AI in Geospatial Analysis

1. Data collection, storage & assembling
   • Identifying problems
2. GeoAI analytic & real-time modelling
   • Understanding system function & reliable prediction
3. GeoAI scenarios simulation, muiti-objective optimization & smart decision support
   • Scenarios assessment, management, plan design & implementation
4. Web-based geovisualization, stakeholder engagement & early warning
   • Monitoring, evaluating & management plan adjustment

General Workflow

Steps

1. Data Acquisition and Preparation
   • Image data and label data for the specific location of the feature you want to identify
   • For example, to identify buildings, you need the image of the target area and the label of the vector outline of the building, and then generate training data in the corresponding format.
2. Model Building and Management
   • Model construction and management:
     • to train the neural network model based on the training data samples generated in the previous data preparation process,
     • iteratively evaluate the training model through the validation dataset and test dataset to achieve the actual application accuracy and precision requirements.
3. Model Inference
   • a trained model is used to infer/predict the testing samples and comprises of a similar forward pass as training to predict the values