![]() The TorontoCity Dataset torontocity contains high resolution 5-10cm aerial 4-channel imagery, and ∼ 700 km 2 of coverage building and roads are labeled at high fidelity (among other items), but the data has yet to be publicly released. ![]() Imagery is obtained via an aerial platform and is 3 or 4 channel and 5-10cm in resolution. For example, the ISPRS semantic labeling benchmark isprs_semĭataset contains high quality 2D semantic labels over two cities in Germany and covers a compact area of 4.8 km 2 There is a demonstrated need for both automated building footprint extraction as well as automated road network extraction, as the Humanitarian OpenStreetMap Team’s Tasking Manager hotosmĬurrently has scores of open tasks for both roads and buildings.Įxisting publicly available labeled overhead or satellite imagery datasets tend to be relatively small, or labeled with lower fidelity than desired for foundational mapping. The potential application of automated building footprint and road network extraction ranges from the determination of optimal aid station locations during an epidemic in under mapped locations, to the establishment of an effective logistical schema in a disaster stricken region. The ability to create a road network is an important map feature (particularly if one is able to use this road network for routing purposes), while building footprint extraction serves as a useful proxy for population density zhangpop, popnature, popplos1. The third challenge addressed another foundational geospatial intelligence problem, road network extraction. The first two SpaceNet challenges focused on building footprint extraction from satellite imagery. SpaceNet imagery, labels, evaluation metrics, and prize challenge results to The first two of these competitionsįocused on automated building footprint extraction, and the most recentĬhallenge focused on road network extraction. The SpaceNet partners also launchedĪ series of public prize competitions to encourage improvement of remote Solutions, and NVIDIA), released a large corpus of labeled satellite imagery onĪmazon Web Services (AWS) called SpaceNet. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Quickly revise foundational maps when combined with advanced machine learning We propose that the frequent revisits ofĮarth imaging satellite constellations may accelerate existing efforts to Requiring a large number of human labelers to either create features or Modifying maps is currently a highly manual process Particularly during dynamic scenarios such as natural disasters when timely The competition will challenge developers to improve performance from the first competition using the higher-resolution imagery and more geographically diverse training data samples.Foundational mapping remains a challenge in many parts of the world, The next phase of the SpaceNet Challenge will be a follow-on competition using DigitalGlobe’s 30 cm imagery from WorldView 3 and building footprints across new locations around the globe. The Rio geodatabase contains 12 datasets with 35 unique layers containing more than 120,000 individual points of interest. National Geospatial-Intelligence Agency, which licensed the dataset produced by DigitalGlobe. This data is made available through the participation of the U.S. “We are really thrilled by the developers’ level of engagement to use DigitalGlobe imagery, training data, and open-source code to create innovative algorithms,” said Tony Frazier, senior vice president and general manager of services at DigitalGlobe.Ī newly released Points of Interest (POI) dataset for Rio de Janeiro is now freely available to the public via SpaceNet on AWS. The winning algorithms will be made available to the open-source community through the SpaceNet GitHub repository and users of DigitalGlobe’s Geospatial Big Data platform (GBDX). The participants submitted 242 solutions over a three-week period to compete for a total prize pool of $35,000 that was awarded to the top five performing contestants. SpaceNet is a collaboration between DigitalGlobe, CosmiQ Works and NVIDIA, which consists of an online repository of freely available satellite imagery, co-registered map layers to train algorithms, and public challenges that aim to accelerate innovation in machine learning.ĭigitalGlobe launched the first SpaceNet Challenge in November 2016, and 42 developers competed in an open challenge hosted by TopCoder to create algorithms that extract building footprints from satellite imagery. DigitalGlobe announced the results of the first SpaceNet Challenge, which will release openly licensed satellite imagery of Rio de Janeiro taken from the WorldView 2 satellite at 50cm Ground Sample Distance (GSD) using eight spectral bands.
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