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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 Artificial intelligence meets cartography Artificial intelligence meets cartography Graduate students in Nathan Jacobs’ lab presented mapping tools to create satellite images from text prompts at EarthVision 2024 Shawn Ballard  06.17.2024 Satellite images can be synthesized using GeoSynth based on given text prompts. In this example, the model uses a street map to determine the layout and the text prompt “city after earthquake” to fill in the style details of the created satellite image. (Image: Srikumar Sastry) Share Share on Facebook Share on Twitter Share on Linkedin Email Most people interact with maps regularly, for example, when they&＃8217;re trying to get from point A to point B, track the weather or plan a trip. But, beyond those daily activities, maps are also increasingly being combined with artificial intelligence to create powerful tools for urban modeling, navigation systems, natural hazard forecasting and response, climate change monitoring, virtual habitat modeling, and other kinds of surveillance. &＃8220;Maps are a fundamental product in our life,&＃8221; says Aayush Dhakal, a graduate student in the McKelvey School of Engineering at Washington University in St. Louis. &＃8220;They allow us to learn patterns and see distributions across geospatial area.&＃8221; Dhakal and Srikumar Sastry, also a McKelvey Engineering graduate student, are working with Nathan Jacobs, professor of computer science & engineering, to develop models that use satellite imagery to support these endeavors. Dhakal&＃8217;s project, Sat2Cap, allows users to create maps from free-form textual descriptions. Sastry developed GeoSynth, a model for synthesizing satellite images based on a given textual prompt or geographic location. Dhakal and Sastry presented their work at this year&＃8217;s June 17 EarthVision workshop in Seattle in conjunction with the Computer Vision and Pattern Recognition 2024 Conference. EarthVision aims to advance machine learning-based analysis of remote sensing data with particular attention to urgent challenges and applications, such as monitoring natural hazards, urban growth, deforestation and climate change. Mapping text from satellite images Creating a map can be a time-consuming process. A would-be cartographer must collect all the relevant data for the region of interest, then carefully plot it to produce an accurate map. Dhakal developed Sat2Cap as a solution to this &＃8220;tedious and not scalable&＃8221; map-making process. The paper won the Best Paper Award at the workshop. &＃8220;Our model allows us to create maps of any concept that is expressed using text over a large geographic region,&＃8221; Dhakal said. &＃8220;We contrastively trained a model that takes as input a satellite image over a location and learns to predict meaningful textual representation for that location.&＃8221; The tricky part, Dhakal says, is large-scale data collection. Based on many satellite images &＃8211; Dhakal used 6 million data points to train Sat2Cap &＃8211; the model can produce a map showing likely locations for a given text query. For example, say the model has lots of images of the United States. If you give it the text prompt, &＃8220;amusement parks,&＃8221; the model will produce a map showing the most likely locations that contain amusement parks across the U.S. &＃8220;We describe this process as &＃8216;zero-shot mapping&＃8217;, where you can create maps of never-before-seen concepts, as opposed to laborious data collection,&＃8221; Dhakal said. &＃8220;People might use this tool to map concepts for which data is not yet collected or available. The ability to interact with our model using &＃8216;natural human language&＃8217; also makes it much more friendly and flexible.&＃8221; High-resolution satellite image synthesis Generative artificial intelligence has gotten a lot of hype lately, but just how capable are generative models? &＃8220;Generating satellite images is much more difficult than generating single-subject images like dogs and cats,&＃8221; Sastry said. With GeoSynth, he set out to see how well generative models could perform when trained on geographic location data. &＃8220;The key obstacle was to condition the diffusion model on geographic location to learn a region's high-level geography,&＃8221; Sastry said. &＃8220;For example, when told to generate an image from Phoenix, the model should generate a desert-looking image. On the other hand, for Des Moines, the model should generate more greenish and farm-like images.&＃8221; The resulting GeoSynth model displays zero-shot satellite image generation capability. Given a text prompt or geographic location, the model can produce satellite images ranging from flooded cities to island resorts, scenes of post-earthquake destruction to arctic civilizations. Notably, these images are distinct from the kinds of images seen in the training dataset.&＃160; &＃8220;Imagine a scenario where you describe a scene and a layout and suddenly a realistic satellite image blooms into existence,&＃8221; Sastry said. &＃8220;GeoSynth can do that. The model could be used for planning cities, augmenting existing remote sensing datasets or as a generative tool, similar to DALLE-3 or Midjourney.&＃8221; Dhakal A, Ahmad A, Khanal S, Sastry S, Kerner H, Jacobs N. Sat2Cap: Mapping fine-grained textual descriptions from satellite images. EarthVision workshop at the Computer Vision and Pattern Recognition (CVPR) 2024 Conference, June 17, 2024. https://arxiv.org/pdf/2307.15904 Sastry S, Khanal S, Dhakal A, Jacobs N. GeoSynth: Contextually aware high-resolution satellite image synthesis. EarthVision workshop at the Computer Vision and Pattern Recognition (CVPR) 2024 Conference, June 17, 2024. https://arxiv.org/pdf/2404.06637 Click on the topics below for more stories in those areas Graduate Students Research Computer Science & Engineering Back to News Faculty in this story View Profile Nathan Jacobs Professor You may also be interested in: Can weak human supervision train superhuman models? Chien-Ju Ho won a grant from OpenAI to study weak human supervision in training powerful machine learning models. 06.13.2024 Imaging method shows promise for personalized cancer treatment Abhinav Jha and collaborators conduct a computer-based trial of a novel technique to reliably estimate dose of radiopharmaceutical therapy. 06.13.2024 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 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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