#SPACENET CHALLENGE SERIES#

Introducing the SpaceNet Off-Nadir Imagery DatasetĪ Baseline Model for the SpaceNet Off-Nadir Building Detection ChallengeĬhallenges with the SpaceNet 4 off-nadir satellite imagery For more details about the challenge (or if you’re unfamiliar with that terminology), check out these earlier posts describing the competition: The data comprised both South-facing and North-facing collects with look angle ranging from 7 degrees off-nadir to a whopping 54 degrees off-nadir. In Round 4 of the SpaceNet Challenge we asked competitors to identify building footprints from 27 different satellite collects taken during a single pass of a WorldView 2 satellite over Atlanta.

#SPACENET CHALLENGE SERIES#

This is the first post in a series where we’ll dig into the competitors’ solutions to find out how they applied machine learning to identify buildings in overhead imagery taken at look angles from 7 to 54 degrees off-nadir. Their solutions represented a 1.5-fold improvement over our initial baseline model’s performance. Check out this GitHub repository for their solution algorithm code. The SpaceNet Challenge: Round 4, Off-Nadir Building Footprint Extraction hosted by TopCoder is complete! Congratulations to cannab, selim_sef, MaksimovKA, number13, and XD_XD for taking home the top 5 prizes. SpaceNet’s mission is to accelerate geospatial machine learning and is supported by the SpaceNet LLC member organizations. Stay tuned to our website and social media channels for updates.Off-nadir imagery of Atlanta, GA. Maxar continues to engage the SpaceNet partners to plan the next phase of the project and future challenges. Maxar believes that AI enables huge benefits for the speed and scale at which we can serve important missions, such as rapid mapping after natural disasters, maintaining updated maps globally and understanding our changing planet.” “We are grateful for IQT Labs’ outstanding leadership of SpaceNet, and Maxar is thrilled to have the opportunity to lead the next phase of innovation. ”SpaceNet has become the gold standard for geospatial computer vision reference datasets and has fostered open innovation for algorithms to automate the extraction of foundation mapping features,” said Tony Frazier, Executive Vice President of Global Field Operations at Maxar. During this planning period, rest assured that all existing SpaceNet resources, including training datasets, tools and algorithms, will remain available. We’re excited to explore the application of AI to new sources of imagery, 3D data and other sources of geospatial data that can help us better understand our planet. We are pleased to share that we plan to host SpaceNet 8 in the second half of 2021 we will share details when possible. We look forward to continuing SpaceNet’s impressive track record of open innovation as the geospatial community takes on new mission challenges. Maxar commends the significant contributions of the CosmiQ Works team for managing this project for the past five years, and we’re grateful for the collaboration. Under CosmiQ Works' leadership, SpaceNet completed seven challenges, developed 36 open-sourced algorithms, had participation from more than 80 countries and released about 67,000 sq km of high-resolution satellite imagery, from which 11 million labeled building footprints and 20,000 km of road labels were mapped. SpaceNet is dedicated to accelerating open-source, AI-applied research for geospatial applications, specifically foundational mapping (i.e., building footprint and road network detection). Today, we are excited to announce a leadership transition of the SpaceNet project to Maxar. Maxar co-founded SpaceNet with IQT Labs’ CosmiQ Works five years ago to harness open-source innovations in emerging computer vision technology to accelerate complex analysis of satellite imagery. From smart speakers to facial recognition, artificial intelligence (AI) is increasingly part of our daily lives.

Spacenet challenge