For example, the advancements in road extraction and optimized routing resulting from SpaceNet Challenges 3 and 5 connect immediately to numerous national security missions. Demonstrated that open-source challenges provide value for the U.S.Before we go, we want to celebrate highlights and accomplishments that SpaceNet produced so far:
#SPACENET 5 CODE#
SpaceNet resources (data, code and website) will continue to be openly available. With CosmiQ Works’ exit, leadership of SpaceNet will be transitioned to Maxar. In March 2021, IQT Labs and CosmiQ Works will step back from its leadership of SpaceNet and focus resources on new initiatives.
#SPACENET 5 FULL#
This blog is an abridged version of the full post appearing on The DownLinQ. Among other applications, these challenges inform a number of humanitarian and national security use cases where maps may be outdated or lacking. CosmiQ Works ran the organization for the past five years and managed seven unique data science challenges focused on foundational mapping (building footprints and road networks). Over time, SpaceNet grew into a robust collaboration among co-founder and managing partner CosmiQ Works, co-founder Maxar Technologies, and six valuable partners. If you have any questions, please reach out through the Topcoder Forum ( ).Founded by IQT Labs’ CosmiQ Works with Maxar Technologies in 2016, SpaceNet began as an informal collaboration to accelerate open-source machine learning capabilities specifically for geospatial use cases. To further aid competitors, the SpaceNet 5 baseline is fully open source, and yields a score of 54. The first 20 competitors to reach a score of 50 (out of a possible 100) received a credit for 10 hours on a p3.2xlarge for training and improving their models. For the first time in SpaceNet history, the final submissions were tested on a mystery city dataset that was revealed and open sourced at the end of the Challenge.
SpaceNet open sourced new data sets for the following cities: Moscow, Russia Mumbai, India and San Juan, Puerto Rico. You can find a detailed description of CosmiQ Works’ algorithmic baseline on their blog at The DownLinQ. The task of this challenge was to output a detailed graph structure with edges corresponding to roadways and nodes corresponding to intersections and end points, with estimates for route travel times on all detected edges. The SpaceNet 5 challenge sought to build upon the advances from SpaceNet 3 and test challenge participants to automatically extract road networks and. The SpaceNet 5 challenge sought to build upon the advances from SpaceNet 3 and test challenge participants to automatically extract road networks and routing information from satellite imagery, along with travel time estimates along all roadways, thereby permitting true optimal routing. Satellite or aerial imagery often provides the first large-scale data in such scenarios, rendering such imagery attractive. This statement is as true today as it was two years ago when the SpaceNet Partners announced the SpaceNet Challenge 3 focused on road network detection and routing. In a disaster response scenario, for example, pre-existing foundational maps are often rendered useless due to debris, flooding, or other obstructions. Determining optimal routing paths in near real-time is at the heart of many humanitarian, civil, military, and commercial challenges. The SpaceNet Dataset is hosted as an Amazon Web Services (AWS) Public Dataset.
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