Selected research
Industry Research
AI in Disaster Prediction Industry Research Report 2026
Market perspective
The global AI in Disaster Prediction market was valued at US$ million in 2025 and is projected to reach US$ million by 2032, implying a CAGR of % over 2026–2032.
North America: the AI in Disaster Prediction market is projected to increase from US$ million in 2026 to US$ million by 2032, reflecting a CAGR of % over 2026–2032. Europe: the AI in Disaster Prediction market is projected to rise from US$ million in 2026 to US$ million by 2032, registering a CAGR of % over 2026–2032. Asia Pacific: the AI in Disaster Prediction market is expected to grow from US$ million in 2026 to US$ million by 2032, at a CAGR of % over 2026–2032. Leading global service providers of AI in Disaster Prediction include OroraTech, Spectee, NTT Corporation, LiXia, AI Property, One Concern, Zindi, Appier and Fujitsu and among others; in 2025, the top three vendors together accounted for approximately % of global revenue.
Report Scope
Read the full market overview
This report quantifies the global AI in Disaster Prediction market in terms of revenue (US$ million) and, where applicable, service volume (Unit), using 2024 as the base year and providing annual historical and forecast data for 2021–2032.
It standardizes definitions of service Types and end-use Applications, harmonizes provider attribution, and delivers comparable time series by company, Type, Application, and region or country, including indicative price bands (US$/Unit) and concentration ratios (CR5/CR10). Outputs are intended to support service design, budgeting, capacity planning, and benchmarking for providers, platforms, channel partners, and investors; the report also reviews technology shifts and notable service innovations relevant to AI in Disaster Prediction.
Key Companies & Market Share Insights
This section profiles leading service providers with 2021–2025 results and a 2026–2032 outlook—covering revenue, market share, price bands, service portfolio and client mix, regional and channel mix, and key developments (M&A, network expansion, certifications). It also provides global revenue, average price, and—where applicable—volume metrics by provider, and calculates CR5/CR10 and rank changes to support comparative benchmarking.
AI in Disaster Prediction Market by Company
OroraTech
Spectee
NTT Corporation
LiXia
AI Property
One Concern
Zindi
Appier
Fujitsu
Sipremo
AI in Disaster Prediction Segment by Type
Machine Learning
Natural Language Processing
Computer Vision
Others
AI in Disaster Prediction Segment by Application
Wildfire Detection
Flood Forecasting
Earthquake Response
Others
AI in Disaster Prediction Segment by Region
North America
United States
Canada
Mexico
Europe
Germany
France
U.K.
Italy
Spain
Russia
Netherlands
Nordic Countries
Asia-Pacific
China
Japan
South Korea
India
Australia
Taiwan
Southeast Asia
South America
Brazil
Argentina
Chile
Colombia
Middle East & Africa
Saudi Arabia
Israel
United Arab Emirates
Turkey
Iran
Egypt
Key Drivers & Barriers
High-impact rendering factors and drivers have been studied in this report to aid the readers to understand the general development. Moreover, the report includes restraints and challenges that may act as stumbling blocks on the way of the players. This will assist the users to be attentive and make informed decisions related to business. Specialists have also laid their focus on the upcoming business prospects.
Reasons to Buy This Report
1. This report will help the readers to understand the competition within the industries and strategies for the competitive environment to enhance the potential profit. The report also focuses on the competitive landscape of the global AI in Disaster Prediction market, and introduces in detail the market share, industry ranking, competitor ecosystem, market performance, new product development, operation situation, expansion, and acquisition. etc. of the main players, which helps the readers to identify the main competitors and deeply understand the competition pattern of the market.
2. This report will help stakeholders to understand the global industry status and trends of AI in Disaster Prediction and provides them with information on key market drivers, restraints, challenges, and opportunities.
3. This report will help stakeholders to understand competitors better and gain more insights to strengthen their position in their businesses. The competitive landscape section includes the market share and rank (in volume and value), competitor ecosystem, new product development, expansion, and acquisition.
4. This report stays updated with novel technology integration, features, and the latest developments in the market
5. This report helps stakeholders to gain insights into which regions to target globally
6. This report helps stakeholders to gain insights into the end-user perception concerning the adoption of AI in Disaster Prediction.
7. This report helps stakeholders to identify some of the key players in the market and understand their valuable contribution.
Chapter Outline
Chapter 1: Research objectives, research methods, data sources, data cross-validation;
Chapter 2: Introduces the report scope of the report, executive summary of different market segments (product type, application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 3: Provides the analysis of various market segments product types, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 6: Detailed analysis of AI in Disaster Prediction companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 7, 8, 9, 10, 11: North America, Europe, Asia Pacific, South America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 12: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including revenue, gross margin, product introduction, recent development, etc.
Chapter 13: The main points and conclusions of the report.
More report detail
Tables and figures
Scope & coverage.
Types
- Machine Learning
- Natural Language Processing
- Computer Vision
- Others
Applications
- Wildfire Detection
- Flood Forecasting
- Earthquake Response
- Others
Companies covered
- OroraTech
- Spectee
- NTT Corporation
- LiXia
- AI Property
- One Concern
- Zindi
- Appier
- Fujitsu
- Sipremo
Inside the report.
1 Preface
- 1.1 Scope of Report
- 1.2 Reasons for Doing This Study
- 1.3 Research Methodology
- 1.4 Research Process
- 1.5 Data Source
- 1.5.1 Secondary Sources
- 1.5.2 Primary Sources
2 Market Overview
- 2.1 Product Definition
- 2.2 AI in Disaster Prediction by Type
- 2.2.1 Market Value Comparison by Type (2021 VS 2025 VS 2032)
- 2.2.2 Machine Learning
- 2.2.3 Natural Language Processing
- 2.2.4 Computer Vision
- 2.2.5 Others
- 2.3 AI in Disaster Prediction by Application
- 2.3.1 Market Value Comparison by Application (2021 VS 2025 VS 2032)
- 2.3.2 Wildfire Detection
- 2.3.3 Flood Forecasting
- 2.3.4 Earthquake Response
- 2.3.5 Others
- 2.4 Assumptions and Limitations
3 AI in Disaster Prediction Breakdown Data by Type
- 3.1 Global AI in Disaster Prediction Historic Market Size by Type (2021-2026)
- 3.2 Global AI in Disaster Prediction Forecasted Market Size by Type (2027-2032)
4 AI in Disaster Prediction Breakdown Data by Application
- 4.1 Global AI in Disaster Prediction Historic Market Size by Application (2021-2026)
- 4.2 Global AI in Disaster Prediction Forecasted Market Size by Application (2027-2032)
5 Global Growth Trends
- 5.1 Global AI in Disaster Prediction Market Perspective (2021-2032)
- 5.2 Global AI in Disaster Prediction Growth Trends by Region
- 5.2.1 Global AI in Disaster Prediction Market Size by Region: 2021 VS 2025 VS 2032
- 5.2.2 AI in Disaster Prediction Historic Market Size by Region (2021-2026)
- 5.2.3 AI in Disaster Prediction Forecasted Market Size by Region (2027-2032)
- 5.3 AI in Disaster Prediction Market Dynamics
- 5.3.1 AI in Disaster Prediction Industry Trends
- 5.3.2 AI in Disaster Prediction Market Drivers
- 5.3.3 AI in Disaster Prediction Market Challenges
- 5.3.4 AI in Disaster Prediction Market Restraints
6 Market Competitive Landscape by Players
- 6.1 Global Top AI in Disaster Prediction Players by Revenue
- 6.1.1 Global Top AI in Disaster Prediction Players by Revenue (2021-2026)
- 6.1.2 Global AI in Disaster Prediction Revenue Market Share by Players (2021-2026)
- 6.2 Global AI in Disaster Prediction Industry Players Ranking, 2023 VS 2024 VS 2025
- 6.3 Global Key Players of AI in Disaster Prediction Head Office and Area Served
- 6.4 Global AI in Disaster Prediction Players, Product Type & Application
- 6.5 Global AI in Disaster Prediction Manufacturers Established Date
- 6.6 Global AI in Disaster Prediction Market CR5 and HHI
- 6.7 Global Players Mergers & Acquisition
7 North America
- 7.1 North America AI in Disaster Prediction Market Size (2021-2032)
- 7.2 North America AI in Disaster Prediction Market Growth Rate by Country: 2021 VS 2025 VS 2032
- 7.3 North America AI in Disaster Prediction Market Size by Country (2021-2026)
- 7.4 North America AI in Disaster Prediction Market Size by Country (2027-2032)
- 7.5 United States
- 7.5 United States
- 7.6 Canada
- 7.7 Mexico
8 Europe
- 8.1 Europe AI in Disaster Prediction Market Size (2021-2032)
- 8.2 Europe AI in Disaster Prediction Market Growth Rate by Country: 2021 VS 2025 VS 2032
- 8.3 Europe AI in Disaster Prediction Market Size by Country (2021-2026)
- 8.4 Europe AI in Disaster Prediction Market Size by Country (2027-2032)
- 8.5 Germany
- 8.6 France
- 8.7 U.K.
- 8.8 Italy
- 8.9 Spain
- 8.10 Russia
- 8.11 Netherlands
- 8.12 Nordic Countries
9 Asia-Pacific
- 9.1 Asia-Pacific AI in Disaster Prediction Market Size (2021-2032)
- 9.2 Asia-Pacific AI in Disaster Prediction Market Growth Rate by Country: 2021 VS 2025 VS 2032
- 9.3 Asia-Pacific AI in Disaster Prediction Market Size by Country (2021-2026)
- 9.4 Asia-Pacific AI in Disaster Prediction Market Size by Country (2027-2032)
- 9.5 China
- 9.6 Japan
- 9.7 South Korea
- 9.8 India
- 9.9 Australia
- 9.10 China Taiwan
- 9.11 Southeast Asia
10 South America
- 10.1 South America AI in Disaster Prediction Market Size (2021-2032)
- 10.2 South America AI in Disaster Prediction Market Growth Rate by Country: 2021 VS 2025 VS 2032
- 10.3 South America AI in Disaster Prediction Market Size by Country (2021-2026)
- 10.4 South America AI in Disaster Prediction Market Size by Country (2027-2032)
- 10.5 Brazil
- 10.6 Argentina
- 10.7 Chile
- 10.8 Colombia
- 10.9 Peru
11 Middle East & Africa
- 11.1 Middle East & Africa AI in Disaster Prediction Market Size (2021-2032)
- 11.2 Middle East & Africa AI in Disaster Prediction Market Growth Rate by Country: 2021 VS 2025 VS 2032
- 11.3 Middle East & Africa AI in Disaster Prediction Market Size by Country (2021-2026)
- 11.4 Middle East & Africa AI in Disaster Prediction Market Size by Country (2027-2032)
- 11.5 Saudi Arabia
- 11.6 Israel
- 11.7 United Arab Emirates
- 11.8 Turkey
- 11.9 Iran
- 11.10 Egypt
12 Players Profiled
- 12.1 OroraTech
- 12.1.1 OroraTech Company Information
- 12.1.2 OroraTech Business Overview
- 12.1.3 OroraTech Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.1.4 OroraTech AI in Disaster Prediction Product Portfolio
- 12.1.5 OroraTech Recent Developments
- 12.2 Spectee
- 12.2.1 Spectee Company Information
- 12.2.2 Spectee Business Overview
- 12.2.3 Spectee Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.2.4 Spectee AI in Disaster Prediction Product Portfolio
- 12.2.5 Spectee Recent Developments
- 12.3 NTT Corporation
- 12.3.1 NTT Corporation Company Information
- 12.3.2 NTT Corporation Business Overview
- 12.3.3 NTT Corporation Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.3.4 NTT Corporation AI in Disaster Prediction Product Portfolio
- 12.3.5 NTT Corporation Recent Developments
- 12.4 LiXia
- 12.4.1 LiXia Company Information
- 12.4.2 LiXia Business Overview
- 12.4.3 LiXia Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.4.4 LiXia AI in Disaster Prediction Product Portfolio
- 12.4.5 LiXia Recent Developments
- 12.5 AI Property
- 12.5.1 AI Property Company Information
- 12.5.2 AI Property Business Overview
- 12.5.3 AI Property Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.5.4 AI Property AI in Disaster Prediction Product Portfolio
- 12.5.5 AI Property Recent Developments
- 12.6 One Concern
- 12.6.1 One Concern Company Information
- 12.6.2 One Concern Business Overview
- 12.6.3 One Concern Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.6.4 One Concern AI in Disaster Prediction Product Portfolio
- 12.6.5 One Concern Recent Developments
- 12.7 Zindi
- 12.7.1 Zindi Company Information
- 12.7.2 Zindi Business Overview
- 12.7.3 Zindi Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.7.4 Zindi AI in Disaster Prediction Product Portfolio
- 12.7.5 Zindi Recent Developments
- 12.8 Appier
- 12.8.1 Appier Company Information
- 12.8.2 Appier Business Overview
- 12.8.3 Appier Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.8.4 Appier AI in Disaster Prediction Product Portfolio
- 12.8.5 Appier Recent Developments
- 12.9 Fujitsu
- 12.9.1 Fujitsu Company Information
- 12.9.2 Fujitsu Business Overview
- 12.9.3 Fujitsu Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.9.4 Fujitsu AI in Disaster Prediction Product Portfolio
- 12.9.5 Fujitsu Recent Developments
- 12.10 Sipremo
- 12.10.1 Sipremo Company Information
- 12.10.2 Sipremo Business Overview
- 12.10.3 Sipremo Revenue in AI in Disaster Prediction Business (2021-2026)
- 12.10.4 Sipremo AI in Disaster Prediction Product Portfolio
- 12.10.5 Sipremo Recent Developments
13 Report Conclusion
14 Disclaimer
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