Advancements in Deep Learning for Remote Sensing: Exploring Planetary and Earth Observation Applications

Dear Colleagues,

The rapid evolution of remote sensing technologies, combined with groundbreaking advances in deep learning, has revolutionized the way we acquire, analyze, and interpret imagery from both terrestrial and extraterrestrial environments. Today, state-of-the-art satellite and airborne sensors generate vast amounts of data that require sophisticated processing techniques to extract meaningful information.

Deep learning methods have emerged as powerful tools for automating feature extraction, improving classification accuracy and enhancing image analysis. In planetary science, these techniques enable detailed investigations of Martian terrains, lunar craters, and other celestial surfaces, offering new insights into geological processes beyond Earth. Similarly, deep learning applications in earth observation are transforming the analysis of land cover changes, environmental monitoring, natural hazard detection, and urban development assessments—enhancing our ability to study and manage Earth’s dynamic systems.

The primary aim of this Special Issue is to compile and showcase innovative research that leverages deep learning techniques to address complex challenges in remote sensing, spanning both planetary and terrestrial applications. By focusing on topics such as the analysis of terrestrial terrains alongside earth observation tasks like land cover change detection, environmental monitoring, natural hazard assessment, and urban development analysis, this Special Issue seeks to demonstrate how advanced computational methods can enhance our understanding of diverse environments.

This topic directly aligns with the scope of MDPI’s Remote Sensing, which emphasizes the development of novel sensors, methodologies, and data analysis techniques for a wide array of remote sensing applications. This journal is dedicated to publishing high-quality research that advances remote sensing technologies and their practical applications. By exploring the integration of deep learning—a rapidly evolving area in artificial intelligence—into both space and earth observation studies, this Special Issue not only meets the journal’s focus on innovative remote sensing methodologies but also encourages interdisciplinary collaboration. Ultimately, it aims to broaden the scope of remote sensing research and stimulate advancements that benefit both planetary science and earth observation.

We are delighted to invite you to contribute to this Special Issue titled “Advancements in Deep Learning for Remote Sensing: Exploring Planetary and Earth Observation Applications”. This Special Issue aims to foster interdisciplinary dialogue and collaboration by highlighting research that bridges these two dynamic areas. We welcome original research, reviews, and case studies that advance the state of the art and provide innovative solutions to contemporary challenges in remote sensing.

This Special Issue invites original research, review articles, and case studies on the application of deep learning techniques to remote sensing challenges across diverse domains. Recent advancements in sensor technologies and artificial intelligence have unlocked new opportunities for analyzing and interpreting remote sensing data. This issue aims to bridge planetary science and earth observation by showcasing state-of-the-art deep learning methodologies for complex remote sensing tasks.

On the planetary side, we welcome studies focused on terrestrial planets (Mercury, Venus, Earth, and Mars), including terrain characterization, crater detection, volcano analysis, landslide mapping, cave identification, and other geological features. For earth observation, we seek innovative contributions in agricultural monitoring and precision farming, time series analysis, image classification and segmentation, integration of multi-source data, and novel methodologies that enhance information extraction from remote sensing datasets.

By bringing together cutting-edge research at the intersection of deep learning and remote sensing, this Special Issue aims to foster interdisciplinary collaboration, drive innovation, and advance the application of remote sensing technologies for both planetary and terrestrial studies. We encourage contributions that not only push methodological boundaries but also offer practical insights for real-world applications.

We look forward to your contributions that will push the frontiers of both planetary exploration and earth observation.

Dr. Riccardo La Grassa
Dr. Ignazio Gallo
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.

• deep learning
• earth observation
• planetary science
• feature detection
• mapping
• super-resolution
• classification