WildTrack Assist is a prototype system for supporting field search operations by combining drone status, field team information, sighting reports, alerts, and AI-assisted report validation.

The project is not a real emergency-response product.
It is a simulation-oriented system prototype designed to show how search operation data can be collected, organized, prioritized, and visualized.

Field search operations can suffer from fragmented information.

Common problems include:

• separated drone, team, and report data
• delayed situational awareness
• search gaps caused by drone battery limitations
• duplicated or unreliable sighting reports
• unclear priority between search zones

WildTrack Assist focuses on building an integrated operation layer rather than claiming full autonomous detection.

Drone simulator
+ field team status
+ sighting reports
+ alert logic
+ report validation
→ operation dashboard and search support

The core idea is to support decision-making by organizing field data into a structured workflow.

• drone status monitoring
• field team status
• alert and event tracking
• search zone visualization
• operation timeline support

• image-based sighting reports
• location-aware report registration
• duplicate report checking
• trust score calculation
• metadata-based validation support

• drone movement simulation
• battery decrease logic
• fake thermal event generation
• simulated drone feed

• search priority support
• dispatch support
• heatmap-related service structure
• zone and team coordination logic

wildtrack-assist/
├─ backend/
│  ├─ app/
│  │  ├─ routers/
│  │  ├─ models/
│  │  ├─ schemas/
│  │  ├─ services/
│  │  ├─ db/
│  │  ├─ core/
│  │  └─ main.py
│  ├─ tests/
│  └─ requirements.txt
│
├─ frontend/
│  ├─ src/app/
│  └─ package.json
│
├─ drone_simulator/
│  ├─ simulator.py
│  ├─ battery_logic.py
│  ├─ fake_feed_generator.py
│  └─ thermal_mock.py
│
├─ ai_tools/
│  ├─ image_check/
│  └─ report_ranker/
│
└─ docs/
   ├─ architecture.md
   ├─ api-spec.md
   ├─ scenario.md
   └─ ui-wireframe.md

• Python
• FastAPI
• WebSocket
• SQLAlchemy-style project structure
• pytest

• Next.js
• TypeScript

• Python drone simulator
• image metadata parsing
• duplicate report checking
• trust score calculation
• thermal mock event generation

cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload

cd frontend
npm install
npm run dev

cd drone_simulator
python simulator.py

A field search operation is simulated with:

1. drone movement
2. battery decrease
3. thermal event generation
4. sighting report submission
5. duplicate report detection
6. trust score calculation
7. alert and zone status updates

The project demonstrates how multiple field signals can be organized into a single operation-support workflow.

This repository includes:

• FastAPI backend entry point
• routers for alerts, drones, sightings, teams, zones, and WebSocket
• service layer for dispatch, drone status, reports, heatmap logic, and verification
• backend tests for drone, sighting, and team logic
• drone simulator modules
• AI-assisted report ranking modules
• frontend dashboard scaffold
• architecture and API documents

This project does not claim:

• real drone integration
• certified emergency response usage
• production-grade AI detection
• verified thermal camera pipeline
• offline field operation support
• complete authentication and authorization
• production database persistence

It is a prototype for system architecture, simulation, and operation-support workflow design.

• connect ROS2 / PX4 drone simulation
• add real-time map visualization
• improve authentication and authorization
• add persistent database storage
• add multi-drone coordination logic
• add offline-first mobile field reporting
• add dashboard screenshots and operation flow GIFs
• improve report validation with real image datasets

WildTrack Assist is a prototype search-operation support system.

The current version focuses on backend structure, drone simulation, report validation logic, and dashboard scaffolding.