MSAW covers 120 km^2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels, enabling the creation and evaluation of mapping algorithms for multi-modal data. The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources. 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). 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. Consequently, SAR data are particularly valuable in the quest to aid disaster response, when weather and cloud cover can obstruct traditional optical sensors. Conversely, Synthetic Aperture Radar (SAR) sensors have the unique capability to penetrate clouds and collect during all weather, day and night conditions. optical data is often the preferred choice for geospatial applications, but requires clear skies and little cloud cover to work well. 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. Download a PDF of the paper titled SpaceNet 6: Multi-Sensor All Weather Mapping Dataset, by Jacob Shermeyer and 10 other authors Download PDF Abstract:Within the remote sensing domain, a diverse set of acquisition modalities exist, each with their own unique strengths and weaknesses.
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