Furthermore, we think that solving this challenge is an important stepping stone to unleashing the power of advanced computer vision algorithms applied to a variety of remote sensing data applications in both the public and private sector. We believe that advancing automated feature extraction techniques will serve important downstream uses of map data including humanitarian and disaster response, as observed by the need to map road networks during the response to recent flooding in Bangladesh and Hurricane Maria in Puerto Rico. Today, map features such as roads, building footprints, and points of interest are primarily created through manual techniques. CosmiQ Works, Radiant Solutions and NVIDIA have partnered to release the SpaceNet data set to the public to enable developers and data scientists to work with this data. Worlds first rapid, accurate, and fully contactless Parkinsons Disease Diagnosis. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. This report can also tell patients if they should try to schedule a call or appointment with specialists sooner rather than later. Der Datenbestand umfasst mehr als 140 Millionen Patentdokumente aus der ganzen Welt. The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One of the goals of ParkinSense is to make Parkinson’s patients feel more informed with their prognosis, so by providing a summary report, we are making their status as transparent as possible for them. Espacenet eignet sich für Anfänger und Experten und wird täglich aktualisiert. Winning algorithms for the SpaceNet 4: Off-Nadir Building Footprint Detection Challenge Python 164 Apache-2.0 36 2 0 Updated on SAR-RGB-DataFusion Public A small codebase featuring a few elementary methods for SAR and Optical imagery data fusion Jupyter Notebook 16 Apache-2.
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