We also released code for our CRESI public baseline model (explored in a number of previous blogs), and scored this model on the public testing data (under the username cosmiq_baseline). We score with the APLS metric, weighted to optimize travel time. Robustness of algorithms to new and potentially unseen geographies is crucial, so for the SpaceNet 5 public leaderboard we elected to score challenge contestants on a composite metric of 20% each for Moscow and Mumbai, and 60% on a new city without any training data: San Juan, Puerto Rico. For satellite imagery, performance of algorithms varies markedly across geographies, as noted in previous SpaceNet competitions and CosmiQ’s recent robustness study. Thus, the submitted algorithms may work well on the provided data, yet break down when applied to slightly different data. One of the critiques of public data science challenges is that solutions can sometimes be hyper-tuned and overtrained for the provided datasets. 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. 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. Announcing the Winners of the SpaceNet 5 Challenge
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