How does machine learning enhance GIS in disaster scenarios?

How can machine learning improve GIS applications during disasters?

Machine learning enhances GIS by analyzing vast datasets to predict disaster patterns and optimize response strategies.

Can you give an example of how this works in real life?

Sure! For instance, during hurricanes, machine learning models can analyze satellite images to assess damage and prioritize rescue efforts.

What types of data are most useful for these machine learning models?

Data from satellite imagery, social media, weather reports, and historical disaster records are crucial for training these models.

Are there any limitations to using machine learning in GIS for disasters?

Yes, limitations include data quality, the need for large datasets, and the challenge of real-time processing during emergencies.