![]() The area of interest for this challenge will be centered over the largest port in Europe: Rotterdam, the Netherlands. ![]() This openly-licensed dataset features a unique combination of half-meter Synthetic Aperture Radar (SAR) imagery from Capella Space and half-meter electro-optical (EO) imagery from Maxar’s WorldView 2 satellite. ![]() The task of SpaceNet 6 is to automatically extract building footprints with computer vision and artificial intelligence (AI) algorithms using a combination of SAR and electro-optical imagery datasets. However, despite these advantages, there is limited open data available to researchers to explore the effectiveness of SAR for such applications, particularly at ultra-high resolutions. Overhead collects from SAR satellites could be particularly valuable in the quest to aid disaster response in instances where weather and cloud cover can obstruct traditional electro-optical sensors. She has a Masters in Public Relations from Boston University.Synthetic Aperture Radar (SAR) is a unique form of radar that can penetrate clouds, collect during all- weather conditions, and capture data day and night. She has also launched new company podcasts, “Training_Data” and the “IQT Podcast”, serving as content creator and producer, providing post-production edits, and creating and implementing a marketing posting strategy. She lead, designs, and implements company communication materials including corporate brochures, flyers, executive presentations, case studies, and internal initiatives. in physics from Stanford University and bachelor’s in physics and astronomy from the University of Washington.Ĭhristyn is the Director for Marketing & Communications at In-Q-Tel. Prior to joining CosmiQ Works, Adam was a Data Scientist for Data Tactics working at DARPA headquarters developing tools and scalable algorithms for big data analysis on a variety of projects. Adam also helps run the SpaceNet initiative and focuses on researching rapid computer vision techniques that readily scale to the enormous sizes of satellite imagery corpora. He applies machine learning and computer vision techniques to satellite imaging data, focusing on problems of interest to the U.S. Following an Insight Data Science Fellowship in Boston, Daniel joined CosmiQ Works in February 2019.Īdam is the Director of Research at CosmiQ Works. from the University of California, Berkeley, where he analyzed data from the LUX dark matter experiment. Originally from the Kansas City area, Daniel earned bachelor’s degrees in physics and mathematics from the University of Kansas, coauthoring papers in astroparticle physics, accelerator-based particle physics, and astrobiology. Recently, Daniel has started exploring the use of deep learning for interpreting synthetic aperture radar imagery. All of the datasets, code, papers, and evaluations are available at Register here.ĭaniel’s work focuses on data requirements for geospatial machine learning to better understand how much training data is really needed for different performance levels under various conditions. SpaceNet is co-founded and managed by In-Q-Tel’s CosmiQ in coordination with its co-founder Maxar Technologies and the other SpaceNet Partners: Amazon Web Services (AWS), Capella Space, TopCoder, the Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS), the National Geospatial-Intelligence Agency (NGA), and Planet. In this talk, members from SpaceNet will provide an overview of their previous work, a deep dive into some of the key findings from recent challenges, and discussion about emerging trends in the computer vision and geospatial domains. It is planning to launch its seventh public challenge in August featuring a deep time series dataset. Since its informal launch in 2016, SpaceNet has labeled and open sourced over 26,000 km2 of satellite imagery and synthetic aperture radar (SAR) data, structured and hosted six public data science challenges, and open sourced 28 deep learning algorithms from the challenges. SpaceNet LLC, a nonprofit organization dedicated to accelerating open source, applied computer vision research, have striven to direct more research and development towards remote sensing applications. The significant advances in image classification, object detection, and image segmentation have profound implications for a wide variety of geospatial applications, including foundational mapping. There has been exponential growth in computer vision research focused on deep learning techniques. SpaceNet: Building an Open Source Analytics Ecosystem for Geospatial Applications
0 Comments
Leave a Reply. |