Early Forest Fire Detection
Democratize open and low-tech solutions for fighting wildfires, for the benefit of the ecosystems and the citizens.
Democratize open and low-tech solutions for fighting wildfires, for the benefit of the ecosystems and the citizens.
The project monitors wild salmon migration to ensure the number passing through meets state regulations, addressing threats from human activities like fisheries and dams.
Conservation efforts struggle to monitor forest elephants in dense rainforests, with acoustic monitoring providing a promising solution via accurate, user-friendly detection systems that aid in population monitoring and anti-poaching efforts while mitigating human-elephant conflicts.
A backend engineering mission with the EarthRanger team at Ai2, separating read and write query paths, partitioning the database, and speeding up the APIs that power real-time conservation across 900+ protected areas.
Drone-based monitoring of coastal erosion, sedimentation and land cover change on the Wadden Sea coast, served through an interactive web portal.
An innovative use of sonar imagery to monitor and analyze the migration patterns of smolt salmon as they journey from freshwater to the ocean
Free, open-source software we build with field teams. Explore each one and try it for yourself.
Democratizing open and low-tech solutions for fighting wildfires. An early detection solution that is open source, efficient, automatic, energy-efficient, economical and modular.
Underwater cameras, sonar and drones combined with innovative AI technology to enable precise and automated salmon counting in rivers.
Open-source desktop app for analyzing camera trap data. Runs 100% locally, so your sensitive wildlife data never leaves your computer.
How we trained a U-Net to map land cover on the Wadden Sea coast from drone photos, from ecologists' labels at any level of detail to a seamless map of every survey.
How CODAP measures erosion and sedimentation on the Wadden Sea coast by differencing drone elevation models, cleaning out the noise without losing real change, and reading the result with transects.
An intuition-first, visual guide to metric learning and how machines learn to tell individual animals apart for conservation re-identification.
Our tools are free and open source. Support our work or partner with us, and help bring conservation technology to the field teams who need it.