Climate-Aware Machine Learning for Above-Ground Biomass Estimation
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 m...
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| Format: | Article |
| Language: | Danish |
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Aalborg University Open Publishing
2024-12-01
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| Series: | Geoforum Perspektiv |
| Online Access: | https://discurso.aau.dk/index.php/gfp/article/view/8376 |
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| Summary: | 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.
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| ISSN: | 1601-8796 2245-8433 |