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Overview

AI GIS is the integration of AI and GIS. It includes the following features:
1) GeoAI: Spatial analysis algorithm and related process tools integrated with AI.
2) AI for GIS: enhances the function and interactive user experience of GIS software based on AI technology, and improves the intelligence of GIS software.
3) GIS for AI: Management, visualization and analysis of GeoAI results based on GIS.

Highlights

AI GIS capability
  • Augmented intelligent image interpretation on the server, and supports intelligent interpretation based on image services.
  • Augmented video AI on the desktop, and supports training of YOLO v7 series models.
  • Supports remote sensing image intelligent interpretation for model training, reasoning, evaluation in component terminal.
  • Supports AI object detection and classification in mobile terminal.

AI GIS workflow tools
  • Support batch generation of training data at the data preparation stage.
  • Supports automatic initialization of learning rate at the model construction stage.
  • Supports batch reasoning and reasoning based on range at the model application stage.

Geospatial sampling and statistical inference function
  • Supports simple random sampling, systematic sampling and stratified sampling.
  • Supports geospatial random sampling, geospatial stratified sampling and sandwich sampling.
  • Spports SPA model and B-Shade model.

Geospatial machine learning functions
  • Cluster analysis: supports geospatial hotspot analysis, geospatial density clustering, k-means clustering, shift mean clustering, etc.
  • Classification analysis: supports map matching, address element identification, forest-based classification, etc.
  • Regression analysis: supports geosimulation, geographically weighted regression, Spatiotemporal geographical weighted regression, forest-based regression, etc.

Deep learning model
  • Image analysis target detection: Cascade R-CNN, Faster R-CNN, RetinaNet.
  • Binary classification of image analysis: FPN, DeepLabv3+, U-Net, D-LinkNet, SFNet, Segformer.
  • Image analysis ground-object classification: FPN, DeepLab V3+, U-Net, SFNet, Segformer.
  • Image analysis scene classification: EfficientNet.
  • Image analysis object extraction: Mask R-CNN.
  • Image general change detection: DSAMNet, Siam-SFNet, Siam-Segformer.