How AI Improves Natural Disaster Recovery
When natural catastrophes strike, the immediate need is to save the local inhabitants and restore infrastructure to a functional state, especially power, fresh water, and healthcare.
This is becoming a growing problem, with the bill for such events ever growing in the past decade, driven by climate change and a growing population.
Source: Spire
However, true recovery of a community takes a lot longer, with assessing damages and then rebuilding taking months, if not years.
Often, this is delayed by the sheer overwhelming load of problems to identify and properly assess. Individual inspection of each building can take a lot of time, especially if insurance companies, emergency services, and other stakeholders are short on personnel.
“Manual field inspections are labor-intensive and time-consuming, often delaying critical response efforts.”
Abdullah Braik – Civil engineering doctoral student at Texas A&M
This could now change thanks to AI technology. Two researchers at Texas A&M University have developed an AI system using remote sensing, deep learning, and restoration models to predict accurately in less than one hour tornado damage assessments and recovery.
This method could help better organize recovery efforts, and was published in Sustainable Cities and Society1, under the title “Post-tornado automated building damage evaluation and recovery prediction by integrating remote sensing, deep learning, and restoration models”.
“Our method uses high-resolution sensing imagery and deep learning algorithms to generate damage assessments within hours, immediately providing first responders and policymakers with actionable intelligence.”
Abdullah Braik – Civil engineering doctoral student at Texas A&M
Tornado Devastation: A Growing Threat in the U.S.
When unleashed, nature can be devastating for people and the cities they live in. One such example is tornadoes, a relatively regular occurrence in many parts of the world, including the so-called Tornado Alley in the USA.
Source: Weather and Radar
For example, in spring 2011, Joplin, Missouri, was devastated by an EF5 tornado with estimated winds exceeding 200 mph (321 km/h). The storm killed 161 people, injured over 1,000, and damaged and destroyed around 8,000 homes and businesses. The tornado carved a mile-wide path through the densely populated south-central area of the city, leaving behind miles of splintered rubble and causing over $2 billion in damage.
Just this month, deadly tornadoes destroyed many houses and caused major damage in the Midwest and the South of the USA.
It will likely take a lot of time to fully grasp the full scale of the damage these tornadoes caused. And this is where the Texas A&M University researchers think they can help.
Challenges in Post-Tornado Damage Assessment
After such an event, it is the standard procedure to have an array of first responders, city planners, and insurance experts move in to assess the destruction and how to react to it.
This is especially true with tornadoes, as the wind and heavy things lifted by them can destroy even the strongest buildings.
“Post-disaster damage surveys are typically time-consuming and labor-intensive, primarily focused on continuous model and code refinement rather than immediate and rapid updates.”
Some tentative steps have been made to use existing data to analyze the post-catastrophe damages, notably Geographic Information Systems (GIS), which bring together multiple layers of data about a given location.
But the way this data is used is often insufficient to provide accurate evaluations. It also requires a lot of manual interventions and human judgment to be turned into usable metrics. This is where adding extra information and AI can help.
AI-Powered Model for Damage Evaluation and Recovery
Merging Existing Resources With AI
The researchers combined 3 different tools together: remote sensing, deep learning, and restoration modeling.
To better evaluate the immediate damages and changes after the catastrophe, they used remote sensing like high-resolution satellite or aerial images.
“These images are crucial because they offer a macro-scale view of the affected area, allowing for rapid, large-scale damage detection.”
Abdullah Braik – Civil engineering doctoral student at Texas A&M
Then, deep learning methods were used to automatically analyze these images to identify the severity of the damage accurately.
The AI was trained on thousands of images of previous disasters and learned to recognize visible signs of damage such as collapsed roofs, missing walls, and scattered debris. It then classifies every building into categories like no damage, moderate damage, major damage, or destroyed.
The last element is restoration modeling. One part is using data about infrastructure details and community factors, like income levels or access to resources.
Another part is using existing and tested recovery models to judge how long it might take for homes and neighborhoods to recover under different funding or policy conditions.
This completely changes how damage assessment is performed:
- Remote sensing gives an immediate overview of the entire situation.
- The AI model can analyze this data in less than an hour, compared to the months required for on-the-ground humans to perform the damage evaluation.
- Restoration modeling turns the AI evaluation into actionable metrics on which areas need the most help and what resources are required.
“Ultimately, this research bridges the gap between rapid disaster assessment and strategic long-term recovery planning, offering a risk-informed yet practical framework for enhancing post-tornado resilience.”
Abdullah Braik – Civil engineering doctoral student at Texas A&M
Validating the AI Model with Real-World Tornado Data
To validate this approach, the researchers looked back at the 2011 Joplin tornado catastrophe.
This event was extensively documented, creating a rich dataset that could be used as a backtest for the AI system. The calculated assessments could then be compared to real-life, on-the-ground damage assessments done at the time.
And the AI-generated results proved remarkably close to the historical data. It also gave a record of how the catastrophe unfolded.
“One of the most interesting findings was that, in addition to detecting damage with high accuracy, we could also estimate the tornado’s track.
By analyzing the damage data, we could reconstruct the tornado’s path, which closely matched the historical records, offering valuable information about the event itself.”
Abdullah Braik – Civil engineering doctoral student at Texas A&M
Scaling the AI Model to Other Natural Disasters
While it was developed and back-tested for tornado-caused damage, this method could be deployed for other situations, like hurricanes and earthquakes, as long as satellites can detect damage patterns.
This limitation could be less of a problem than initially expected, even if damages from earthquakes are, for example, less easily visible from the sky than blown-off roofs. This is because the model learns from real-life examples, and we often see AIs being able to detect patterns invisible to the human eye.
“The key to the model’s generalizability lies in training it to use past images from specific hazards, allowing it to learn the unique damage patterns associated with each event.”
Abdullah Braik – Civil engineering doctoral student at Texas A&M
At the very least, it seems that the model will match well for another common disaster in the USA: hurricanes.
“We have already tested the model on hurricane data, and the results have shown promising potential for adapting to other hazards.”
Abdullah Braik – Civil engineering doctoral student at Texas A&M
This matches another application of AI in this field, with better hurricane prediction now becoming a reality, including with AIs like Graphcast, Spire, and Climavision.
Another extension of this research could be to move beyond damage assessment. It could be used for creating real-time updates on recovery progress and tracking recovery over time.
This sort of automated, AI-driven feedback could then inform policy and optimize the rebuilding efforts.
Investing in Satellite Data & AI
Spire
Spire Global, Inc. (SPIR -0.94%)
Spire is a space data company that operates the world’s largest multi-purpose constellation of satellites in private hands.
The company strongly focuses on weather data, and its satellites can capture images in multiple spectra, giving a more data-rich picture of a given location.
For example, its satellite images of moisture measurement are precise up to 100m and can be used by farmers, but also insurance companies, commodity traders, environmental monitoring agencies, construction companies, and civil engineers to better understand soil condition and upcoming agricultural yields.
Source: Spire
The company offers to build for its client their own proprietary satellite constellation, with the LEMUR satellite platform.
Source: Spire
The company is active in security as well, notably with its aviation offer for Automatic Dependent Surveillance-Broadcast (ADS-B), which uses GPS to determine airspeed, location, and other information about aircraft.
Meanwhile, Spire has been selected for a $237M contract with the US Space Force to “to design, build, integrate, and operate small satellite buses for next-generation space experiments”.
It was also active in the maritime industry, with its satellites used for vessel tracking, but this branch of the company was acquired by Kpler in April 2025.
Regarding AI, Spire is collaborating with NVIDIA to integrate into NVIDIA‘s Earth–2 Cloud APIs all of Spire’s Radio Occultation (RO) data and proprietary data assimilation (DA)
“Aligning Spire’s proprietary data and unmatched global weather coverage with NVIDIA’s cutting-edge technology and expertise positions us to markedly elevate the accuracy of weather prediction. This collaboration will help ensure our customers are not just informed but empowered to proactively address the evolving climate landscape.”
Michael Eilts – General manager of weather and climate at Spire
Overall, Spire is a data company embracing the conjunction of satellite imagery and AI for better weather forecasts, agriculture prediction, tracking of airplanes, and even defense purposes.
It is now reaching a scale where it could become profitable, an important potential turning point for investors, and maybe it will not need too much additional money raising after a successful $40M gross proceeds in selling new shares in Q1 2025.
Source: Spire
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Study Referenced:
1. Abdullah M. Braikand Maria Koliou. Post-tornado automated building damage evaluation and recovery prediction by integrating remote sensing, deep learning, and restoration models. Sustainable Cities and Society. Volume 123, 1 April 2025, 106286. https://doi.org/10.1016/j.scs.2025.106286