Next-Gen Biodiversity Monitoring with Drones and AI

There is a desperate need to efficiently monitor changes in biodiversity as we attempt to mitigate species loss associated with climate change and other human impacts. Drones can cover significantly larger areas than human teams, but data processing remains an issue. This student team will work to create a set of tools that will greatly increase survey speed and accuracy for assessing large areas for evidence of habitation e.g. bird nests. This student team will work to create, from scratch, a set of tools to enable drone based biodiversity monitoring. Through this project, this student team will work to: - Create a web app for inspecting a 3D scene in detail using crowd-sourcing and AI (stretch goal) - Build/Integrate image labeling tools - Build relationships between 2D images and 3D scene for de-duplication and enhanced inspection - Automate photogrammetry for building 3D scene from inputs - implement coverage guarantees for biodiversity measurement as well as potential search and rescue applications. A successful project would be the first step towards bringing a new and impactful product to market in a field that is wholly focused on mitigating the impacts of human activity and climate change. More specifically the outcomes this student team will work to accomplish include: - A deployed, user-ready web app. - Stretch goal: Machine Learning integration for faster or more accurate coverage.

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