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New aI Tool Generates Realistic Satellite Images Of Future Flooding

Visualizing the prospective effects of a cyclone on people’s homes before it hits can help citizens prepare and decide whether to leave.

MIT scientists have established a method that creates satellite imagery from the future to portray how a region would care for a possible flooding event. The method combines a generative expert system design with a physics-based flood design to develop sensible, birds-eye-view pictures of an area, showing where flooding is most likely to occur given the strength of an oncoming storm.

As a test case, the group used the technique to Houston and produced satellite images depicting what particular areas around the city would appear like after a storm similar to Hurricane Harvey, which struck the region in 2017. The team compared these produced images with actual satellite images taken of the same regions after Harvey struck. They also compared AI-generated images that did not consist of a physics-based flood model.

The team’s physics-reinforced approach produced satellite images of future flooding that were more practical and precise. The AI-only method, in contrast, generated pictures of flooding in places where flooding is not physically possible.

The group’s method is a proof-of-concept, suggested to demonstrate a case in which generative AI models can produce practical, reliable material when paired with a physics-based model. In order to apply the method to other areas to illustrate flooding from future storms, it will require to be trained on much more satellite images to learn how flooding would look in other areas.

“The concept is: One day, we could utilize this before a hurricane, where it supplies an additional 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). “Among the biggest obstacles is motivating people to evacuate when they are at risk. Maybe this could be another visualization to assist increase that readiness.”

To illustrate the capacity of the new method, which they have actually called the “Earth Intelligence Engine,” the group has made it available as an online resource for others to attempt.

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; in addition to partners from multiple institutions.

Generative adversarial images

The new research study is an extension of the team’s efforts to apply generative AI tools to picture future climate circumstances.

“Providing a hyper-local perspective of environment appears to be the most reliable method to interact our clinical results,” says Newman, the study’s senior author. “People relate to their own postal code, their local environment where their friends and family live. Providing local climate simulations ends up being instinctive, personal, and relatable.”

For this study, the authors use a conditional generative adversarial network, or GAN, a kind of artificial intelligence technique that can create reasonable images utilizing 2 competing, or “adversarial,” neural networks. The first “generator” network is trained on sets of genuine data, such as satellite images before and after a hurricane. The second “discriminator” network is then trained to compare the real satellite imagery and the one manufactured by the very first network.

Each network automatically enhances its efficiency based on feedback from the other network. The idea, then, is that such an adversarial push and pull need to ultimately produce artificial images that are indistinguishable 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 deceive viewers,” states Lütjens, who started to question whether such hallucinations might be avoided, such that generative AI tools can be trusted to help inform people, particularly in risk-sensitive situations. “We were thinking: How can we utilize these generative AI designs in a climate-impact setting, where having trusted information sources is so essential?”

Flood hallucinations

In their new work, the researchers considered a risk-sensitive situation in which generative AI is entrusted with producing satellite pictures of future flooding that could be trustworthy sufficient to notify choices of how to prepare and potentially evacuate individuals out of damage’s method.

Typically, policymakers can get a concept of where flooding might happen based on visualizations in the kind of color-coded maps. These maps are the end product of a pipeline of physical designs that normally begins with a typhoon track model, which then feeds into a wind design that simulates the pattern and strength of winds over a local region. This is integrated with a flood or storm rise design that anticipates how wind might push any neighboring body of water onto land. A hydraulic model then maps out where flooding will happen based on the local flood facilities and generates a visual, color-coded map of flood elevations over a particular area.

“The concern is: Can visualizations of satellite imagery add another level to this, that is a bit more tangible and mentally interesting than a color-coded map of reds, yellows, and blues, while still being trustworthy?” Lütjens says.

The group first tested how generative AI alone would produce satellite images of future flooding. They trained a GAN on actual satellite images taken by satellites as they passed over Houston before and after Hurricane Harvey. When they entrusted the generator to produce new flood images of the exact same regions, they discovered that the images resembled common satellite imagery, however a closer look revealed hallucinations in some images, in the kind of floods where flooding should not be possible (for example, in locations at higher elevation).

To reduce hallucinations and the trustworthiness of the AI-generated images, the group matched the GAN with a physics-based flood design that incorporates genuine, physical parameters and phenomena, such as an approaching cyclone’s trajectory, storm surge, and flood patterns. With this physics-reinforced approach, the group generated satellite images around Houston that portray the same flood degree, pixel by pixel, as anticipated by the flood design.