SDZWA Conservation Technology Lab
Developing open-source technology and machine learning tools for wildlife conservation
SDZWA Conservation Technology Lab
Developing open-source technology and machine learning tools for wildlife conservation
The Conservation Tech Lab develops cutting-edge technology solutions for wildlife conservation and ecological research. Our work spans machine learning for camera trap analysis, edge-AI field devices, bioacoustics tools, and animal tracking systems.
All of our projects are open-source, promoting collaboration and knowledge sharing within the conservation technology community. We focus on practical, field-deployable solutions that help researchers and conservationists better understand and protect wildlife.
Machine learning models and tools for automatically classifying and analyzing wildlife in camera trap images and videos.
Field-deployable devices with on-board AI processing and LoRa connectivity for real-time wildlife detection and monitoring in remote locations.
Tools for capturing and detecting wildlife sounds, and for building training data sets and training models.
AI-powered systems for identifying individual animals using visual features.
Platforms and tools for organizing, visualizing, and analyzing large-scale ecological data from various field devices.
Detection and tracking systems using thermal cameras for monitoring wildlife in challenging environmental conditions.
Animl comprises a variety of machine learning tools for analyzing ecological data. The R library includes a set of functions to classify subjects within camera trap field data and can handle both images and videos.
Animl comprises a variety of machine learning tools for analyzing ecological data. This Python package includes a set of functions to classify subjects within camera trap field data and can handle both images and videos.
Code for Edge-AI-enabled field wildlife camera that processes images directly in the field for real-time wildlife detection and analysis.
Dashboard for organizing, visualizing, and analyzing images received from ScrubCams in the field.
Tools for capturing, analyzing, and parsing bioacoustic data to help researchers study wildlife through sound recordings.
GUI tool for human validation of AI-powered animal re-identification, helping researchers verify and improve AI predictions for individual animal identification.
Polar bear maternal den observation system designed for monitoring and studying polar bear denning behavior in remote Arctic environments.
Tools to automatically analyze images and videos from telemetering field cameras and to take responsive action based on detected wildlife.
GUI application for managing data from camera traps used in field ecology projects, streamlining data organization and analysis workflows.
Thermal imagery detection and tracking tools for monitoring wildlife using thermal cameras.
Tools for training custom animal object detectors, enabling researchers to create specialized models for detecting specific species.
Edge-AI and device for wildlife detection and alerts, a compact version designed for deployment in resource-constrained environments.
Tools for reading and writing to Camera Base database through R, facilitating data management for camera trap research.
A package to aide in the utilization of EarthRanger as a way to capture and display data of interest from camera traps and other data loggers.
Code for animal-borne devices and bench post-processing associated with logging and analysing inertial measurements, including for activity recognition.