| ClimateBench v1. 0: A benchmark for data‐driven climate projections D Watson‐Parris, Y Rao, D Olivié, Ø Seland, P Nowack, G Camps‐Valls, ... Journal of Advances in Modeling Earth Systems 14 (10), e2021MS002954, 2022 | 36 | 2022 |
| Predicting the solar potential of rooftops using image segmentation and structured data D de Barros Soares, F Andrieux, B Hell, J Lenhardt, J Badosa, S Gavoille, ... NIPS Proceedings, 2021 | 4 | 2021 |
| Predicting the solar potential of rooftops using image segmentation and structured data DB Soares, F Andrieux, B Hell, J Lenhardt, J Badosa, S Gavoille, ... arXiv preprint arXiv:2106.15268, 2021 | 3 | 2021 |
| ClimateBench: A benchmark dataset for data-driven climate projections D Watson-Parris, Y Rao, D Olivié, Ø Seland, PJ Nowack, G Camps-Valls, ... Authorea Preprints, 2022 | 2 | 2022 |
| Leveraging surface observations and passive satellite retrievals of cloud properties: Applications to cloud type classification and cloud base height retrieval J Lenhardt, J Quaas, D Sejdinovic, D Klocke EGU24, 2024 | | 2024 |
| Marine cloud base height retrieval from MODIS cloud properties using machine learning J Lenhardt, J Quaas, D Sejdinovic EGUsphere 2024, 1-26, 2024 | | 2024 |
| From MODIS cloud properties to cloud types using semi-supervised learning J Lenhardt, J Quaas, D Sejdinovic EGU General Assembly Conference Abstracts, EGU-13250, 2023 | | 2023 |
| ClimateBench: A benchmark for data-driven climate projections D Watson-Parris, Y Rao, D Olivié, O Seland, PJ Nowack, G Camps-Valls, ... AGU Fall Meeting Abstracts 2022, GC22I-0688, 2022 | | 2022 |
| Combining cloud properties and synoptic observations to predict cloud base height using Machine Learning J Lenhardt, J Quaas, D Sejdinovic EGU General Assembly Conference Abstracts, EGU22-7355, 2022 | | 2022 |
