AiGeo

Geospatial AI & Real-time Biodiversity Monitoring Backend

System Overview

AiGeo is a high-performance platform custom-designed for conservation management, forest monitoring, and wildlife analytics. The system harmoniously integrates AI processing, R scientific analysis, and an advanced Spatial Database (GIS) into a single, scalable architecture.

Technology Stack

Layer Key Technologies Function / Role
Frontend & GIS Vue.js, Vite, MapLibre GL Interactive interface for maps, operational dashboards, and field apps (PWA).
API & Real-time Node.js, Express, Socket.IO Main gateway, authentication, and real-time GPS tracking updates.
Visual AI Brain Python, YOLO, PyTorch Camera trap image detection, species recognition, and drone monitoring.
Scientific Analytics R, sf, terra, vegan Calculation of biodiversity indices, animal movement corridors, and ecology stats.
Database PostgreSQL + PostGIS Advanced spatial data storage and lightning-fast geometric queries.
Message Broker Redis + BullMQ Background queue system to manage heavy workloads between Node.js, Python, and R.

Development Roadmap

Phase 1

Database & Infrastructure

Establishing the foundation for AiGeo using containerization.

  • Setup PostgreSQL + PostGIS for the spatial database.
  • Configure Redis for memory management and task queues.
Phase 2

Central Nervous System

Building the main API Gateway and orchestrator.

  • Develop REST API with JWT Authentication.
  • Implement image/file upload handling via Multer.
  • Integrate BullMQ to dispatch tasks to Python/R workers.
Phase 3

Visual AI Engine

Deploying the artificial intelligence for image processing.

  • Create a Python worker listening to the Redis Queue.
  • Execute YOLO models on camera trap or drone imagery.
  • Save spatial coordinates and wildlife classifications to PostGIS.
Phase 4

Scientific Analytics Engine

Processing ecological models and spatial statistics.

  • Process thousands of spatial points using sf and terra.
  • Calculate Biodiversity Indices and analyze animal Home Ranges.
  • Persist analytical reports to the database.
Phase 5

Real-time Visualization

Displaying live intelligence to the control center.

  • Implement push notifications and live tracking via Socket.IO.
  • Render spatial data using MapLibre GL within Vue.js.
Phase 6

Offline-First Field App

Ensuring operational continuity for rangers without internet.

  • Convert the Vue.js frontend into a Progressive Web App (PWA).
  • Utilize Workbox and IndexedDB for local data storage.
  • Implement background auto-sync upon internet connection recovery.