11 citations as recorded by crossref.

1. Comparing CNNs and Random Forests for Landsat Image Segmentation Trained on a Large Proxy Land Cover Dataset T. Boston et al. https://doi.org/10.3390/rs14143396
2. A national-scale assessment of land use change in peatlands between 1989 and 2020 using Landsat data and Google Earth Engine—a case study of Ireland W. Habib & J. Connolly https://doi.org/10.1007/s10113-023-02116-0
3. Land Cover Classification Model Using Multispectral Satellite Images Based on a Deep Learning Synergistic Semantic Segmentation Network A. Gharahbagh et al. https://doi.org/10.3390/s25071988
4. Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022 J. Sobrino et al. https://doi.org/10.3390/land13071072
5. High-Resolution Land Use Land Cover Dataset for Meteorological Modelling—Part 1: ECOCLIMAP-SG+ an Agreement-Based Dataset G. Bessardon et al. https://doi.org/10.3390/land13111811
6. The Cycle 46 Configuration of the HARMONIE-AROME Forecast Model E. Gleeson et al. https://doi.org/10.3390/meteorology3040018
7. Using machine learning to produce a cost-effective national building height map of Ireland to categorise local climate zones E. Keany et al. https://doi.org/10.5194/asr-19-13-2022
8. Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul https://doi.org/10.3390/atmos13020180
9. Quantifying peatland land use and CO2 emissions in Irish raised bogs: mapping insights using Sentinel-2 data and Google Earth Engine W. Habib et al. https://doi.org/10.1038/s41598-024-51660-0
10. The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models H. Lean et al. https://doi.org/10.1002/qj.4858
11. Improving 2 m temperature forecasts of numerical weather prediction through a machine learning-based Bayesian model H. Zhang et al. https://doi.org/10.1007/s00703-024-01056-6