mlsanityData to 14 Oct 2026

Earth System Data Cubes: Avenues for advancing Earth system research

arXiv 2408.02348 ↗First posted 5 Aug 2024cs.CV cs.DBcited by 3

Not on the map. Why?

Abstract (arXiv)

Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatio-temporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloud-based technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 26 of its 173 references that are in the map. First posted on arXiv 5 Aug 2024.

Builds on

26 of 173 references.

References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2023Segment Anythingcited by 8,234
  5. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  6. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
  7. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  8. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,175
  9. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 664
  10. 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 420
  11. 2020RSVQA: Visual Question Answering for Remote Sensing Datacited by 203
  12. 2020TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learningcited by 177
14 more
  1. 2023The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shotcited by 83
  2. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
  3. 2023Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networkscited by 61
  4. 2020PyTorch Metric Learningcited by 57
  5. 2021Will Artificial Intelligence supersede Earth System and Climate Models?cited by 37
  6. 2023Discovering Causal Relations and Equations from Datacited by 36
  7. 2021EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction taskcited by 34
  8. 2020Predicting into unknown space? Estimating the area of applicability of spatial prediction modelscited by 29
  9. 2021Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineationcited by 19
  10. 2023GLH-Water: A Large-Scale Dataset for Global Surface Water Detection in Large-Size Very-High-Resolution Satellite Imagerycited by 16
  11. 2022Deep generative model super-resolves spatially correlated multiregional climate datacited by 9
  12. 2022ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Modelscited by 8
  13. 2022Satellite Image Time Series Analysis for Big Earth Observation Datacited by 5
  14. 2024DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremescited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modellingcited by 0
  2. 2025Transformers vs. Recurrent Models for Estimating Forest Gross Primary Productioncited by 0
  3. 2026VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulationcited by 0