John Brandt is a Senior Data Science Associate n the Restoration team at the World Resources Institute. He works on developing novel datasets to monitor forest and landscape restoration activities with remote sensing. His teams work analyzes petabytes of optical and radar satellite imagery with computer vision and deep learning approaches to map large-scale tree cover change.

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.

Fabian is a geospatial analyst and ecologist focused on studying large-scale changes in ecosystems using advanced imaging, machine learning, and mapping techniques. He earned his PhD from Auckland University of Technology, where his research investigated the abrupt treeline patterns in the Southern Alps using drone imagery, plant physiology, and agent-based modelling. At Dragonfly Data Science, Fabian is using remote sensing and machine learning applications to study ecological changes. His work includes developing deep learning solutions for native bird song annotation or qualitative and quantitative assessments of terrestrial landscapes. Fabian is committed to the use of open source solutions, enhancing accessibility and innovation in ecological research.

Kevan Cote is a contractor to the Eco-index programme focusing on the development of ecosystem detectors by combining remote sensing data with artificial intelligence. Kevan has a cross-disciplinary background in production data analysis and product development engineering. He has worked on projects ranging from using AI to assist in animal welfare measurement automation to measuring indices for human well-being remotely as well as developing an AI framework for environmental monitoring for an indigenous audience. His diverse expertise promotes agility and a fresh perspective on practical application of AI techniques.

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.