Johannes Rahlf is aresearcher at the Norwegian National Forest Inventory (NFI), with a PhD from the Norwegian University of Life Sciences. He specializes in remote sensing and large-scale mapping of forest resources using both airborne and satellite 3D and optical data. Johannes is particularly interested in incorporating artificial intelligence into remote and proximal sensing for forestry and forest monitoring. As part of NIBIOs SmartForest center, he oversees the project's cloud service, ForestSens.com, facilitating the application of AI algorithms for sensor data processing.

Christopher Schiller studied Geoecology at Karlsruhe Institute of Technology (KIT) in Karlsruhe, Germany. Afterwards, he started his PhD at Free University Berlin in the Remote Sensing and Geoinformatics working group, led by Prof. Fabian Fassnacht. There, his topic is Deep Learning-based Forest Disturbance Monitoring using Sentinel-2 time series.

Melanie is a data analyst specialising in geospatial and computer vision at Scion. Melanie has a background in geography and geospatial science with an interest in the intersection between the physical and social sciences. Melanie's research at Scion revolves around the use of remote sensing, GIS, and AI for a wide range of forestry applications.

Amandine is a PhD student in the Department of Geography at the University of Cambridge and a member of the Cambridge Centre for Earth Observation. She graduated from the French Engineering School CentraleSupélec with an MSc in Engineering, and Imperial College London with an MSc in Environmental Engineering and Business Management. Before joining the University of Cambridge, she worked as a Young Graduate Trainee at the European Space Agency Centre for Earth Observation in Italy. She has also worked as a consultant for the environmental law NGO ClientEarth; been part of the Environmental Monitoring Project Team with the Alan Turing Institute, the UK's national institute for data science and artificial intelligence; and is a Teaching Assistant for classes on statistics, cartography, and 'Nature-based solutions to Climate Change' at the University of Cambridge. She is interested in using Earth Observation to inform policy in the broad area of Sustainable Development Goals.

Jonathan specializes in fine-scale remote sensing technologies such as drone-based digital aerial photogrammetry and terrestrial lidar. Trees, drones, and lidar points galore! Using fine-scale remote sensing techniques to quantify processes and change at a local level to then develop models for landscape-level characterization of vegetation structure regarding fire effects and habitat.

Pratima Khatri-Chhetri is a PhD Candidate at the Forest Resilience Lab, the University of Washington. Her research is focused on developing deep learning models for monitoring the impact of climate change in various forest ecosystems including boreal and mixed conifer. She has recently published high-resolution remote sensing benchmark data and deep learning models for mapping individual tree mortality for mixed-conifer forest ecosystem. Her research interests include large scale ecological modeling using high resolution remote sensing data and deep learning models.