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 |
| Published: |
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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| _version_ | 1832577741942161408 |
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| author | Aske Meineche |
| author_facet | Aske Meineche |
| author_sort | Aske Meineche |
| collection | DOAJ |
| description | 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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| format | Article |
| id | doaj-art-5c52c7ac16c74932b86d90e6e02aaf48 |
| institution | Kabale University |
| issn | 1601-8796 2245-8433 |
| language | Danish |
| publishDate | 2024-12-01 |
| publisher | Aalborg University Open Publishing |
| record_format | Article |
| series | Geoforum Perspektiv |
| spelling | doaj-art-5c52c7ac16c74932b86d90e6e02aaf482025-01-30T16:46:48ZdanAalborg University Open PublishingGeoforum Perspektiv1601-87962245-84332024-12-01234410.54337/ojs.perspektiv.v23i44.8376Climate-Aware Machine Learning for Above-Ground Biomass EstimationAske Meineche0StudentThis 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. https://discurso.aau.dk/index.php/gfp/article/view/8376 |
| spellingShingle | Aske Meineche Climate-Aware Machine Learning for Above-Ground Biomass Estimation Geoforum Perspektiv |
| title | Climate-Aware Machine Learning for Above-Ground Biomass Estimation |
| title_full | Climate-Aware Machine Learning for Above-Ground Biomass Estimation |
| title_fullStr | Climate-Aware Machine Learning for Above-Ground Biomass Estimation |
| title_full_unstemmed | Climate-Aware Machine Learning for Above-Ground Biomass Estimation |
| title_short | Climate-Aware Machine Learning for Above-Ground Biomass Estimation |
| title_sort | climate aware machine learning for above ground biomass estimation |
| url | https://discurso.aau.dk/index.php/gfp/article/view/8376 |
| work_keys_str_mv | AT askemeineche climateawaremachinelearningforabovegroundbiomassestimation |