Nvidia spacenet1/15/2024 ![]() The trio compiled the SpaceNet repository for applications such as infrastructure mapping, land usage classification and population estimates.Īs part of the effort, Nvidia said it is demonstrating mapping applications based on GPU-assisted deep learning. (NASDAQ: NVDA) and CosmiQ Works, a startup funded by the CIA venture fund, In-Q-Tel. New imagery and ground features are updated quarterly.Īlong with the crowdsourcing effort, DigitalGlobe is also working with graphics processor vendor Nvidia Corp. ![]() The imagery data is stored on the Amazon Web Service (NASDAQ: AMZN) Simple Storage Service. That capability allows precise mapping of each pixel in an image to a specific location on Earth. Data formats include GeoJSON for building footprints and GeoTIFF, a public domain metadata standard that allows “geo-referencing” information such as map projections to be embedded with a TIFF file. The dataset was released to advance algorithm development used to automatically extract ground features such as roads, buildings and points of interest. Along with eight spectral bands, the dataset also includes nearly 220,600 building footprints derived from imagery that can be used as training data for machine learning. The company said its SpaceNet imagery includes about 1,900 square kilometers of high-resolution imagery collected by its WorldView-2 commercial satellite. Volunteers will used the datasets to develop algorithms for extracting mapping data from high-resolution satellite images. The competition aims to speed development of applications such as automated mapping of changes in the urban landscape and monitoring the accuracy of maps used by self-driving cars.ĭigitalGlobe (NYSE: DGI) said the Topcoder crowdsourcing community would use its SpaceNet repository of global satellite imagery along with “labeled training data” released during the summer by data scientists and developers. ![]() A technology competition sponsored by commercial satellite imagery vendor DigitalGlobe will seek to advance development of machine- and deep-learning algorithms that would be applied to geospatial data for automated mapping. ![]()
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