Earth Observation Data: The Digital Transformation of Society

Dear Colleagues,

Access to Earth observation data and the ability to use it are essential for innovation and growth. We live in a data-driven society, and data-driven innovation is bringing important and concrete benefits, including better policymaking, smarter mobility, advanced public services, and more environmentally efficient development. The digital transformation of society also requires a transformation of the methods and technologies used to access, merge, process, and use Earth observation data. There is a need for a paradigm change, moving from the physical to virtual space (cyber–physical world). Most of the tasks are carried out in the traditional Spatial Data Infrastructure (SDI) paradigm. More advanced and innovative interaction patterns (notably, the Digital Twin) must be applied to respond to the needs of the application developers. New concepts, such as Data cubes, Analysis-Ready-Data, and Data Spaces, have been introduced to address the diverse big data challenges. Finally, the data-driven AI revolution has increasingly demonstrated that today (and more so in the near future), the concepts and infrastructures for data and its analysis must converge, ensuring users all the services and scalability that are necessary to create information and actionable intelligence.

This Special Issue aims to present new methodologies and technological solutions to address the challenges posed by big data related to Earth observations for their fusion and processing to create actionable intelligence. The proposed works should discuss how they have overcome the traditional paradigm of SDI and instead support the innovative concept of data-driven AI and digital twins. Finally, a discussion on the sustainability and scalability of the proposed solutions and methodologies would be very important.

Scientists, scholars, and practitioners are invited to submit their original research papers on the following interrelated and not exclusive topics:

• Remote sensing applications for society smartification.
• Big Earth observation data for building digital twins of the Earth.
• Earth Observation Analysis-ready data and scalable computing platforms.
• (Towards) the concept of Earth Observation Data Space.
• Remote sensing and edge computing.
• Remote sensing data to train data-driven AI.
• Earth observation data and scientific models as part of the Metaverse.

Dr. Stefano Nativi
Dr. Gregory Giuliani
Dr. Joan Masó
Paolo Mazzetti
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 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data-driven AI
• automated knowledge extraction
• high-performance platforms and computing systems
• big data and data mining
• digital twin
• earth observation
• computational approaches to modeling, estimation, and inference
• remote sensing applications
• open science
• environmental efficiency