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Phd Fellow in Deep Learning and Drone-based Lidar Systems for Assessing Carbon Stocks in Cropland and Forest Areas (MapCland).
  • Despite the strong power of deep learning techniques on extracting patterns from large datasets and the concurrent realization of the high uncertainty in C stock estimations there is a lack of well established methods in acquiring and processing Lidar point cloud data of high density with deep learning for accurately monitoring ecosystems’ carbon dynamics. The MapCland project targets exactly this line of research and applies an interdisciplinary approach building upon the combined competences of team partners from the Department of Geosciences and Natural Resource Management (IGN) and the Department of Computer Science (DIKU) at the University of Copenhagen. The aim is to initiate new groundbreaking research by applying and extending deep learning methods to explore relevant information from large datasets from Lidar. For further details please read the attached pdf. (read full)
  • Close Date:
  • Contact: Katerina Trepekli email