Earth System Data Cubes: Avenues for advancing Earth system research

• URL: http://arxiv.org/abs/2408.02348v1
• Date: Mon, 5 Aug 2024 09:50:16 GMT
• Title: Earth System Data Cubes: Avenues for advancing Earth system research
• Authors: David Montero, Guido Kraemer, Anca Anghelea, César Aybar, Gunnar Brandt, Gustau Camps-Valls, Felix Cremer, Ida Flik, Fabian Gans, Sarah Habershon, Chaonan Ji, Teja Kattenborn, Laura Martínez-Ferrer, Francesco Martinuzzi, Martin Reinhardt, Maximilian Söchting, Khalil Teber, Miguel D. Mahecha,
• Abstract summary: Earth System Data Cubes ( ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust format. ESDCs achieve this by organising data into an analysis-ready format with atemporal grid. There exist barriers to realising the full potential of data in light of novel cloud-based technologies.
• Score: 4.408949931570938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Foundation Models for Remote Sensing and Earth Observation: A Survey [101.77425018347557]
  This survey systematically reviews the emerging field of Remote Sensing Foundation Models (RSFMs) It begins with an outline of their motivation and background, followed by an introduction of their foundational concepts. We benchmark these models against publicly available datasets, discuss existing challenges, and propose future research directions.
  arXiv  Detail & Related papers  (2024-10-22T01:08:21Z)
• Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research [90.91438597133211]
  We introduce WarpSci, a framework designed to overcome crucial system bottlenecks in the application of reinforcement learning. We eliminate the need for data transfer between the CPU and GPU, enabling the concurrent execution of thousands of simulations.
  arXiv  Detail & Related papers  (2024-08-01T21:38:09Z)
• A Survey of Generative Techniques for Spatial-Temporal Data Mining [93.55501980723974]
  This paper focuses on the integration of generative techniques into spatial-temporal data mining. The paper provides a comprehensive analysis of generative technique-based spatial-temporal methods. It also introduces a standardized framework specifically designed for the spatial-temporal data mining pipeline.
  arXiv  Detail & Related papers  (2024-05-15T12:07:43Z)
• Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges [4.497634148674422]
  Intemporal big data can foster new opportunities to solve problems that have not been possible before. The distinctive characteristics of big data pose new challenges for deep learning technologies.
  arXiv  Detail & Related papers  (2023-10-30T19:12:51Z)
• Dynamic Spatio-Temporal Summarization using Information Based Fusion [3.038642416291856]
  We propose a dynamic-temporal data summarization technique that identifies informative features in key timesteps and fuses less informative ones. Unlike existing methods, our method retains both raw and summarized timesteps, ensuring a comprehensive view of information changes over time. We demonstrate the versatility of our technique across diverse datasets, encompassing particle-based flow simulations, security and surveillance applications, and biological cell interactions within the immune system.
  arXiv  Detail & Related papers  (2023-10-02T20:21:43Z)
• Exploring the Potential of AI-Generated Synthetic Datasets: A Case Study on Telematics Data with ChatGPT [0.0]
  This research delves into the construction and utilization of synthetic datasets, specifically within the telematics sphere, leveraging OpenAI's powerful language model, ChatGPT. To illustrate this data creation process, a hands-on case study is conducted, focusing on the generation of a synthetic telematics dataset.
  arXiv  Detail & Related papers  (2023-06-23T15:15:13Z)
• Graph Neural Network for spatiotemporal data: methods and applications [7.612070518526342]
  Graph neural networks (GNNs) have emerged as a powerful tool for understanding data with dependencies to each other. This article aims to provide an overview of the technologies and applications of GNNs in thetemporal domain.
  arXiv  Detail & Related papers  (2023-05-30T02:27:17Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review [33.40031994803646]
  This survey aims to present a systematic overview in DL-based multimodal RS data fusion. Sub-fields in the multimodal RS data fusion are reviewed in terms of to-be-fused data modalities. The remaining challenges and potential future directions are highlighted.
  arXiv  Detail & Related papers  (2022-05-03T09:08:16Z)
• Batch Exploration with Examples for Scalable Robotic Reinforcement Learning [63.552788688544254]
  Batch Exploration with Examples (BEE) explores relevant regions of the state-space guided by a modest number of human provided images of important states. BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka robot.
  arXiv  Detail & Related papers  (2020-10-22T17:49:25Z)
• Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review [68.15472610671748]
  Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance.
  arXiv  Detail & Related papers  (2020-05-23T20:57:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.