KQGIS AI
KQGIS integrates AI-driven applications and provides six major categories of analytical algorithms, covering: spatial analysis, cluster analysis, 3D analysis, classification analysis, network analysis, association rule analysis, and geostatistical analysis—a total of eight types of analytical algorithms.
These classic algorithms enable GIS to become identifiable and quantifiable.
At the same time, decision-making models such as decision trees based on random forests and perception-prediction models based on neural networks are introduced to make GIS perceivable, ultimately realizing intelligent GIS.
Key Features of KQGIS AI
Intelligent Semantic Retrieval Based on Distributed Search Engines
- Traditional keyword-based search methods are limited by expression barriers and irrelevant results.
- KQGIS uses Elastic Search to implement a distributed search engine for near real-time data retrieval (minutes reduced to seconds).
- Integrates IK Analyzer for:
- Dictionary-based segmentation
- Grammar analysis
- Rule template matching
- Automatically extracts keywords like:
- Place names
- Satellite names
- Image resolutions
- Converts place names into POI points and maps them to geographic regions.
- Uses FudanNLP and machine learning to:
- Build a semantic dataset
- Extract temporal semantic information – Enable semantic matching beyond simple text matching
AI-Based Interpretation of High-Resolution Imagery
- Utilizes AI and deep learning frameworks for smart remote sensing image analysis.
- Applies multi-scale segmentation technology to:
- Avoid over/under-segmentation
- Prevent overlap in multi-scale object stacking
- Enhances object recognition and classification of:
- Buildings
- Water bodies
- Roads
- Land types
- Supports:
- Annual monitoring of regional changes
- Natural resource planning
- Ecological protection
- Business decision-making
- Emergency disaster response
User Profiling and Personal Behavior Inference Based on Multi-Source Data Integration:
- Tracks frequently viewed or queried areas to analyze user preferences and habits.
- Builds user profiles for personalized services.
- Uses techniques like:
- Clustering
- Association analysis
- Collaborative filtering
- Integrates online and offline data to improve recommendations.
- Shifts from mass recommendations to personalized, scenario-based recommendations.
- Enables intelligent data service recommendations tailored to individual users.
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