Solutions
Applied GeoAI Solutions and Geospatial Intelligence Systems
A growing set of operational GeoAI solutions across weather intelligence, flood hazard awareness, emergency reporting, compliance and siting intelligence, and decision support for environmental and infrastructure systems.
Featured Solutions
Operational GeoAI Solutions in Practice
These solutions reflect SiwaLab’s approach to applied geospatial intelligence by combining real-time data, spatial analysis, standards-based evaluation, environmental modeling, and modern application development into decision-ready systems.
GeoAI Studio
Streamlit · Python · RAG · Code Generation · Geospatial AI
● Intelligent Development Environment
Overview
A domain-aware geospatial AI development environment that combines expert advisory reasoning with runnable Python code generation in a single Streamlit interface. GeoAI Studio extends the GeoAI Advisor foundation into a full development workflow by supporting knowledge-guided technical responses, executable code generation, validation, sandboxed execution, and user file exchange.
Key Features
- Unified advisory and Python code generation workflow for geospatial AI tasks
- Curated knowledge base spanning 100+ geospatial libraries across 13 problem categories
- Multi-stage intent detection with keyword routing and LLM fallback
- GeoAI reasoning layer enforcing task-specific domain guidance and spatial integrity
- Code validation pass targeting raster, vector, CRS, dtype, and workflow correctness issues
- Sandboxed subprocess execution with automated error correction loop
- Strict geospatial domain boundary enforcement with controlled application metadata routing
System Stack
Interface: Streamlit
Core Architecture: RAG + reasoning layer
Knowledge Base: 100+ geospatial libraries
Code Workflow: generation, validation, execution, correction
Runtime: Python subprocess sandbox with conda isolation
Scope Control: geospatial-only response enforcement
Evaluation Highlights
100% intent classification accuracy
100% library hit rate at rank 8
100% keyword-based detection rate
Mean top-chunk cosine similarity of 0.790
Zero LLM dependency across the 20-query gold-standard routing evaluation
Real-Time Weather Intelligence
React · Node.js · PostgreSQL · MapLibre GL · Docker
● Live Application
Overview
A production-grade real-time weather intelligence platform that aggregates live observations from more than 450 stations across Texas, spanning TexMesonet, ASOS airport stations, and RAWS fire-weather stations. The system provides a unified geospatial interface for situational awareness, National Weather Service alert monitoring, and threshold-based environmental intelligence.
Key Features
- Interactive MapLibre GL map with real-time station markers and alert-based symbology
- Animated radar overlay with recent precipitation playback
- NWS official alert integration with severity-aware display
- Texas county boundaries with popup interaction
- Rolling PostgreSQL observations database with automated retention
- Scheduled ingestion and refresh workflows for multiple live data feeds
- Mobile-responsive design for operational monitoring
Tech Stack
Frontend: React 18, Vite, MapLibre GL
Backend: Node.js, Express, scheduled jobs
Database: PostgreSQL 16 (Docker)
APIs: TexMesonet, Synoptic, NWS, RainViewer
Deployment: Docker Compose
Data Sources
TexMesonet API (TWDB)
Synoptic Data API (ASOS and RAWS)
NWS Alerts API
RainViewer radar imagery
Emergency Incident Reporting System
React · Leaflet · Supabase · Geolocation API
Overview
A location-aware emergency reporting application designed for field operations. Users can report incidents through an interactive map, classify emergency type, add descriptive notes, and synchronize submissions immediately through a live backend workflow.
Key Features
- GPS-based user location and continuous position tracking
- Click-to-report map interface for incident placement
- Incident type classification and description entry
- Real-time synchronization through Supabase
- Local storage fallback for resilience in unstable conditions
- Report history with map-focused navigation
- Mobile-first interface for field usability
- User feedback notifications for submission status
Tech Stack
Frontend: React 18, Vite
Maps: React-Leaflet, Leaflet 1.9
Backend: Supabase (PostgreSQL)
Services: Geolocation API, client-side storage
Build: Vite
Flood Hazard & Hydrologic Intelligence
GeoAI Solution · Real-Time Data · Hazard Modeling
Overview
A near real-time flood intelligence platform designed to integrate rain rate observations, river stage data, river extent mapping, and elevation models to estimate evolving flood hazard conditions across Texas. The system is intended to support early warning awareness for roads, campsites, low-lying infrastructure, and other flood-prone locations.
Core Inputs and Capabilities
- Near real-time rain rate observations
- River stage and gauge-based hydrologic conditions
- River extent and watershed spatial layers
- Elevation-informed hazard screening using DEM data
- Spatial identification of roads, campsites, and critical exposed areas
- Early warning logic for elevated flood risk conditions
Primary Data Inputs
Rain rate observations
River stage and gauge feeds
River and watershed spatial layers
Digital elevation models
Critical location overlays
Renewable Energy Siting Intelligence
Geospatial Intelligence · Wind & Solar · Feasibility-Aware Analysis
Overview
A geospatial intelligence workflow for identifying high-potential wind and solar regions across Texas by integrating historical weather observations, spatial variability analysis, and right-of-way constraints. The solution is designed to move beyond simple resource mapping by supporting more realistic, feasibility-aware identification of candidate development zones.
Core Capabilities
- Wind and solar potential screening using historical observations
- Spatial variability analysis for more robust suitability assessment
- Right-of-way and infrastructure-aware feasibility filtering
- Candidate zone ranking and comparative spatial evaluation
- Support for decision-ready regional siting intelligence
Primary Inputs
Historical weather observations
Wind and solar variability indicators
Right-of-way constraints
Infrastructure proximity layers
Regional suitability ranking logic
Data Quality & Compliance Intelligence
Evidence-Based Assessment · Audit-Ready Review · AI-Assisted Reporting
Overview
An evidence-based compliance assessment solution for mesonet networks that evaluates prepared case packages, scores criterion-level compliance, and generates AI-assisted interpretations and draft reports. The workflow combines deterministic scoring outputs with a language-model interpretation layer to support structured, traceable review of standards-driven compliance criteria.
Core Capabilities
- Prepared case-package loading for reproducible compliance review
- Criterion-level scoring with status, confidence, and evidence traceability
- Category summaries across instrumentation, data acquisition, QA/QC, operations, and metadata governance
- AI case summaries, criterion explanations, and draft technical reports
- Audit-ready review workflow with evidence-linked interpretation
Solution Stack
Frontend: React + TypeScript
Backend: FastAPI + Pandas
AI Layer: summary, explanation, and report generation
Workflow: prepared case review and assessment orchestration
Output: structured compliance review and draft reporting
Lifecycle Role
Planning → Siting → Deployment → Compliance → Monitoring
Mesonet Siting Intelligence Platform
GIS Decision Support · Standards-Based Siting · Spatial Scoring
Overview
A GIS-based decision support system for evaluating and optimizing mesonet station siting using standards-based criteria and spatial analysis. The platform is designed to assess existing station locations, identify optimal new sites, and quantify siting quality through reproducible scoring and ranking workflows.
Core Capabilities
- Distance-from-obstruction analysis for buildings and vegetation
- Terrain slope and elevation screening using DEM inputs
- Land use / land cover validation for siting suitability
- Road and right-of-way proximity analysis
- Weighted scoring and ranking of candidate locations
- Standards-based evaluation using AASC, WMO, and customizable criteria
Planned Stack
Frontend: React + map interface
Backend: FastAPI / Python
GIS Layer: GeoPandas, Rasterio, PyProj
Processing: raster and vector siting analysis
Output: suitability maps and ranked candidate locations
Lifecycle Fit
The siting platform complements the compliance engine by evaluating where stations should be located, while the compliance engine evaluates how existing networks perform.
Interested in building something similar?
Whether you need a weather intelligence dashboard, flood awareness system, emergency reporting workflow, compliance review platform, or standards-based siting intelligence solution, SiwaLab can help turn your data into practical operational systems.