In this post, we focus on issues #1 and #2. Since the goal of the challenge is to reproduce these centerline labels, the variance in road structure presents a challenge. The SpaceNet road network was created in such a fashion that only centerlines are labeled, regardless of roadway size. All roads are not created equal, as lane widths, number of lanes, and road surface all vary widely between differing geographic locales.Vector labels are rarely compatible with machine learning architectures, particularly in the computer vision realm.(We recommend QGIS to view raw SpaceNet data.) Few programs are designed to display 16-bit imagery, necessitating conversion.The SpaceNet imagery is distributed as 16-bit imagery, and the road network is distributed as a line-string vector GeoJSON format, which poses a few challenges: Remote sensing data is often formatted in a bespoke manner, which greatly complicates this process. One of the primary challenges of working with and eventually classifying remote sensing datasets is creating training data for ingestion into machine learning workflows. In previous posts we detailed the evaluation metric for this challenge, and more recently we described the SpaceNet Roads Dataset in this post we detail the steps to get started working with the roads dataset, with attendant code hosted on the APLS github page. Such automated processes may help improve a vast array of problems, from the mundane (traffic) to the extreme (mass evacuation). Stay tuned to our website and social media channels for updates.The SpaceNet Road Detection and Routing Challenge aims to automatically extract road networks directly from high-resolution satellite imagery. 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.
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