![]() Machine learning (ML) on EO imagery is used in a wide variety of scientific and commercial applications. EO imagery is commonplace-anyone who has used Google Maps or similar mapping software has interacted with EO satellite imagery. The most widely used form of remote sensing data is electro-optical (EO) satellite imagery. With hundreds of terabytes of data being downlinked from satellites to data centers every day, gaining knowledge and actionable insights from that data with manual processing has already become an impossible task. Storing this information, let alone understanding, is an engineering challenge that is of growing urgency. We have rapidly transitioned from having very little information to now having more data than we can meaningfully extract knowledge from. With the amount of satellites in orbit today, our understanding of the environment is updated almost daily. Conversely, the proper and responsible surveillance has allowed us to learn deep truths about our world which have resulted in advances in the scientific and humanitarian domains. Historically, surveillance without checks and balances has been detrimental to society. As with any tool, it has been a double-edged sword. Vigilance, or to the French, surveillance, has been a part of human history for millenia. Maintaining a constant state of vigilance has been a goal of mankind since we were able to conceive such a thought, all the way from when Nadar took the first aerial photograph to when Sputnik 1’s radio signals were used to analyze the ionosphere. Surveyors were sent out to explore our new reality, and their distributed findings were often noisily integrated into a source of reality. Understanding this change has historically been difficult. We live in a rapidly changing world, one that experiences natural disasters, civic upheaval, war, and all sorts of chaotic events which leave unpredictable-and often permanent-marks on the face of the planet. ![]() In this post, we present a baseline method and pretrained models that enable the interchangeable use of RGB and SAR for downstream classification, semantic segmentation, and change detection pipelines. Improving the access to and availability of SAR-specific methods, codebases, datasets, and pretrained models will benefit intelligence agencies, researchers, and journalists alike during this critical time for Ukraine. This leads to suboptimal performance on this critical modality. Automating this tedious task would enable real-time insights, but current computer vision methods developed on typical RGB imagery do not properly account for the phenomenology of SAR. Synthetic Aperture Radar (SAR) imagery penetrates cloud cover, but requires special training to interpret. With Ukraine experiencing a large amount of cloud cover and attacks often occuring during night-time, many forms of satellite imagery are hindered from seeing the ground. Military strategists, journalists, and researchers use this imagery to make decisions, unveil violations of international agreements, and inform the public of the stark realities of war. ![]() Satellite imagery is a critical source of information during the current invasion of Ukraine. Network extraction.Ritwik Gupta*, Colorado Reed*, Anja Rohrbach, and Trevor Darrellįigure 1: Airmass measurements (clouds) over Ukraine from FebruMafrom the SEVIRI instrument. The first two of these competitions focused on automatedīuilding footprint extraction, and the most recent challenge focused on road Public prize competitions to encourage improvement of remote sensing machine The SpaceNet partners also launched a series of NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Satellite constellations may accelerate existing efforts to quickly updateįoundational maps when combined with advanced machine learning techniques.Īccordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and We propose that the frequent revisits of earth imaging Large number of human labelers to either create features or rigorously validateĪutomated outputs. Updating maps is currently a highly manual process requiring a Particularly in dynamic scenarios such as natural disasters when timely updatesĪre critical. Download a PDF of the paper titled SpaceNet: A Remote Sensing Dataset and Challenge Series, by Adam Van Etten and 2 other authors Download PDF Abstract: Foundational mapping remains a challenge in many parts of the world,
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