![]() 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. Total training and inference times are calculated based upon an AWS p3.Ritwik Gupta*, Colorado Reed*, Anja Rohrbach, and Trevor Darrellįigure 1: Airmass measurements (clouds) over Ukraine from FebruMafrom the SEVIRI instrument. Note that the total contribution to the total NN’s ensembled is listed in parentheses in the Architectures column. The model architectures, ensemble and pre-training schemes, as well as training and inference time for each of the winning solutions. We also report model precision (ratio of false predictions) and recall (ratio of missed ground truth polygons): The overall score represents the SpaceNet Metric (x 100) for the entire scoring set. See the blog post on CosmiQ Works' blog The DownlinQ for an additional summary.Ĭompetitors’ scores in the SpaceNet 6: Multi-Sensor All Weather Mapping Challenge compared to the baseline model. Each subdirectory contains the competitors' written descriptions of their solution to the challenge. The five subdirectories in this repository comprise the code for the winning solutions of SpaceNet 6: Multi-Sensor All Weather Mapping Challenge hosted by TopCoder. ![]() ![]() SpaceNet 6: Multi-Sensor All Weather Mapping Competitor Solutions
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