Summary
Artificial intelligence is helping humanitarian organizations assess disaster damage and identify communities that may need urgent assistance. Microsoft’s High-speed Assessment and Satellite Tracking for Emergencies (HASTE) system uses satellite imagery and AI to help responders understand the impact of disasters more quickly.
The technology analyzes satellite images captured before and after an emergency to identify damaged buildings and map affected areas. According to Microsoft, HASTE can help organizations produce assessments within hours instead of spending days or weeks reviewing images manually.
Since 2023, the system has been used following 23 major disasters across 16 countries, including wildfires, floods, earthquakes, and cyclones. The goal is to help relief teams make faster, better-informed decisions when time and resources are limited.
Why Is Rapid Disaster Assessment Important?
When a major disaster occurs, emergency responders need to understand where the damage is worst, which communities require immediate assistance, and which roads remain accessible.
However, gathering reliable information can be difficult during the first hours after an earthquake, flood, or other emergency. Roads may be damaged, communication networks may be disrupted, and reports from affected communities may be incomplete.
Without a clear picture of the situation, relief teams risk spending valuable time traveling to areas that are less severely affected while other communities wait for help.
Traditional damage assessments can require specialists to examine large amounts of satellite imagery and compare conditions before and after the event. This process can take days or even weeks, depending on the scale of the disaster.
AI-powered analysis offers a way to speed up this work and help humanitarian organizations decide where to send their teams and supplies.
How Does Microsoft’s HASTE System Work?
HASTE is part of Microsoft’s AI for Good initiative. It uses satellite imagery and AI to help humanitarian organizations assess damage across large areas following a disaster.
The system compares images captured before and after an event to identify changes in buildings and surrounding areas.
Here is how the process works:
- Collect satellite images: Images from before and after the disaster provide information about conditions on the ground.
- Train the AI model: The system is trained on examples of the damage relevant to the specific disaster, helping it recognize similar patterns in other images.
- Analyze affected areas: AI processes large amounts of imagery to identify potentially damaged buildings and locations.
- Create damage maps: The results help responders visualize which areas appear to have suffered the greatest damage.
- Support relief planning: Humanitarian teams use the assessment alongside other information to prioritize field visits, plan routes, and direct emergency supplies.
Microsoft describes HASTE as a free, open-source tool. Its purpose is to reduce the time needed to turn satellite imagery into information that responders can use.
The system does not replace field inspections or confirm every individual damage report. Instead, it provides an initial overview that can guide further investigation and response.
How AI Helped After an Earthquake in Venezuela
One example described by Microsoft involved a major earthquake that struck Venezuela on June 24.
Carlos Rivas, a disaster risk management specialist at Catholic Relief Services, needed to understand which locations had suffered the most damage and where response teams should focus their efforts.
After confirming that local staff and volunteers were safe, Rivas contacted Microsoft’s AI for Good team for help with the assessment.
By the following morning, he had received a map analyzing the damage.
According to Nora Lindstrom, who leads Catholic Relief Services’ global digital programming team, producing a similar assessment through the organization’s previous manual process could have taken approximately two weeks.
The faster analysis gave responders information they could use to prioritize their work instead of waiting for a lengthy review of satellite images.
For humanitarian organizations operating with limited budgets, this time savings can help them allocate staff, equipment, and supplies more effectively.
Why Speed and Accuracy Both Matter
Speed is important during disaster response, but incorrect information can create additional problems.
For example, a relief team might spend hours traveling to a particular town based on incomplete reports, only to discover that another community has suffered more extensive damage.
AI-assisted mapping can help teams compare conditions across a wider area before deciding where to go.
Kevin White, a senior director at Microsoft’s AI for Good Lab, emphasized the importance of producing accurate assessments quickly. Rapid results are useful only when the information is reliable enough to support decisions.
HASTE aims to help responders identify damage patterns across large territories more efficiently than manual analysis alone.
However, satellite images have limitations. Clouds, image resolution, the timing of image collection, and the type of disaster can affect what the system detects. AI-generated maps therefore need to be interpreted alongside local reports and on-the-ground assessments.
Where Has HASTE Been Used?
Microsoft says humanitarian organizations and government agencies have used HASTE following 23 major disasters in 16 countries since 2023.
The reported applications include:
- Wildfires in France: Supporting efforts to understand the extent of affected areas.
- Floods in Libya: Helping assess the impact of flooding.
- Earthquake in Türkiye: Supporting post-disaster damage analysis.
- Cyclone in Madagascar: Helping responders understand the scale of the damage.
These examples show how satellite imagery analysis can support different types of emergencies.
Although every disaster presents unique challenges, the underlying need is similar: responders need timely information about where damage occurred and which communities may require assistance.
How Can AI Help Humanitarian Organizations?
Humanitarian organizations often operate under difficult conditions, with limited personnel, tight budgets, and incomplete information.
AI-assisted disaster mapping could provide several benefits.
Faster assessments: Automated analysis can reduce the time required to review large amounts of satellite imagery.
Better resource allocation: Damage maps can help responders prioritize areas that may need urgent attention.
Improved route planning: Information about affected areas can help teams investigate road conditions and plan field visits, although separate road-access data may still be necessary.
Wider coverage: AI can help analyze large territories that would take human analysts considerable time to examine manually.
Support for local teams: Faster access to damage information can help local staff and volunteers coordinate their work with humanitarian organizations.
These benefits are particularly important when several communities need assistance at the same time.
What Are the Limitations of AI-Based Disaster Mapping?
AI can make disaster assessments faster, but it cannot solve every problem associated with emergency response.
Satellite imagery may not reveal whether people are trapped inside buildings, whether medical supplies have run out, or whether a community needs food and clean water immediately.
AI models can also misidentify damaged structures or miss important changes in the images. Results may be less reliable when imagery is incomplete or conditions differ from the examples used to train the model.
For these reasons, HASTE should be understood as a decision-support tool rather than a replacement for emergency workers, local knowledge, or direct assessments.
Responders still need to verify important findings, communicate with affected communities, and make decisions based on the full situation.
What Comes Next?
As climate-related hazards and other major emergencies continue to challenge communities around the world, rapid access to reliable damage information is becoming increasingly important.
AI systems such as HASTE could help humanitarian organizations process satellite imagery faster and improve the way they coordinate their initial response.
Future improvements may make damage assessments more detailed and useful, but their effectiveness will depend on the quality of available imagery, model accuracy, and how well the results are integrated into emergency planning.
The ultimate objective is to ensure that technology supports the people making decisions on the ground and helps assistance reach communities where it is needed most.
Conclusion
Microsoft’s HASTE system demonstrates how AI can support disaster response by turning satellite imagery into useful damage assessments more quickly.
By identifying potentially damaged buildings and mapping affected areas, the technology can help humanitarian organizations prioritize their work and make better use of limited resources.
AI cannot replace local knowledge or direct emergency assessments, but it can give responders a faster starting point when every hour matters. Combined with reliable field information and experienced relief teams, these tools could help improve the coordination of disaster assistance around the world.