DeepCrop: Quantification of Carbon Stocks via Deep Learning (DeepCrop)
Basic data for this project
Type of project: Own resources project
Duration at the University of Münster: since 01/04/2020
Description
Recent technological developments in deep learning and drone-borne Lidar scanners pave the way for constraining the uncertainty inherent to quantify and project ecosystems' carbon (C) stocks. With a rising demand for biomass, DeepCrop aims to precisely measure above ground biomass and to estimate C sinks in croplands and forests. The ambition is to bridge expertise of experimental and computer scientists to develop novel tools for the automated processing of Lidar data utilizing deep learning and drones. Joint work with the University of Copenhagen (Katerina Trepekli, Thomas Friborg, Christian Igel). This project is, in part, supported by the Villum Foundation and the Data+ program of the University of Copenhagen.
Keywords: deep learning; carbon stocks; carbon sinks; biomass; drones; lidar scanners
Project management at the University of Münster
| Gieseke, Fabian | Chair of Machine Learning and Data Engineering (Prof. Gieseke) (MLDE) |
Project partners outside the University of Münster
- University of Copenhagen (UCPH)Denmark