We ultimately release 202 unique image strips with two different processing levels: georeferenced magnitude and polarimetry (MAG-POL) data and Single Look Complex (SLC) data. aws s3 ls s3://spacenet-dataset/AOIs/AOI_11_Rotterdam/ The SAR DatasetĪll of the SAR data comes from Capella Space’s X-band quad-pol sensor mounted on an aircraft. Once you’ve done that, you can query the full dataset using the command below and download your components of interest. You can explore and download the data now, all you need is an AWS account and the AWS CLI installed and configured. We believe this dataset will continue to further research and advance remote sensing analytics beyond the optical spectrum and into the SAR modality. Complimentary to our SAR data, we also release our untiled Maxar WorldView 2 image spanning ~92 km² at 0.5m spatial resolution. The decomposition channels show different types of scattering behavior. We distribute 202 SAR image strips in two formats: one with minimal pre-processing (Single Look Complex) as well as a second set of new six-band georeferenced products that include 4 channels of intensity and 2 channels derived from a Pauli decomposition. The SAR data in particular is one-of-kind and the first openly licensed dataset to feature quad-polarized imagery at 0.25m spatial resolution over such a vast extent. The expanded version is unique and features a combination of Capella Space Synthetic Aperture Radar (SAR) and Maxar WorldView 2 imagery. Today the SpaceNet partners are pleased to announce the release of the EXPANDED version of the SpaceNet 6 dataset over the port of Rotterdam, the Netherlands. SpaceNet is run in collaboration by co-founder and managing partner, CosmiQ Works, co-founder and co-chair, Maxar Technologies, and our partners including Amazon Web Services (AWS), Capella Space, Topcoder, IEEE GRSS, the National Geospatial-Intelligence Agency and Planet. SpaceNet 8 - The Detection of Flooded Roads and Buildings by Ronny Hansch, Jacob Arndt, Dalton Lunga, Matthew Gibb, Tyler Pedelose, Arnold Boedihardjo, Desiree Petrie, Todd M.Preface: SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e., building footprint & road network detection).SpaceNet 6: Dataset Release by Jake Shermeyer.SpaceNet 5 Dataset Release by Adam Van Etten and Ryan Lewis.Introducing the SpaceNet Road Detection and Routing Challenge and Dataset by David Lindenbaum.Introducing the SpaceNet Off-Nadir Imagery Dataset by David Lindenbaum.FCAU-Net for the Semantic Segmentation of Fine-Resolution Remotely Sensed Images by Xuerui Niu, Qiaolin Zeng, Xiaobo Luo and Liangfu Chen.Deploying the SpaceNet 6 Baseline on AWS by Adam Van Etten and Nick Weir.Creating Training Datasets for the SpaceNet Road Detection and Routing Challenge by Adam Van Etten and Jake Shermeyer.Accelerating Ukraine Intelligence Analysis with Computer Vision on Synthetic Aperture Radar Imagery by Ritwik Gupta, Colorado Reed, Anja Rohrbach, and Trevor Darrell.Solaris: an open source Python library for analyzing overhead imagery with machine learning by Nick Weir.Getting Started with SpaceNet Data by Adam Van Etten.Extracting buildings and roads from AWS Open Data using Amazon SageMaker by Yunzhi Shi, Tianyu Zhang, and Xin Chen.
0 Comments
Leave a Reply. |