GEO-INFER — a 44-module geospatial inference monorepo from the Active Inference Institute. Spatial analysis, active inference, domain modeling, agent workflows, and reproducible repository validation in one uv/Python workspace (H3, Bayesian models, place & risk).
Canonical GEO-INFER Active Inference implementation. Use when implementing or reviewing free-energy minimization, belief updating, generative models, policy selection, H3/spatial active inference, or typed ACT diagnostics.
Precision agriculture and soil health modeling. Use when analyzing soil health, crop water usage (FAO-56), carbon sequestration (IPCC Tier 1), precision farming, or agricultural land management.
Multi-agent geospatial systems with Active Inference. Use when building spatial agents, implementing perception-action loops, managing agent telemetry, or coordinating multi-agent spatial exploration.
Machine learning pipelines and model selection for geospatial AI. Use when training spatial ML models, building prediction pipelines, performing feature engineering on geographic data, or selecting between ML approaches for spatial problems.
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.
REST and GraphQL API endpoints for geospatial services. Use when building API routes, defining spatial query endpoints, or exposing GEO-INFER functionality as web services.
Application framework for geospatial dashboards and web interfaces. Use when building spatial dashboards, map-based UIs, agent control widgets, interactive geospatial web applications, or configuring agent parameters through forms.
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.
Bayesian inference and probabilistic modeling for geospatial data. Use when building hierarchical models, computing posteriors with PyMC or TFP, performing variational inference, model comparison (LOO/WAIC/DIC), or spatial Gaussian processes.
Biodiversity analysis and ecological modeling. Use when analyzing species distributions, habitat connectivity, biodiversity indices, ecological networks, conservation planning, or ecosystem health assessment.
Civic engagement and participatory mapping. Use when building participatory GIS, STEW-MAP implementations, community mapping platforms, citizen science data collection, or democratic spatial planning processes.
Cognitive modeling for geospatial agents including attention, memory, and trust. Use when implementing spatial attention mechanisms, working memory for geographic contexts, or trust dynamics in multi-agent geospatial systems.
Communication systems for geospatial coordination. Use when implementing spatial messaging, multi-channel notifications (email/SMS/push), event streaming, spatial message routing, or subscriber management for geographic broadcasts.
Data connectors, ETL pipelines, and data management for geospatial datasets. Use when loading spatial data from databases, APIs, files (GeoJSON, Shapefile, GeoParquet), or building data transformation pipelines.
Geospatial economics and bioregional market modeling. Use when analyzing spatial economic patterns, bioregional markets, location-based pricing, call auctions, or supply-demand modeling with geographic context.
Energy systems analysis and renewable energy siting. Use when computing LCOE, analyzing energy grid spatial patterns, optimizing renewable energy placement, assessing energy storage, or performing techno-economic analysis of energy projects.
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.
Git-based versioning and collaboration for geospatial datasets. Use when versioning spatial data, managing geospatial dataset lineage, tracking spatial data changes, resolving merge conflicts in geospatial formats, or building reproducible analysis pipelines.
Spatial epidemiology and public health analysis. Use when modeling disease spread, analyzing health disparities, performing spatial health risk assessment, building epidemiological surveillance systems, or assessing healthcare accessibility.
Central documentation hub and cross-module integration guides for GEO-INFER. Use when navigating documentation, finding cross-module integration patterns, understanding data flow architecture, or locating tutorials and API references.
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At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: