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Дата на основаване февруари 14, 1902
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Сектори Дизайн, Криейтив, Видео и Анимация
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New aI Tool Generates Realistic Satellite Images Of Future Flooding
Visualizing the possible impacts of a cyclone on people’s homes before it hits can assist residents prepare and choose whether to evacuate.
MIT scientists have developed an approach that produces satellite imagery from the future to portray how a region would care for a potential flooding event. The method combines a generative expert system design with a physics-based flood model to create realistic, birds-eye-view pictures of an area, revealing where flooding is likely to take place offered the strength of an approaching storm.
As a test case, the group applied the method to Houston and produced satellite images illustrating what specific places around the city would look like after a storm similar to Hurricane Harvey, which struck the region in 2017. The team compared these generated images with real satellite images taken of the very same regions after Harvey hit. They likewise compared AI-generated images that did not consist of a physics-based flood design.
The group’s physics-reinforced method created satellite images of future flooding that were more sensible and accurate. The AI-only approach, in contrast, produced images of flooding in locations where flooding is not physically possible.
The team’s approach is a proof-of-concept, meant to demonstrate a case in which generative AI models can generate sensible, trustworthy material when coupled with a physics-based model. In order to apply the technique to other areas to portray flooding from future storms, it will need to be trained on much more satellite images to find out how flooding would look in other regions.
„The concept is: One day, we might use this before a cyclone, where it supplies an extra visualization layer for the general public,“ states Björn Lütjens, a postdoc in MIT’s Department of Earth, Atmospheric and Planetary Sciences, who led the research while he was a doctoral student in MIT’s Department of Aeronautics and Astronautics (AeroAstro). „One of the greatest difficulties is motivating people to leave when they are at danger. Maybe this might be another visualization to assist increase that readiness.“
To highlight the capacity of the new approach, which they have actually called the „Earth Intelligence Engine,“ the group has actually made it readily available as an online resource for others to try.
The researchers report their outcomes today in the journal IEEE Transactions on Geoscience and Remote Sensing. The research study’s MIT co-authors consist of Brandon Leshchinskiy; Aruna Sankaranarayanan; and Dava Newman, teacher of AeroAstro and director of the MIT Media Lab; along with partners from numerous institutions.
Generative adversarial images
The new study is an extension of the group’s efforts to apply generative AI tools to picture future environment scenarios.
„Providing a hyper-local point of view of environment seems to be the most efficient method to communicate our scientific outcomes,“ says Newman, the research study’s senior author. „People connect to their own zip code, their regional environment where their household and friends live. Providing local climate simulations ends up being intuitive, personal, and relatable.“
For this study, the authors use a conditional generative adversarial network, or GAN, a kind of machine learning approach that can produce practical images utilizing two competing, or „adversarial,“ neural networks. The first „generator“ network is trained on sets of real information, such as satellite images before and after a typhoon. The 2nd „discriminator“ network is then trained to differentiate between the real satellite images and the one manufactured by the first network.
Each network automatically enhances its performance based on feedback from the other network. The concept, then, is that such an adversarial push and pull need to eventually produce artificial images that are equivalent from the real thing. Nevertheless, GANs can still produce „hallucinations,“ or factually incorrect features in an otherwise sensible image that should not exist.
„Hallucinations can misguide audiences,“ says Lütjens, who began to wonder whether such hallucinations might be avoided, such that generative AI tools can be trusted to help inform individuals, especially in risk-sensitive circumstances. „We were thinking: How can we use these generative AI designs in a climate-impact setting, where having trusted data sources is so essential?“
Flood hallucinations
In their new work, the researchers considered a risk-sensitive scenario in which AI is tasked with developing satellite images of future flooding that could be reliable sufficient to inform choices of how to prepare and potentially evacuate people out of harm’s method.
Typically, policymakers can get a concept of where flooding might happen based on visualizations in the type of color-coded maps. These maps are the end product of a pipeline of physical models that normally begins with a cyclone track design, which then feeds into a wind design that mimics the pattern and strength of winds over a local region. This is integrated with a flood or storm surge design that forecasts how wind may push any neighboring body of water onto land. A hydraulic model then draws up where flooding will take place based on the local flood facilities and creates a visual, color-coded map of flood elevations over a particular area.
„The concern is: Can visualizations of satellite imagery include another level to this, that is a bit more tangible and emotionally appealing than a color-coded map of reds, yellows, and blues, while still being trustworthy?“ Lütjens says.
The team first tested how generative AI alone would produce satellite pictures of future flooding. They trained a GAN on real satellite images taken by satellites as they passed over Houston before and after Hurricane Harvey. When they tasked the generator to produce new flood images of the very same regions, they found that the images looked like common satellite imagery, but a closer look revealed hallucinations in some images, in the form of floods where flooding should not be possible (for circumstances, in places at greater elevation).
To reduce hallucinations and increase the reliability of the AI-generated images, the group matched the GAN with a physics-based flood design that includes real, physical criteria and phenomena, such as an approaching cyclone’s trajectory, storm rise, and flood patterns. With this physics-reinforced approach, the group generated satellite images around Houston that depict the exact same flood level, pixel by pixel, as anticipated by the flood model.