New Progress in Big Earth Data

Dear Colleagues,

Recent advances in Big Data, machine learning, and high-performance computing (HPC) are revolutionizing Earth science research. With extensive datasets from satellite imagery, geophysical sensors, and environmental monitoring, these technologies offer new opportunities to tackle global challenges like climate change and resource management. This Special Issue focuses on innovative methods and applications that utilize Big Earth Data, machine learning, and scalable computing architectures to advance geoscience and environmental monitoring.

We welcome research articles, dataset descriptions, communications, and reviews that explore novel data-driven approaches, machine learning applications, and HPC solutions. Studies leveraging parallel computing, GPU architectures, and advanced sensing technologies are especially encouraged. 

Potential topics include the following:

• Machine learning in hydrocarbon exploration: Using DAS, DTS, and electromagnetic surveys combined with machine learning to enhance subsurface analysis for hydrocarbon exploration. Studies can also explore GPU-accelerated simulations for faster, more accurate outcomes.
• Geothermal energy exploration: Data-driven methods that use fiber optic sensing, GPR, and machine learning to improve geothermal reservoir management. Contributions that show how parallel computing speeds up data processing are welcome.
• Remote sensing and geospatial data analysis: Applying machine learning to process satellite images, LiDAR, and other data for Earth system monitoring. HPC techniques to handle large datasets and improve efficiency are of special interest.
• AI in disaster management: Using AI models to predict natural hazards like floods and earthquakes. Emphasis on real-time data fusion, aided by GPU-accelerated computing, to improve response times.
• Climate and ecosystem monitoring: Machine learning and big data applications in climate science, including predictive models for biodiversity and resource management. Submissions may focus on the role of cloud-based HPC for handling massive datasets.
• Advanced sensing for subsurface analysis: Machine learning with distributed acoustic, temperature, and strain sensing for geological surveys, using GPU-based data processing for faster insights.
• Predictive models for resource management: Exploring machine learning models for managing natural resources like water and forests, highlighting parallel computing for improved scalability.
• FAIR datasets in Earth sciences: Open access geospatial datasets and how to manage and share them using HPC, with a focus on adhering to FAIR principles for better data accessibility and reuse.

This Special Issue aims to showcase real-world applications of Big Earth Data, machine learning, and HPC to address pressing environmental and geoscientific challenges.

Dr. Aditya Chakravarty
Dr. Juanle Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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