Mediaboss Marketing

Bonavita, M. et al. Machine learning for earth system observation and prediction. Bull. Am. Meteorol. Soc. 102, E710E716 (2021).

Article Google Scholar

Tuia, D. et al. Toward a collective agenda on AI for earth science data analysis. IEEE Geosci. Remote Sens. Mag. 9, 88104 (2021).

Article Google Scholar

Li, Y., Li, M., Li, C. & Liu, Z. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Sci. Rep. 10, 9952 (2020).

Article Google Scholar

Hedelt, P., Efremenko, D. S., Loyola, D. G., Spurr, R. & Clarisse, L. Sulfur dioxide layer height retrieval from Sentinel-5 Precursor/TROPOMI using FP_ILM. Atmos. Meas. Tech. 12, 55035517 (2019).

Article Google Scholar

Copernicus Services. https://www.copernicus.eu/en.

Daudt, R. C., Le Saux, B., Boulch, A. & Gousseau Y. Weakly supervised change detection using guided anisotropic diffusion. Mach. Learn. https://doi.org/10.1007/s10994-021-06008-4 (2021).

Castillo-Navarro, J., Le Saux, B., Boulch, A., Audebert, N. & Lefvre, S. Semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite, dataset analysis and multi-task network study. Mach. Learn. https://doi.org/10.1007/s10994-020-05943-y (2021).

Sumbul, G., Ravanbakhsh, M. & Demir, B. BigEarthNet-MM: a large-scale, multimodal, multilabel benchmark archive for remote sensing image classification and retrieval [software and data sets]. IEEE Geosci. Remote Sens. Mag. 9, 174180 (2021).

Article Google Scholar

Roscher, R., Bohn, B., Duarte, M. F. & Garcke, J. Explainable machine learning for scientific insights and discoveries. IEEE Access 8, 4220042216 (2020).

Article Google Scholar

Kang, J. et al. Learning convolutional sparse coding on complex domain for interferometric phase restoration. IEEE Trans. Neural Netw. Learn. Syst. 32, 826840 (2021).

Article Google Scholar

Arcucci, R., Zhu, J., Hu, S. & Guo, Y. K. Deep data assimilation: integrating deep learning with data assimilation. Appl. Sci. 11, 1114 (2021).

Article Google Scholar

Buizza, C. et al. Data learning: integrating data assimilation and machine learning. J. Comput. Sci. 58, 101525 (2022).

Article Google Scholar

Mack, J., Arcucci, R., Molina-Solana, M. & Guo, Y. K. Attention-based convolutional autoencoders for 3d-variational data assimilation. Computer Methods Appl. Mech. Eng. 372, 113291 (2020).

Article Google Scholar

Brajard, J., Carrassi, A., Bocquet, M. & Bertino, L. Combining data assimilation and machine learning to infer unresolved scale parametrization. Phil. Trans. R. Soc. A. https://doi.org/10.1098/rsta.2020.0086 (2021).

Geer, A. J. Learning earth system models from observations: machine learning or data assimilation?. Phil. Trans. R. Soc. A. https://doi.org/10.1098/rsta.2020.0089 (2021).

Farchi, A., Laloyaux, P., Bonavita, M. & Bocquet, M. Using machine learning to correct model error in data assimilation and forecast applications. Quart. J. R. Meteorol. Soc. https://doi.org/10.1002/qj.4116 (2021).

Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686707 (2019).

Article Google Scholar

Beucler, T. et al. Enforcing analytic constraints in neural networks emulating physical systems. Phys. Rev. Lett. 126, 098302 (2021).

Article Google Scholar

Nowack, P., Runge, J., Eyring, V. & Haigh, J. D. Causal networks for climate model evaluation and constrained projections. Nat Commun. https://doi.org/10.1038/s41467-020-15195-y (2020).

Watt-Meyer, O. et al. Correcting weather and climate models by machine learning nudged historical simulations. Geophys. Res. Lett. 48, e2021GL092555 (2021).

Article Google Scholar

Keisler, R. Forecasting Global Weather with Graph Neural Networks. Preprint at https://doi.org/10.48550/arXiv.2202.07575 (2022).

McGovern, A., Ebert-Uphoff, I., Gagne II, D. J. & Bostrom, A. The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences. Preprint at https://arxiv.org/abs/2112.08453 (2021).

Paul, S. & Ganju, S. Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning. Preprint at https://doi.org/10.48550/arXiv.2107.08369 (2021).

Schneider, R. et al. A satellite-based spatio-temporal machine learning model to reconstruct daily PM2.5 concentrations across Great Britain. Remote Sens. 12, 3803 (2020).

Article Google Scholar

Stafoggia, M. et al. Estimation of daily PM10 and PM2.5 concentrations in Italy, 20132015, using a spatiotemporal land-use random-forest model. Environ. Int. 124, 170179 (2019).

Article Google Scholar

Schneider, R. D. S. Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data. Int. J. Appl. Earth Obs. Geoinf. 88, 110 (2020).

Google Scholar

Kloog, I., Nordio, F., Coull, B. A. & Schwartz, J. Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA. Remote Sens. Environ. 150, 132139 (2014).

Article Google Scholar

Here is the original post:
ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction | npj Climate and...