WildLens: A Standalone Wildlife Monitoring System for Conservation

Project Description

Overview

WildLens is an open-source platform designed to assist conservationists and researchers in monitoring wildlife through AI-powered tools. The system supports audio, image, and GPS data processing to detect species, track movement patterns, and analyze habitat usage. It is designed to work offline or in low-connectivity environments, making it ideal for remote fieldwork.

Key Features

Data Loading and Processing:

Support for wildlife-specific data formats:

Audio: WAV/MP3 files for animal sounds.

Images: JPEG/PNG from camera traps.

GPS: CSV/GeoJSON for tracking animal movements.

Tools for cleaning and preprocessing raw data (e.g., noise reduction in audio, blur detection in images).

AI-Powered Species Identification:

Image classification using pre-trained models like MobileNet or YOLOv8.

Audio classification using spectrogram-based CNNs for detecting animal calls.

Real-time detection of species from live feeds or pre-recorded data.

Visualization Tools:

Generate spectrograms for sound analysis.

Interactive maps (using OpenStreetMap) to visualize animal movements and sightings.

Heatmaps and temporal activity plots to analyze behavior patterns.

Customizable Pipelines:

Modular design allowing users to integrate their own datasets or models.

Easy-to-use APIs for extending functionality.

Use Cases

Monitoring endangered species through camera traps and audio sensors.

Identifying migration routes and habitat usage patterns.

Detecting poaching activities or environmental threats in real time.

Technical Stack

Backend: Python with FastAPI or Flask for API development.

AI Models:

Image classification: YOLOv8 or MobileNetV2.

Audio classification: Librosa + CNN-based models.

Frontend: React.js or Vue.js for a user-friendly interface.

Database: SQLite/PostgreSQL for storing metadata (e.g., species labels, timestamps).

Visualization:

Plotly/Dash for spectrograms and time-series plots.

Leaflet.js or OpenLayers for interactive maps.

Example Workflow

Upload raw data (audio files, images, GPS logs).

Preprocess the data (e.g., denoise audio, crop images).

Run AI models to classify species or detect anomalies.

Visualize results (e.g., maps of movement patterns, heatmaps of activity).

Export processed data and insights as reports.

Potential Extensions

Integrate drone-based thermal imaging for tracking nocturnal animals.

Add support for real-time alerts (e.g., detecting poaching threats).

Expand the library of pre-trained models for more ecosystems.