New aI Tool Generates Realistic Satellite Images Of Future Flooding

Visualizing the prospective impacts of a hurricane on people’s homes before it strikes can help residents prepare and choose whether to leave.

MIT researchers have actually established a technique that generates satellite images from the future to depict how a region would care for a possible flooding occasion. The technique combines a generative artificial intelligence model with a physics-based flood design to create realistic, birds-eye-view images of an area, showing where flooding is likely to happen given the strength of an oncoming storm.

As a test case, the team used the method to Houston and created satellite images portraying what specific locations around the city would look like after a storm equivalent to Hurricane Harvey, which struck the area in 2017. The team compared these generated images with actual satellite images taken of the very same areas after Harvey hit. They also compared AI-generated images that did not include a physics-based flood model.

The team’s physics-reinforced approach generated satellite images of future flooding that were more realistic and accurate. The AI-only technique, on the other hand, created pictures of flooding in locations where flooding is not physically possible.

The group’s approach is a proof-of-concept, implied to show a case in which generative AI designs can produce practical, credible material when coupled with a physics-based model. In order to apply the technique to other regions to depict 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 idea is: One day, we might use this before a typhoon, where it supplies an additional visualization layer for the general public,” says 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 most significant difficulties is encouraging individuals to evacuate when they are at risk. Maybe this might be another visualization to help increase that preparedness.”

To highlight the capacity of the brand-new approach, which they have dubbed the “Earth Intelligence Engine,” the group has made it offered as an online resource for others to attempt.

The scientists report their outcomes today in the journal IEEE Transactions on Geoscience and Remote Sensing. The study’s MIT co-authors include Brandon Leshchinskiy; Aruna Sankaranarayanan; and Dava Newman, teacher of AeroAstro and director of the MIT Media Lab; along with partners from multiple institutions.

Generative adversarial images

The new study is an extension of the team’s efforts to apply generative AI tools to visualize future .

“Providing a hyper-local perspective of environment appears to be the most effective way to communicate our clinical outcomes,” says Newman, the research study’s senior author. “People connect to their own postal code, their regional environment where their friends and family live. Providing regional climate simulations becomes user-friendly, individual, and relatable.”

For this research study, the authors utilize a conditional generative adversarial network, or GAN, a type of maker learning method that can create practical images using 2 competing, or “adversarial,” neural networks. The very first “generator” network is trained on pairs of genuine data, such as satellite images before and after a hurricane. The 2nd “discriminator” network is then trained to differentiate in between the genuine satellite images and the one synthesized by the first network.

Each network automatically improves its efficiency based on feedback from the other network. The idea, then, is that such an adversarial push and pull should ultimately produce artificial images that are identical from the genuine thing. Nevertheless, GANs can still produce “hallucinations,” or factually inaccurate features in an otherwise reasonable image that should not be there.

“Hallucinations can misinform viewers,” states Lütjens, who started to question whether such hallucinations might be prevented, such that generative AI tools can be trusted to help inform individuals, particularly in risk-sensitive situations. “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 scientists considered a risk-sensitive circumstance in which generative AI is entrusted with developing satellite images of future flooding that might be credible adequate to inform choices of how to prepare and potentially leave individuals out of harm’s way.

Typically, policymakers can get a concept of where flooding may happen based upon visualizations in the type of color-coded maps. These maps are the end product of a pipeline of physical models that generally begins with a cyclone track design, which then feeds into a wind design that replicates the pattern and strength of winds over a regional region. This is combined with a flood or storm rise model that anticipates how wind may push any neighboring body of water onto land. A hydraulic design then draws up where flooding will happen based upon the regional flood facilities and creates a visual, color-coded map of flood elevations over a specific region.

“The question is: Can visualizations of satellite imagery include another level to this, that is a bit more concrete and emotionally engaging than a color-coded map of reds, yellows, and blues, while still being trustworthy?” Lütjens says.

The group first evaluated how generative AI alone would produce satellite images 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 pictures of the same areas, they discovered that the images looked like typical satellite images, however a closer look revealed hallucinations in some images, in the kind of floods where flooding must not be possible (for example, in locations at higher elevation).

To lower hallucinations and increase the reliability of the AI-generated images, the group matched the GAN with a physics-based flood model that incorporates real, physical criteria and phenomena, such as an approaching hurricane’s trajectory, storm surge, and flood patterns. With this physics-reinforced technique, the group produced satellite images around Houston that portray the very same flood level, pixel by pixel, as anticipated by the flood design.