How can I train an ANN using current raster data (population, precipitation, DEM, land use) and use this model to predict future land use based on upcoming raster data (population, precipitation, temperature)?
I've been studying the relevant theory extensively recently, and I'd like to find a case study to see how it's practically implemented. Could you please guide me to where I can find similar case studies? I've noticed that many papers follow this approach, but I'm struggling to locate any code examples.
If there are no case studies available, I'd like to understand how to use raster data as inputs for an ANN and what preprocessing steps are needed, especially when dealing with multiple raster data inputs.
Xuyang Bai
Hi,
utilizing an Artificial Neural Network (ANN) to predict land use based on raster data is a potent application of machine learning in geospatial analysis. This approach involves training a model on historical raster data, such as population, precipitation, Digital Elevation Model (DEM), and existing land use, and then leveraging this trained model to forecast future land use patterns using forthcoming raster data containing parameters like population, precipitation, and temperature.
To practically implement this approach, you can follow these steps:
As for case studies and code examples, you can explore academic journals, conferences, and online repositories like GitHub. Many researchers have indeed explored this approach, but the specific code examples might not be readily available due to variations in dataset availability and implementation choices. Look for papers that discuss their methodology in detail; they often provide insights into the data preprocessing, model architecture, and evaluation techniques used.
If you find my reply is useful , please recommend it .Thanks
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