Deep learning and process understanding for data-driven Earth system science

By Markus Reichstein, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, N. Carvalhais, Prabhat. 2019

4,521 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Global and Planetary Change, Signal Processing

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Citations in the corpus

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning (2020, methodology): “For example, Kratzert et al. (2018) used LSTM to predict runoff for the next day, and Hu et al. (2018) used LSTM to predict the runoff for the next 6 hr. Recently, Reichstein et al. (2019) proposed that language translation modeling methods could be applied to time series tasks.”
A high-resolution canopy height model of the Earth (2022, neutral): “, 2021) and has great potential to advance environmental monitoring (Reichstein et al., 2019; Tuia et al., 2022), but its application to global remote sensing is technically challenging due to the large data volume (Reichstein et al.”
Machine learning and artificial intelligence to aid climate change research and preparedness (2019, result): “We concur with Schneider et al (2017) and Reichstein et al (2019) that as ML and AI algorithms develop, these will open vast opportunities to aid climate research, facilitating and going beyond dimension reduction.”
Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6? (2020, background): “Moreover, new machine learning techniques are a promising avenue forward for such multivariate approaches and for constraining uncertainties in multi-model projections (Schlund et al., accepted) with the aim of further improving climate modelling and analysis (Reichstein et al., 2019).”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “Machine learning (ML) models, which have already found tremendous success in commercial applications, are beginning to play an important role in advancing scientific discovery in domains traditionally dominated by mechanistic (e.g. first principle) models [22, 112, 117, 126, 127, 138, 214, 261].”
SpectralGPT: Spectral Remote Sensing Foundation Model (2023, background): “…(MS/HS) remote sensing (RS) data the preferred tool of choice and a key component in a wide range of Earth Observation (EO) applications [4], [5], [6], [7], including land use/land cover mapping, ecosystem monitoring, weather forecasting, energy resource development, biodiversity…”
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends (2020, background): “Furthermore, a well-founded understanding of DL as Earth observation researcher is highly necessary when it comes to incorporating knowledge of physical and ecological relationships into DL models [7,9]—an ability that will make the difference between purely data driven Earth observation with a tend”
Towards Physics-informed Deep Learning for Turbulent Flow Prediction (2019, background): “Recently, data-driven methods, including deep learning, have demonstrated great success in the automation, acceleration, and streamlining of highly compute-intensive workflows for science [28].”
A review of machine learning applications in wildfire science and management (2020, background): “Two recent perspectives have also made compelling arguments for the application of deep learning in earth system sciences Reichstein et al. (2019) and for tackling climate change Rolnick et al. (2019).”
Perspectives in machine learning for wildlife conservation (2021, neutral): “This “hybrid models” paradigm (54, 56) is one of the most exciting avenues in modern ML and promises real collaboration between domains of application and ML, especially when coupled with algorithmic designs”
Tackling Climate Change with Machine Learning (2019, background): “[445] Markus Reichstein, Gustau Camps-valls, Bjorn Stevens, Martin Jung, Joachim Denzler, and Nuno Carvalhais.”