![]() The single most important SpaceNet pillar is the development and release of highly curated, labeled remote sensing datasets. SpaceNet has focused on four strategic pillars since its founding in 2016. ![]() This approach has remained largely unchanged since SpaceNet’s founding because the combined output provides practitioners and researchers alike a comprehensive resource spanning the entire supervised ML model lifecycle. SpaceNet’s strategy for developing and maintaining an open-source geospatial analytics ecosystem has four pillars: (1) develop and publish highly curated, labeled remote sensing datasets (2) design and host public data science challenges targeting a specific foundational mapping problem (3) open source release of the leading algorithms from each challenge and (4) conduct detailed evaluations of ML approaches to difficult geospatial problems. A comprehensive timeline of SpaceNet’s activities over the years.įour Pillar Strategy: Building an Ecosystem The Advisory Council is co-chaired by CosmiQ and Maxar. Each partner serves on the SpaceNet Advisory Council, which is responsible for designing and implementing datasets and challenges. There are currently eight 2019–2020 SpaceNet Partners: CosmiQ, Maxar Technologies, AWS, Capella Space, Topcoder, the IEEE GRSS, National Geospatial-Intelligence Agency (NGA), and Planet. In 2018, we formed SpaceNet into an official nonprofit LLC, solely managed by CosmiQ, to better manage the growth in partnerships and community engagement. We have relied upon forming partnerships with leading commercial, nonprofit, academic and government organizations in order to gain access to datasets and expertise, as well as build awareness in both the geospatial and computer vision communities. SpaceNet was founded to address this gap by accelerating the development of open-source ML capabilities for geospatial applications, specifically the foundational mapping mission. The differences inherent in remote sensing data types such as satellite and aerial imagery present unique challenges for leveraging the latest computer vision techniques. Yet a majority of the initial advancements in open-source computer vision technologies have been built using labeled common photograph datasets such as ImageNet and Common Objects in Context (COCO). As SpaceNet officially turns four years old this month, it seems only fitting to use this milestone as an opportunity to reflect upon our progress to date, discuss several lessons learned and consider the potential paths ahead.Īs discussed frequently throughout this blog publication and on our podcast series, Training_Data, we believe rapidly maturing ML technologies, specifically computer vision, will fundamentally disrupt current geospatial analytics products and services. Although the initial project plan was to release a single, labeled, satellite imagery dataset and host a public data science competition featuring that data, the collaboration has grown significantly through the steady addition of new partner organizations, labeled datasets, public challenges, open source algorithms and detailed evaluations. It was amidst this different but not necessarily simpler time that IQT’s CosmiQ Works (“CosmiQ”) and Maxar Technologies (then DigitalGlobe) launched SpaceNet, a collaborative initiative designed to encourage the development of open-source, machine learning (ML) techniques for geospatial applications. Netflix launched a new series called Stranger Things, the 2016 Summer Olympics were held in Rio de Janeiro (on schedule) and the world continued its battle against another virus: Zika. Despite this challenge, it is worth recalling the summer of 2016 for the sake of this conversation. In April, which itself seems rather long ago, The Atlantic likened our new daily, work-from-home routines to a bad version of the 1993 cult classic movie Groundhog Day (1). It can be difficult to recall the details of the past few days, much less the past few years, given our current socially distanced and quarantined lives. ![]() SpaceNet is solely managed by co-founder, In-Q-Tel CosmiQ Works, in collaboration with co-founder and co-chair, Maxar Technologies, and the other partners: Amazon Web Services (AWS), Capella Space, Topcoder, Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS), the National Geospatial-Intelligence Agency (NGA) and Planet. building footprint and 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.
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