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Prospective Students Current Students Faculty & Staff Alumni Industry Start your application today Undergraduate Admissions Graduate Admissions Dual Degree Program Graduate applicants: Attend an info session and skip the application fee Search Trending Searches graduate admissions academic programs financial aid academic calendar maps & directions summer school Home News & Events Interactive approach to geospatial search combines aerial imagery, reinforcement learning Interactive approach to geospatial search combines aerial imagery, reinforcement learning Framework for large-scale geospatial exploration developed by computer scientists Yevgeniy Vorobeychik, Nathan Jacobs and Anindya Sarkar Shawn Ballard  01.08.2024 Comparison of search pathway using visual active search (VAS) (left) and the most competitive state-of-the-art approach, greedy selection (right). The VAS framework developed by McKelvey engineers quickly learns to take advantage of visual similarities between regions. Share Share on Facebook Share on Twitter Share on Linkedin Email When combatting complex problems like illegal poaching and human trafficking, efficient yet broad geospatial search tools can provide critical assistance in finding and stopping the activity. A visual active search (VAS) framework for geospatial exploration developed by researchers in the McKelvey School of Engineering at Washington University in St. Louis uses a novel visual reasoning model and aerial imagery to learn how to search for objects more effectively. The team led by Yevgeniy Vorobeychik and Nathan Jacobs, professors of computer science & engineering, aims to shift computer vision &＃8211; a field typically concerned with how computers learn from visual information &＃8211; toward real-world applications and impact. Their cutting-edge framework combines computer vision with adaptive learning to improve search techniques by using previous searches to inform future searches. &＃8220;This work is about how to guide physical search processes when you&＃8217;re constrained in the number of times you can actually search locally,&＃8221; Jacobs said. &＃8220;For example, if you&＃8217;re only allowed to open five boxes, which do you open first? Then, depending on what you found, where do you search next?&＃8221; The team&＃8217;s approach to VAS builds on prior work by collaborator Roman Garnett, associate professor of computer science & engineering in McKelvey Engineering. It marries active search, an area in which Garnett did pioneering research, with visual reasoning and relies on teamwork between humans and artificial intelligence (AI). Humans perform local searches, and AI integrates aerial geospatial images and observations from people on the ground to guide subsequent searches. An overview of the VAS framework &＃160; The novel VAS framework comprises three key components: an image of the entire search area, subdivided into regions; a local search function, determining if a specific object is present in a given region; and a fixed search budget, limiting the number of times the local search function can be executed. The goal is to maximize the detection of objects within the allocated search budget, and indeed the team found that their approach outperforms all baselines.&＃160; First author Anindya Sarkar, a doctoral student in Vorobeychik&＃8217;s lab, presented the findings Jan. 6 at the Winter Conference on Applications of Computer Vision in Waikoloa, Hawaii. &＃8220;Our approach uses spatial correlation between regions to scale up and adapt active search to be able to cover large areas,&＃8221; Sarkar said. &＃8220;The interactive nature of the framework &＃8211; learning from prior searches &＃8211; had been suggested, but updating the underlying model was very costly and ultimately not scalable for a large visual space. Scaling up with lots of image data, that&＃8217;s the big contribution of our new VAS method. To do that, we&＃8217;ve shifted the underlying fundamentals compared to previous techniques.&＃8221; Looking ahead, the team anticipates exploring ways to expand their framework for use in a wide variety of applications, including specializing the model for different domains ranging from wildlife conservation to search and rescue operations to environmental monitoring. They recently presented a highly adaptable version of their search framework that can produce a maximally efficient search even when the object sought varies drastically from the objects the model is trained on.&＃160; &＃8220;The world looks different in different places, and people will want to search for different things,&＃8221; Jacobs said. &＃8220;Our framework needs to be able to adapt to both of those considerations to be effective as a basis for a search method. We especially want the tool to be able to learn and adapt on the fly, since we won&＃8217;t always know what we&＃8217;re looking for or at in advance.&＃8221; Sarkar A, Lanier M, Alfeld S, Feng J, Garnett R, Jacobs N, and Vorobeychik Y. A visual active search framework for geospatial exploration. Winter Conference on Applications of Computer Vision (WACV), Jan. 4-8, 2024. https://doi.org/10.48550/arXiv.2211.15788 This research was partially supported by the National Science Foundation (IIS-1905558, IIS-1903207, and IIS-2214141), Army Research Office (W911NF-18-1-0208), Amazon, NVIDIA, and the Taylor Geospatial Institute. &＃160; The McKelvey School of Engineering at Washington University in St. Louis promotes independent inquiry and education with an emphasis on scientific excellence, innovation and collaboration without boundaries. McKelvey Engineering has top-ranked research and graduate programs across departments, particularly in biomedical engineering, environmental engineering and computing, and has one of the most selective undergraduate programs in the country. With 165 full-time faculty, 1,420 undergraduate students, 1,614 graduate students and 21,000 living alumni, we are working to solve some of society’s greatest challenges; to prepare students to become leaders and innovate throughout their careers; and to be a catalyst of economic development for the St. Louis region and beyond. Click on the topics below for more stories in those areas Research Computer Science & Engineering Back to News Faculty in this story View Profile Yevgeniy Vorobeychik Professor View Profile Roman Garnett Associate Professor View Profile Nathan Jacobs Professor You may also be interested in: Advancing robot autonomy in unpredictable environments Yiannis Kantaros will enable teams of robots to interact collaboratively, perceive and respond to their environment with a CAREER Award from the National Science Foundation. 06.10.2024 DEMIST artificial intelligence tool may enhance usability of medical images A deep-learning-based image denoising method developed by Abhinav Jha may improve detection of myocardial defects in low-count SPECT scans. 06.04.2024 Quantum physics may help lasers see through fog, aid in communications JT Shen to pioneer two-color quantum photonic laser with DARPA grant. 06.04.2024 Facebook Twitter LinkedIn Instagram YouTube Engineering Departments Biomedical Engineering Computer Science & Engineering Division of Engineering Education Electrical & Systems Engineering Energy, Environmental & Chemical Engineering Mechanical Engineering & Materials Science Sever Institute - professional degrees Technology & Leadership Center - training for industry Contact Us Washington University in St. Louis McKelvey School of Engineering MSC: 1100-122-303 1 Brookings Drive St. Louis, MO 63130-4899 Contact Us Resources COVID-19 Resources Canvas Directory Equity, Diversity & Inclusion Emergency Management Engineering IT Maps & Directions Make a Gift WebFAC / WebSTAC ©2024 Washington University in St. Louis. 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