Testing Dataset and Public Leaderboard Standings SpaceNet 5 image chips in Moscow (left) and Mumbai (right) with attendant road labels colored from 20 mph (yellow) to 65 mph (red) 2. Knowledge of the safe travel speed allows true optimal routing, since one can now minimize the travel time to any desired destinationįigure 1. We use these metadata features to infer the safe travel speed for each roadway. Labels for each roadway are hand drawn, and include metadata features such as surface type, road type (primary, secondary, highway, etc.), and number of lanes. The new cities of Moscow and Mumbai increase the diversity of road labels within the SpaceNet data corpus that now cover 4 continents, particularly given the dense urban nature of Mumbai and the snow present in Moscow (see Figure 1). These add to the existing corpus of labeled road datasets within SpaceNet with corresponding 30 cm imagery (bold cities are new): For SpaceNet 5 we publicly released imagery and road labels for two new training cities. Since its inception, a core feature of SpaceNet has been the release of high fidelity imagery in conjunction with high quality hand-curated labels. In this post we discuss the results of SpaceNet 5, along with details of the dataset and the heretofore unannounced final test city. Enter SpaceNet, where high resolution imagery, meticulous hand labeled datasets, and public prize challenges help illuminate the current state of the art in computer vision and data science as applied to satellite imagery and foundational mapping. The high revisit rates of existing and future satellite constellations have the potential to dramatically improve the response time for foundational mapping updates, provided such features can be extracted with high fidelity from satellite images.
#Spacenet 5 update
Current methods to update foundational mapping features such as road networks is often manually intensive and slow to update, even with large numbers of volunteers. The SpaceNet 5 Challenge, which sought to identify road networks and optimal travel times directly from satellite imagery, is complete! Inferring up-to-date road networks and optimal routing paths is essential to many challenges in the humanitarian, military, and commercial domains. SpaceNet is run in collaboration with CosmiQ Works, Maxar Technologies, Intel AI, Amazon Web Services (AWS), Capella Space, and Topcoder.
building footprint & road network detection). Preface: SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e. SpaceNet is a collaborative effort between CosmiQ Works, AWS, Maxar, and Intel AI.Announcing the Winners of the SpaceNet 5 Challenge
This public challenge will test participants’ ability to automatically extract road networks from satellite imagery, along with travel time estimates along all roadways, thereby permitting true optimal routing. In this Training_Data podcast we are joined by Ryan Lewis and Adam Van Etten to learn about SpaceNet’s upcoming fifth challenge that revisits road detection, routing, and now travel time estimation from satellite imagery. This statement is as true today as it was two years ago when the SpaceNet® partners announced the SpaceNet Challenge 3, focusing on road network detection. Determining optimal routing paths in near real-time is at the heart of many humanitarian, civil, military, and commercial challenges.
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