Wildfire Prediction and Management
GeoAI can be used to predict wildfire risks by analysing spatial data, weather patterns, and vegetation conditions.
AI models can assess real-time data from satellites and sensors to forecast where wildfires are likely to occur and how they might spread. This enables early intervention, better resource allocation, and improved safety measures.
Scientists are using systems of self-coordinating drones designed to tackle wildfires efficiently and effectively. This technology utilises AI and advanced imaging—both thermal and optical—to detect and investigate fires autonomously.
AI drones are used to analyse critical factors like vegetation density, moisture levels and drought conditions to identify higher-risk areas. Advanced sensors collect detailed environmental data and flag locations where dense, dry vegetation and prolonged drought create the most dangerous fire conditions.
In Turkey, the FireAId initiative is using Artificial Intelligence (AI) to improve the prediction and prevention of wildfires. An interactive wildfire risk map has been developed, using AI and machine learning algorithms .
click to enlarge
Image source. Find out more about FireAid
Urban Planning and Smart Cities
Cities are highly complex systems, making it important to integrate artificial intelligence into their planning. .
GeoAI can be employed to optimise land use, transportation networks, and resource allocation and create more sustainable and efficient city plans.
Play the video introducing Artificial Intelligence and Smart Cities
When Ai is integrated with urban planning, innovative approaches can be introduced to encourage low-carbon city development. For instance, GeoAI applications can be used in smart city initiatives for real-time traffic management, pollution monitoring, and energy consumption optimisation.
In smart cities, AI tend to be used for:
Predictive Analytics: AI algorithms can predict future trends and patterns, such as traffic congestion, population growth, and resource consumption.
Scenario Planning: AI-powered simulations allow planners to test different development scenarios and evaluate their potential impact.
Optimisation and Decision-Making: AI algorithms can optimise urban processes, such as land use planning, infrastructure allocation and transport routing, .
Real-Time Monitoring: Sensors and IoT devices collect real-time data on urban conditions, enabling planners to monitor and respond to issues including traffic congestion, pollution, and safety.
Citizen Engagement: AI tools can provide interactive platforms for feedback, surveys, and virtual towns.
