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RESEARCH PAPERS & REPORTS
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FedGeoDay 2022 – SpaceNet 8
Celebrating SpaceNet
SpaceNet 6 Challenge Overview – CVPR Earthvision 2020
SpaceNet 6 Challenge Introduction
Evaluating SpaceNet 5 Challenge Results: Road Network Detection & Optimized Routing
Accelerating Geospatial Machine Learning with SpaceNet
GROUNDWORK COLLABORATION
Interested in building your own labels on SpaceNet data? GroundWork, Azavea’s new geospatial-focused data annotation tool, has the entire library of SpaceNet Geographic Datasets pre-loaded and ready to annotate with semantic segmentation, objection, or classification labels. Sponsored by CosmiQ Works and Azavea
FAQs
What is SpaceNet?
SpaceNet is a collaborative initiative that fosters research and innovation in the development of computer vision algorithms to automatically extract information from remote sensing data.
The four main goals of SpaceNet are to:
- Release openly licensed satellite imagery and associated geospatial labels
- Share open-source software tools to help reduce the amount of geographic information system (GIS) expertise required of machine learning developers interested in aerial and satellite imagery
- Publish and foster research on emerging analytical frameworks in open forums
- Sponsor open analytics prize challenges
What is the value of SpaceNet Challenges? How are the events useful?
A significant amount of the world is not mapped. Organizations such as Missing Maps and Humanitarian OpenStreetMap Team help to solve this problem by distributing tools and organizing mapping campaigns.
The first step is to trace satellite imagery for building footprints and roads. From there, local knowledge can be applied to identify the buildings and roads.
Prize competitions can help develop and assess technology that could be used to accelerate the mapping of these regions.
How are the SpaceNet Challenge results calculated?
The SpaceNet team strives to establish fair and geospatially meaningful metrics. When necessary, the team will create a new metric such as for the Road Network Extraction challenge.
How are SpaceNet results leveraged?
SpaceNet dataset use has included published papers and internal company research as well as the support of multiple additional competitions.
The winning algorithms have informed deployment of novel machine learning algorithms for both building footprint extraction and road network extraction.