Official codebase for MammAlps
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Updated
Jun 5, 2025 - Python
Official codebase for MammAlps
Pytorch implementation for "Iterative Human and Automated Identification of Wildlife Images" (Nature -Machine Intelligence, 2021)
📷🦔 CamTrapML Python Library for Detecting, Classifying, and Analysing Camera Trap Imagery.
[ICCV2019] Challenge - Computer Vision for Wildlife Conservation Solution
WildDetect is a powerful wildlife detection and census system for aerial imagery. It helps conservationists, researchers, and organizations analyze wildlife populations, generate geographic visualizations, and produce actionable reports—all with easy-to-use command-line tools.
This is repository containing a full pipeline (from annotation to training) for building an orangutans detector.
CAMCALT (Complex Animal Movement Capture and Live Transmission) is a forest surveillance and monitoring system designed to capture complex animal movements and provide live video feed wirelessly from any part of the world. It aims to prevent hunting and poaching, enhancing forest security.
AI-powered environmental monitoring system for marine and terrestrial ecosystems using computer vision, real-time analysis, and FAIR data principles.
"Animal Behavior & Disease Detection: Utilize YOLO and MobileNetV2_img_classifier for real-time animal behavior tracking and disease identification. A valuable tool for wildlife researchers and conservationists. 🦁🔍🦠 #WildlifeAI #DeepLearning #Conservation"
Monitor endangered wildlife and assess potential threats using autonomous drones
MegaDetector Desktop: Simple Interface for Detection of Humans, Animals and Vehicles in Camera Trap Imagery
Sample implementation of the Anitra data API client.
Detecting Dormice in Images Using Object Detection and Transfer Learning
Combating Human wildlife conflict using AI/ML
Python library for downloading Animal tracking data from Anitra and Movebank platforms.
A wildlife monitoring system using EfficientNet-B3 and XAI (GradCAM) for species classification, detailed HTML/PDF reports, safety protocols, markdown insights, and CrewAI integration for advanced analysis.
Leverages big data and machine learning for wildlife conservation using GBIF species data. PySpark is used for preprocessing, K-Means for clustering, and Decision Trees for habitat prediction. Tableau visualizes species distribution, biodiversity, and conservation insights.
This repository contains scripts and resources to develop a GUI that helps biologist identify individual Archey's frogs (Leiopelma archeyi).
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