This repository contains the GIS (Geographic Information System) and Weather data processing components of the TSYS AI Operating System. It provides specialized tools and workflows for handling geospatial data and meteorological datasets, particularly for infrastructure planning and balloon path prediction for TSYS Group projects.

Architecture

This system follows the same disciplined container architecture as the parent AIOS-Public project:

Layered containers: Base containers providing foundational tools with specialized containers extending functionality
Security patterns: Non-root users with UID/GID mapping for proper file permissions
Operational patterns: Wrapper scripts for automatic environment setup
Naming convention: TSYS-AIOS-GIS-Tools-[domain]-[type] pattern
Organization: Separate CTO mode (R&D) from COO mode (operational tasks)

Components

Docker Containers

GIS Containers

TSYS-AIOS-GIS-Tools-GIS-Base: Foundation container with core GIS libraries (GDAL, DuckDB, PostGIS client tools)
TSYS-AIOS-GIS-Tools-GIS-Processing: Advanced processing tools with Jupyter notebooks for ETL workflows

Weather Containers

TSYS-AIOS-GIS-Tools-Weather-Base: Foundation container with weather data libraries (xarray, cfgrib, MetPy)
TSYS-AIOS-GIS-Tools-Weather-Analysis: Advanced analysis tools with forecasting libraries

Collaboration Structure

Questions: Initial questions and requirements gathering
Proposals: Design proposals and architectural decisions
Plans: Implementation roadmaps and timelines
Prompts: AI prompt templates for development
Audit: Compliance and review records

Usage

Docker Container Usage

Each container is located in the Docker/ directory with its own subdirectory:

# Navigate to a specific container directory
cd Docker/TSYS-AIOS-GIS-Tools-GIS-Base

# Build and start the container with automatic user mapping
./docker-compose-wrapper.sh up --build

# Run a specific command in the container
./docker-compose-wrapper.sh run tsys-gis-base [command]

Design Principles

Self-hosted GIS stack: Privacy and control over geospatial data processing
Multi-format support: Shapefiles, GeoJSON, Parquet, GRIB, NetCDF
Database integration: PostgreSQL/PostGIS client tools for spatial analysis
ETL workflows: Support for both technical (GIS/Weather) and business data processing
Documentation integration: Compatible with existing documentation tools
MinIO integration: Output to MinIO buckets for business use

Integration with AIOS Ecosystem

Compatible with existing user management (UID/GID mapping)
Can be orchestrated with documentation containers when needed
Follows same naming conventions and wrapper script patterns
Separate from documentation containers but can work together in CTO mode

Next Steps

Phase 1: Deploy and test base GIS container with sample datasets
Phase 2: Deploy and test weather base container with GRIB support
Phase 3: Advanced processing containers with visualization and Jupyter support
Phase 4: Optional fusion container for integrated GIS+weather analysis (balloon path prediction)