SpaceNet 6: Multi-Sensor All Weather Mapping Dataset
| Autor: | Fabio Pacifici, Nicholas R. Weir, Jason Brown, Scott Soenen, Adam Van Etten, Ronny Hänsch, Todd M. Bacastow, Daniel Hogan, Alexei Bastidas, Ryan Lewis, Jacob Shermeyer |
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| Rok vydání: | 2020 |
| Předmět: |
Synthetic aperture radar
FOS: Computer and information sciences Geospatial analysis 010504 meteorology & atmospheric sciences Computer science Cloud cover Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology computer.software_genre 01 natural sciences Set (abstract data type) Footprint FOS: Electrical engineering electronic engineering information engineering 021101 geological & geomatics engineering 0105 earth and related environmental sciences data fusion business.industry Image and Video Processing (eess.IV) Ground sample distance deep learning Electrical Engineering and Systems Science - Image and Video Processing semantic segmentation machine learning instance segmentation Benchmark (computing) Artificial intelligence Data mining business computer SAR |
| Zdroj: | CVPR Workshops |
| DOI: | 10.48550/arxiv.2004.06500 |
| Popis: | 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. To appear in CVPR EarthVision Proceedings, 10 pages, 7 figures |
| Databáze: | OpenAIRE |
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