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Working examples and module orchestration patterns. Use when looking for usage examples, cross-module orchestration patterns, end-to-end workflows, or reference implementations of GEO-INFER capabilities.
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基于 SOC 职业分类
Geospatial Active Inference framework with 44 modules for ecological, civic, and commercial spatial analysis. Use when working with geospatial data, Active Inference, Bayesian modeling, H3 hexagonal indexing, spatial statistics, or any domain-specific geographic analysis (agriculture, health, economics, risk, climate, energy, transport, marine, forestry, water).
Statistical Parametric Mapping for geospatial data. Use when performing GLM-based spatial analysis, random field theory corrections, cluster-level inference, or neuroimaging-style statistical mapping on geographic datasets.
Ant Colony Optimization and swarm intelligence for geospatial problems. Use when solving spatial optimization with ACO, PSO, ABC algorithms, implementing stigmergic coordination, or optimizing geographic routing and resource allocation with bio-inspired methods.
H3 hexagonal spatial indexing and multi-backend spatial operations. Use when working with H3 cells, spatial indexing, coordinate systems, raster/vector operations, or any spatial backend dispatch (H3, SRAI, PostGIS).
Unified test runner and testing infrastructure for the GEO-INFER ecosystem. Use when running cross-module tests, configuring test categories, setting up test fixtures for spatial data, or analyzing test results.
Generative geospatial art and cartographic visualization. Use when creating artistic map visualizations, generative spatial art, interactive cartographic displays, animation sequences, or aesthetically-focused geographic rendering.
| name | geo-infer-examples |
| description | Working examples and module orchestration patterns. Use when looking for usage examples, cross-module orchestration patterns, end-to-end workflows, or reference implementations of GEO-INFER capabilities. |
| prerequisites | {"required":[],"recommended":[]} |
| difficulty | beginner |
| estimated_time | 30min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
GEO-INFER-EXAMPLES/examples/
├── module_orchestrators/ # Per-module orchestration demos
├── getting_started/ # Beginner walkthroughs
├── agriculture_integration/ # AG + SPACE + RISK workflows
├── climate_integration/ # CLIMATE + SPACE workflows
├── health_integration/ # HEALTH + SPACE workflows
└── iot_radiation_monitoring/ # IOT + DATA + SPACE workflow
from geo_infer_examples.core.module_orchestrator import ModuleOrchestrator
orchestrator = ModuleOrchestrator(monitoring_enabled=False)
workflow_ids = orchestrator.list_workflows()
module_health = orchestrator.get_module_health()
from geo_infer_examples.core.module_orchestrator import ModuleOrchestrator
# Inspect configured cross-module workflows before execution
orchestrator = ModuleOrchestrator(monitoring_enabled=False)
for workflow_id in orchestrator.list_workflows():
workflow = orchestrator.get_workflow_definition(workflow_id)
print(workflow_id, workflow.execution_strategy)
# Domain workflow: Agricultural risk assessment
from geo_infer_ag.models.soil_health import SoilHealthModel
from geo_infer_risk.core.risk_engine import RiskEngine
from geo_infer_space.backends.h3 import H3Backend
# 1. Tessellate farm boundary
cells = H3Backend().tessellate(farm_polygon, resolution=9)
# 2. Assess soil health per cell
soil = SoilHealthModel()
health_scores = {cell: soil.assess(cell) for cell in cells}
# 3. Compute risk
risk = RiskEngine()
risk_map = risk.assess(hazard=drought_index, exposure=health_scores)
module_orchestrator.py convergence check uses numeric relative-changeexamples/*/scripts/uv run python -m pytest GEO-INFER-EXAMPLES/tests/ -v