Climate-Aware Machine Learning for Above-Ground Biomass Estimation

Abstract

This study explores the role of data science, machine learning, and artificial intelligence in addressing environmental challenges, specifically focusing on the estimation of Above-Ground Biomass (AGBM) using satellite imagery. The research aims to compare the effectiveness of temporal and spatial modelling techniques in AGBM estimation and to assess the utility of the AI-Climate Alignment Framework proposed by Kaack et al. (2022) in guiding environmentally responsible model development. A tree-based learner and a neural learner are trained on a small dataset, using a temporal and a spatial representation. The results show that the tree-based learner emits less carbon in inference, and outperforms the neural learner when training on small a small sample.