Collin Power
Features
AI-Powered Wildlife Detection
SeeOtter employs advanced AI algorithms to detect and count wildlife in aerial imagery with remarkable precision and speed. This automation reduces the time and labor required for manual counting, allowing conservationists to focus on analysis and action."
High Accuracy and Efficiency
Our application ensures high accuracy in wildlife detection, minimizing errors and improving the reliability of population data. This efficiency enables quicker decision-making and more effective conservation strategies.
User-Friendly Interface
SeeOtter is designed with an intuitive interface, making it accessible to users with varying levels of technical expertise. The application provides clear visualizations and easy-to-navigate tools for analyzing wildlife data.
Integration with Existing Workflows
SeeOtter seamlessly integrates with existing data collection and management workflows. It supports various data formats and can be customized to fit the specific needs of different conservation projects.
Rigorous Validation
SeeOtter undergoes rigorous manual validation processes to ensure the accuracy and reliability of its AI predictions. This validation allows users to eliminate false positives while verifying the accuracy of the model's predictions.
Case Studies
01
Sea Otter Population Surveys
In collaboration with the U.S. Geological Survey (USGS), the U.S. Fish & Wildlife Service (USFWS), and the National Park Service (NPS), SeeOtter has been instrumental in improving the accuracy and efficiency of sea otter photo-based population surveys in Alaska. The SE AK sea otter survey that took place in the summer of 2022 covered thousands of kilometers and generated over 250,000 photos, which were all processed using the SeeOtter application. The application is also being used for surveys of Cook Inlet and Glacier Bay National Park. The AI-powered workflow has reduced manual counting efforts and provided reliable data for conservation strategies
02
Ecological Monitoring Initiatives
SeeOtter has also been tested on multiple other ecological systems. While not yet used in practice, there have been promising results identifying elk and deer from drone imagery, whales from satellite imagery, different tree species, wildlife nest locations, as well as various other species of birds and marine mammals