.

What is SpaceNet 6: Multi-Sensor All Weather Mapping Dataset about?

Within the remote sensing domain, a diverse set of acquisition modalities exist, each with their own unique strengths and weaknesses. Yet, most of the current literature and open datasets only deal with electro-optical (optical) data for different detection and segmentation tasks at high spatial resolutions. optical data is often the preferred choice for geospatial applications, but requires clear skies and little cloud cover to work well. Conversely, Synthetic Aperture Radar (SAR) sensors have the unique capability to penetrate clouds and collect during all weather, day and night conditions. Consequently, SAR data are particularly valuable in the quest to aid disaster response, when weather and cloud cover can obstruct traditional optical sensors. Despite all of these advantages, there is little open data available to researchers to explore the effectiveness of SAR for such applications, particularly at very-high spatial resolutions, i.e. <1m Ground Sample Distance (GSD). To address this problem, we present an open Multi-Sensor All Weather Mapping (MSAW) dataset and challenge, which features two collection modalities (both SAR and optical). The dataset and challenge focus on mappi

Author

Shermeyer, Jacob; Hogan, Daniel; Brown, Jason; Van Etten, Adam; Weir, Nicholas; Pacifici, Fabio; Haensch, Ronny; Bastidas, Alexei; Soenen, Scott; Bacastow, Todd; Lewis, Ryan

Published

2020

Language

EN