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WildTrack: AI-Powered Wildlife Conservation Platform for Enthusiasts and Professionals.

Inspiration

Wildlife poaching and habitat destruction remain critical threats to endangered species worldwide. Traditional conservation efforts often rely on fragmented data and delayed reporting, making it difficult to protect animals in real-time. We were inspired by the idea of empowering both citizen scientists and field researchers with a unified platform that turns everyday wildlife sightings into actionable conservation intelligence while ensuring sensitive location data doesn't fall into the wrong hands.

What it does

WildTrack is a full-stack mobile and web platform that enables users to report wildlife sightings, which are then processed using machine learning to create intelligent geofence zones around animal habitats. The system aggregates data from multiple sources—citizen scientists, field researchers, and social media—to build a comprehensive wildlife intelligence network.

Key Features:

Role-Based Data Access: Field researchers see exact GPS coordinates and individual sightings, while public users only see general habitat boundaries—preventing poachers from exploiting precise location data
DBSCAN Clustering: Automatically groups 69,507+ GPS observations into meaningful habitat zones for 119+ endangered species using scikit-learn's DBSCAN algorithm with Haversine distance calculations
Real-Time Data Ingestion: Collects observations from citizen scientists via React Native mobile app with GPS tracking and photo uploads
Interactive Maps: Visualize species distributions, habitat boundaries, and cluster centers using react-native-maps with custom markers and polygons
AI Image Detection: Auto-detects animal species from uploaded photos using Gemini
Animal Encyclopedia: Searches for animal information online and displays nice charts with a detailed descriptions using Gemini
Reddit Data Aggregation: Scrapes wildlife subreddits (r/wildlife, r/animalid, r/conservation, etc.) using Reddit's public JSON API to extract endangered species sightings and location data
Analytics Dashboard: Real-time charts showing species distribution, sighting trends, and top endangered animals in user's area using react-native-chart-kit
Spatial Grid Clustering: Optimized clustering algorithm using spatial grid partitioning (0.1° ≈ 11km cells) for O(n) performance instead of O(n²)
In-Memory Caching: 5-minute TTL cache for observation data to reduce database load and improve API response times

How we built it

Backend Architecture

Framework & API:

FastAPI with async/await for high-performance REST API
Uvicorn ASGI server with automatic API documentation at /docs
Pydantic models for request/response validation
SlowAPI for rate limiting (configurable per endpoint)
CORS middleware for cross-origin requests

Database:

Supabase (PostgreSQL) with PostGIS extension for geospatial queries
Row-Level Security (RLS) policies for role-based access control
14 SQL migration scripts managing schema evolution:
- observations table: Stores GPS coordinates, species, timestamps
- zones table: Pre-computed habitat boundaries with convex hull polygons
- reddit_sightings table: Scraped social media data with metadata
- endangered_species table: Reference data for 119+ species
- journals and logs tables: User-generated observation records
- profiles table: User roles (field_researcher vs public)

Machine Learning Pipeline:

DBSCAN Clustering: scikit-learn implementation with custom distance metrics
- Epsilon: 0.01 degrees (≈1km) for habitat zone detection
- Min samples: 3 points per cluster
- Processes 2,000-4,000 points per clustering run (sampled from 69k+ total)
Convex Hull Generation: SciPy for habitat boundary polygon creation
Spatial Grid Optimization: Custom grid-based clustering (0.1° cells) reducing complexity from O(n²) to O(n)
Haversine Distance: NumPy-optimized calculations for accurate geospatial measurements

Data Processing:

Background task processing for Reddit scraping (FastAPI BackgroundTasks)
Batch processing with pagination (100 records/page) for large datasets
In-memory caching with 5-minute TTL to minimize database queries
Point sampling algorithms to limit processing to 4,000 points for performance

External Integrations:

Reddit JSON API scraping (no authentication required, public endpoints)
Geopy/Nominatim for reverse geocoding location names
Hugging Face transformers for AI image classification
PyTorch for deep learning inference

Frontend Architecture

Mobile App:

React Native 0.81.5 with Expo SDK 54
Expo Router for file-based navigation
TypeScript for type safety
React Native Maps for interactive map visualization
Expo Location for GPS tracking with continuous updates
Expo Image Picker for camera integration
AsyncStorage for local token persistence

State Management:

React Hooks (useState, useEffect, useMemo, useCallback)
Custom API service layer with error handling
Optimistic UI updates for better UX

UI Components:

Custom modals with ScrollView for log details
Species-specific icon mapping with color coding
Cluster visualization with pulse animations
Real-time location tracking with user position markers
Chart visualizations (PieChart, BarChart) for analytics

Authentication:

Supabase Auth with JWT tokens
AsyncStorage for session persistence
Automatic token refresh handling
Role-based UI rendering (researcher vs public views)

Data Scraping

Reddit Integration:

Public JSON API endpoints (no OAuth required)
Scrapes 9 wildlife-focused subreddits
Species detection via keyword matching against 119+ endangered species
Location extraction using geopy/Nominatim from post text
Background processing with FastAPI BackgroundTasks
Duplicate detection via reddit_id to prevent re-scraping
Metadata extraction: upvotes, comments, timestamps, author info

Data Flow:

Scraper queries subreddits via JSON API
Filters posts mentioning endangered species
Extracts location data (if available)
Saves to reddit_sightings table
Frontend queries via /api/v1/reddit-sightings endpoint

API Endpoints

/api/v1/auth/* - Authentication (signup, login, profile sync)
/api/v1/journals - CRUD operations for user journals
/api/v1/logs - Wildlife observation logging
/api/v1/zones/* - Habitat zone queries (clusters, boundaries, nearby species)
/api/v1/reddit-sightings - Social media data aggregation
/api/v1/image-verification - AI image authenticity detection
/api/v1/animal-search - Species encyclopedia and search

Challenges we ran into

Balancing Accessibility vs Security: Designing a system that's useful for public conservation awareness while protecting animals from poachers required careful role-based access control implementation
Performance at Scale: Processing 69,507 observations required optimizing DBSCAN from O(n²) to O(n) using spatial grid partitioning
Git Merge Conflicts: Managing multiple feature branches with different directory structures and conflicting dependencies
Reddit API Limitations: Reddit's free API tier restrictions led us to use public JSON endpoints, requiring custom scraping logic
Real-Time Location Tracking: Implementing continuous GPS updates without draining battery required careful optimization of location polling intervals
Type Safety: TypeScript type errors with backend API responses required extensive type definition work

Accomplishments that we're proud of

Successfully clustered 69,507 GPS observations across 119 species into 1,000+ meaningful habitat zones using DBSCAN
Implemented role-based data access that protects exact animal locations from potential misuse while maintaining public awareness
Built a complete full-stack application with React Native mobile app, FastAPI REST API, Supabase database, and ML pipeline
Created test endpoints that allow rapid development without full auth setup
Processed real-world wildlife data from multiple sources into actionable conservation insights
Optimized clustering performance from O(n²) to O(n) using spatial grid algorithms, enabling real-time cluster updates
Integrated multiple data sources: citizen reports, field researcher data, and Reddit social media scraping
Built AI-powered features: automatic species detection from photos and image verification

What we learned

Security-First Design: The importance of considering data sensitivity in conservation tech—protecting animals requires protecting their location data
Full-Stack Integration: Connecting React Native frontend → FastAPI backend → Supabase database → ML models requires careful API design and error handling
Geospatial Algorithms: Implementing efficient clustering for large-scale GPS data requires understanding spatial indexing and distance metrics
Performance Optimization: Caching, sampling, and algorithmic optimization are critical when processing 70k+ data points
API Design: RESTful endpoints with proper error handling, rate limiting, and documentation improve developer experience

What's next for WildTrack

Integration with Conservation Organizations: Partner with wildlife reserves and NGOs to integrate their existing data systems
Real-Time Alerts: Notify rangers when sightings occur outside expected habitat zones (potential poaching indicator)
Temporal Analysis: Track migration patterns and seasonal movements using time-series analysis
Mobile Push Notifications: Alert users about nearby endangered species sightings
Advanced ML Models: Fine-tune species detection models on domain-specific wildlife datasets
Web Dashboard: Expand beyond mobile to web-based analytics platform for researchers
Data Export: Allow researchers to export zone boundaries and observation data for GIS software
Community Features: Enable users to comment on sightings and share conservation tips