Ghjulia Sialelli: SatML for large-scale above-ground biomass estimation

The combination of remote sensing and machine learning has made it possible to map forest properties at an unprecedented scale and resolution. In this presentation, I will focus on the application of deep learning techniques to estimate above-ground biomass (AGB), a key metric for tracking forest carbon and ecosystem dynamics.

I will begin by introducing our recently published, machine-learning-ready dataset. It features high-resolution (10m) multi-modal satellite imagery, paired with AGB reference values from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission. Key aspects include the carefully selected geographic coverage, thoughtful integration of diverse satellite data sources, and the establishment of performance baselines using standard deep learning models.

Next, I will describe our ongoing efforts to build on said baselines. This was done both through feature and model engineering. I will also mention some promising yet unsuccessful approaches, highlighting some key challenges of the task at hand. Finally, I will discuss future directions, including incorporating uncertainty estimation and exploring the potential for generating a global above-ground biomass map.

Ghjulia Sialelli is a second-year PhD student in the Photogrammetry and Remote Sensing Lab of ETH Zurich, and an ETH AI Center Doctoral Fellow. Her research lies at the frontier of computer vision and remote sensing. In particular, her current work focuses on high-resolution global mapping of above-ground biomass. She is also an organizer of the AI + Environment Summit, a full-day event designed to inspire, ignite and innovate work in AI for the environment.