The positive potential of AI within disaster mitigation, management, and recovery

As part of our weekly knowledge learning session I recently shared an article about the Japanese government using AI as part of their disaster planning, specifically preparing for the eruption of Mount Fuji — “Tokyo uses AI to warn residents to prepare for the worst with simulation of Mount Fuji erupting”

The article sparked an interest buried deep down in me, having studied Earth Science and Geology at university, with a number of modules related to natural disasters, specifically earthquake predictions and tsunami warning systems — finally that degree has come full circle...

As a break from the polarising views on AI bad/AI good on LinkedIn, I wanted to dig a little deeper into how AI can be used as a force for good supporting the detection, prevention, mitigation, and post-disaster assessment of many natural hazards.

Introduction

There’s no denying climate change is accelerating global natural disasters with devastating wildfires, flooding, and droughts reported on an almost daily basis.

The role of AI is rapidly evolving in the area of disaster management/ emergency response, and also creating new opportunities for industries like insurance, urban planning and infrastructure, all with a view of improving speed, accuracy, and resource allocation.

Covered in this summary article:

• A short history of AI in disaster management

• AI supporting disaster management for Wildfires and Earthquakes

• The challenges around reliability and trust, and opportunities ahead

History and evolution of AI in disaster management

AI’s integration into disaster management has been a gradual evolution spanning several decades, with early risk detection tools based on historic data and GIS software. Geographic Information System is mapping software that combines location data with other information.

Over the last two decades, the rise of big data, cloud computing, Internet of Things sensors, and deep learning has revolutionised disaster prediction and coordination. A 2025 report highlights that 70% of humanitarians use AI regularly, but organisational uptake is still emerging… so more opportunity, but positive signs of adoption.

As technology has developed over the last 25 years, so have deep learning and sensor networks, enabling real-time monitoring and faster response times, while multi-agent and intelligent systems began supporting complex, collaborative decision-making, more on this later.

Advances in satellites for disaster management have dramatically increased too, all empowering predictive analytics, geospatial mapping, scenario simulations.

Wildfires

Of the many environmental disasters, it feels that destructive wildfires have been in the headlines over the last few years, literally spreading across Europe, the UK and the USA too. So, how is AI being used to monitor, mitigate and react to these devastating events?

Of the few examples that kept appearing whilst researching with, they included a bootstrapped startup from India called Farmonaut, who claim to be…

“Revolutionizing Wildfire Management: AI-Powered Satellite Monitoring for Proactive Risk Assessment in Nevada’s Forests”

They discuss the benefits of their system with a clear call out on Early Detection, Real-Time Monitoring, and Accuracy You Can Trust. The integration of AI and machine learning algorithms with satellite data that has the capacity to predict, detect, and respond to wildfires by:

• Analysing massive datasets in real-time

• Identify patterns and anomalies indicative of fire risk

• Generating accurate fire spread predictions

• Optimising resource allocation for firefighting efforts

All sounds impressive and promising, with solutions including AI-Powered Intelligence and Fire Spread Analysis Dashboards. They are a company worth monitoring, though I couldn’t find any real-life cases of their software in action.

Post-Fire Damage Assessment

While Farmonaut focuses on predictive monitoring and early detection, other companies are also tackling similar challenges and post-fire assessment challenges. ICEYE, a Finnish-based company, takes the approach of using unique radar satellites (SAR — Synthetic Aperture Radar) to create 2D or 3D reconstructions of landscapes along with clearly captured images, even through smoke and clouds, day or night.

Their AI models automatically detect and classify which buildings are destroyed or undamaged, generate detailed damage maps, and send rapid updates, even during smoke or darkness.

This automated process helps emergency teams and insurers respond faster and more accurately, replacing slow, manual assessments with reliable, near real-time wildfire insights.

• AI satellite damage detection: Radar images analysed automatically for building destruction

• Real-time data fusion: Combines imagery with geographic data for hourly updates

• Technology partnerships: Collaborates with AI specialists for enhanced image analysis

• All-weather precision: Works through smoke, clouds, darkness for rapid response

“During the January 2025 Los Angeles wildfires (Palisades and Eaton fires), ICEYE’s satellites provided critical data with 99% precision and 91% recall in identifying destroyed properties” — ICEYE Blog

Though not explicitly stated by ICEYE in their wildfire monitoring, there are examples of wildfire monitoring systems using multi-agent systems — networks of independent AI programs that work together like a coordinated team, which we’ll come on to in a little more detail later…

ICEYE’s insurance offering uses the same satellites and advanced analytics to rapidly assess/size losses at the building level, allocate field teams, and process claims much faster thanks to near real-time, verifiable data, even before traditional aerial photos are available.

Earthquakes

Earthquake monitoring is clearly different from floods, fires, droughts, and similar hazards due to its unpredictability, subtle precursors and indicators. That said, AI-supported earthquake prediction span machine learning, deep learning, multi-agent systems, and intelligent agents, and are actively used globally.

A couple of examples to focus on include how researchers have been leveraging advanced machine learning model Random Forest, and neural network models to successfully predicted earthquakes for Los Angeles with an accuracy of 97.97%

Proposed Multi-agent systems (MAS) claim to be able improve earthquake disaster response by dividing complex tasks among autonomous agents that work together. These systems would coordinate in real-time and adapt to changing conditions, maintaining effectiveness if infrastructure fails. Some examples of this work include, drones map damage while ground robots search, as well as the following…

• Specialised agent roles: Independent software agents handle specific tasks like monitoring sensors, analysing data, managing communications, and assessing risks

• Continuous surveillance: The system constantly watches multiple warning signs through distributed sensors, including ground movement, seismic activity, gas emissions, and even changes in soil temperature

• Team decision-making: Agents share information and work together using agreed rules to determine earthquake risk levels (low, medium, high) and issue appropriate warnings for each area

The role of ontologies and digital twins

I wanted to include a short summary of the above areas, as it’s a key topic of discussion amongst our team. It’s something I’m sure could take up many more articles, and is the subject of plenty of research papers, but here’s a short summary version.

Think of ontologies as the ‘magic map’, or knowledge systems that connect dots between different pieces of information. They act like translators between raw data (sensor readings, weather reports, social media posts) and useful information that emergency responders can actually use.

Instead of different agencies and organisations working with incompatible systems and data formats, ontologies create a common language. This means everyone from local fire departments to international aid agencies can share information seamlessly and coordinate response efforts.

The digital twin (a virtual, data-rich replica of a physical object, system, or process) part of this… is essentially the smart digital copy of the location that’s able to track what’s happening in real time, helping people know where the problems are, it’s a virtual representation for more accurate simulations.

Perplexity’s child friendly explanation…

Imagine you have a computer game that builds a pretend city just like a real one. In this game, when a big storm, flood, or fire happens, the computer city changes — roads might get blocked, water could rise, and some buildings might need help.

Increasingly companies like ICEYE are using elements of digital twin technology to provide real-time, and ontology-based frameworks to provide contextual awareness as part of their disaster ‘solutions’, monitoring, and predictions.

This only really scratches the surface about how ontologies and digital twins work. There’s plenty more indepth research including this research paper — https://www.nature.com/articles/s41598-023-34874-6

What are the challenges?

A common theme, or challenge we hear a lot about about AI, is one of trust and reliability. Some of the challenges faced within disaster management include transparency with respect to the data, false positives, along with…

• Data limitations: AI requires vast, reliable datasets often unavailable for rare disasters

• Black box problem: Complex models can lack transparency, hindering trust and interpretation

• Privacy concerns: Personal data collection raises ethical/equity issues

• Infrastructure barriers: Remote areas lack computational resources for advanced AI deployment

• Local knowledge gaps: Global models may miss community-specific vulnerabilities and needs

I’d recommend reading the Eos’s (the news magazine published by the American Geophysical Union article… ‘Cultivating Trust in Disaster Management’ where they discuss all of the above, plus how the ‘complexity of some AI algorithms makes it difficult to pinpoint causes of failure’.

AI can greatly improve disaster preparedness, but it also has the potential to create some level of inequality. Communities with strong digital infrastructure can use tools like early warning systems, real-time monitoring, and fast response coordination. Meanwhile, rural areas, low-income neighborhoods, and developing regions may lack the connectivity, devices, or digital skills to benefit in the same way.

Developing low-bandwidth solutions for areas with poor connectivity, multilingual interfaces that respect local languages, and systems to integrate traditional knowledge with modern technology will be key to avoid creating a two tiered system. Community-led approaches, where local residents help design and validate AI tools, could lead to tech solutions that align with real-world needs and cultural contexts.

The future is bright though…

While the technology is complex, and I don’t fully understand all of it, I’m sure we’ll see continued progress with mitigation models, rapid drone-based assessments, along with…

• Smart device integration: Connecting AI with internet-connected devices that monitor environmental conditions, to enhance real-time awareness and focused aid support

• Tailored community support: AI could potentially help personalise advice and resource allocation based on specific local risks and needs

• Instant damage evaluation: Advanced visual AI provides immediate assessments to prioritise rescue and recovery efforts

• Human-AI partnership: ‘The human in the loop’ Ensuring AI automation is paired with human judgement, experience, and ethical oversight — key to building trust, and maintaining the human understanding of any disastrous situation

In Summary

AI-powered intelligent agents are becoming a critical tool for disaster management by predicting events earlier, coordinating response faster, and automating damage assessment.

We only skimmed the surfaces on some of the challenges, but it’s clear trust and accuracy are key to successful adoption and use within this area. Successful use of AI systems depends on high-quality data, transparency, real-time integration, ethical safeguards, and of course strong human-AI partnerships.

The every evolving AI landscape and pace of change will no doubt play it’s part, especially with the emergence of tools like AlphaEarth Foundations.

This looks to have great potential, whether it’s in monitoring environmental changes, or early detection of floods, wildfires, and landslides.

Borne out of Google DeepMind, AlphaEarth Foundation is a breakthrough AI model that acts like a “virtual satellite,” integrating petabytes of diverse Earth observation data (satellite imagery, radar, LiDAR, climate models, etc.) to map the planet in unprecedented detail and consistency

Summary of industry implications

• Insurance: Imagine Real-time damage assessment, where AI could instantly confirms that property is flood hit, triggering immediate insurance payout, cutting claim processing time right down

• Supply chain: Predictive wildfire models help reroute logistics even before disruption hits

• Property management: Building-level damage detection could enable faster tenant communication and recovery planning, and even AI-monitored properties (at a premium??)

• ROI reality: Industry studies show AI insurance systems can reduce claims processing and potentially create $100 billion in benefits for insurers and customers (McKinsey/Bain & Company research)

Looking ahead, blending AI, advances in satellite imagery with smart sensors, drones, and more accurate/trusted data insights will make future disaster mitigation, response, and management more effective, and will only be enhanced further when communities, experts, and technologists work together.

🔗 References

• Artificial Intelligence in Disaster Risk Management: A Scientometric Mapping of Evolution, Collaboration, and Emerging Trends (2003–2025)

• Cultivating Trust in AI for Disaster Management

• AI-Driven Disaster Response Coordination in 2025

• 70% of humanitarians use AI regularly, but organisational uptake still emerging — global study of 2,500+ finds

• Indepth study & report (Perplexity helped summarised this for me) on ‘Intelligent Agents in Disaster Risk Management: A Systematic Review of Advances and Challenges’

• AlphaEarth Foundations — mapping our planet in unprecedented detail