Revolutionizing Earth Observation: Convolutional Neural Networks Innovations in Remote Sensing and Environmental Monitoring

Dear Colleagues,

We are thrilled to present the Special Issue "Revolutionizing Earth Observation: Convolutional Neural Networks Innovations in Remote Sensing and Environmental Monitoring", which aims to highlight recent advances in deep learning methods that are revolutionizing how we process and interpret satellite data for environmental monitoring.

Convolutional Neural Networks (CNNs) have revolutionized the way we process and interpret remote sensing data, offering sophisticated solutions for a wide range of tasks including image enhancement, classification, and monitoring of environmental changes. These deep learning models excel in capturing complex spatial and temporal patterns from high-dimensional satellite imagery, enabling more accurate and efficient analysis compared to traditional methods. CNNs are particularly well-suited for handling the large volumes and diverse characteristics of remote sensing data, such as varying resolutions, spectral bands, and sensor types. Their ability to learn hierarchical features and adapt to different domains has made them indispensable tools in Earth observation, facilitating advancements in areas like land cover classification, vegetation monitoring, and change detection.

The application of CNNs in remote sensing is transforming how we monitor and understand our environment. Thanks to their ability to rapidly process data and accurately identify spatial patterns, CNNs are helping to address critical challenges in environmental monitoring, climate studies, and disaster response. These techniques are not only improving the accuracy of remote sensing products but also making it possible to scale analyses to global levels, thus offering new insights into Earth system dynamics. With the increasing availability of high-resolution satellite data and the growing demand for real-time monitoring, CNNs will continue to play a central role in advancing Earth observation capabilities, providing valuable tools for decision-making in environmental management, urban planning, and sustainable development.

The primary objective of this Special Issue is to highlight the latest advancements in the use of Convolutional Neural Networks (CNNs) for remote sensing and Earth observation applications. By focusing on cutting-edge deep learning techniques, this issue seeks to explore how CNNs are addressing complex challenges in satellite image processing, including noise reduction, data fusion, and feature extraction. We aim to foster the exchange of ideas between the remote sensing and machine learning communities, promoting interdisciplinary research that leads to more accurate and efficient Earth-monitoring systems.

Alongside model innovations, we warmly invite papers that demonstrate the real-world impact of CNN-based approaches in environmental monitoring, land use classification, vegetation and water body detection, and disaster management. The Special Issue will also encourage discussions on how to scale and deploy CNN techniques for large-scale Earth observation, addressing issues such as model interpretability, training efficiency, and real-time processing.

Furthermore, we invite you to contribute your innovative solutions that leverage CNNs to enhance the reproducibility and accessibility of remote sensing products, ensuring that these models can be widely adopted in both academic and practical contexts.

Suggested Submissions and Topics Include (but are not limited to) the following:

• CNN-based methods for image enhancement and super resolution in remote sensing;
• Land use and land cover classification using deep convolutional architectures;
• Change detection and multitemporal analysis with CNNs;
• Semantic and instance segmentation of satellite and aerial imagery;
• Fusion of multisensor and multiresolution data through deep learning;
• Lightweight and efficient CNN models for onboard and real-time processing;
• Explainable AI and interpretability in CNNs applied to Earth observation;
• Transfer learning and domain adaptation in remote sensing scenarios;
• CNN applications in agriculture, forestry, hydrology, and urban monitoring.

Dr. Antonio Mazza
Dr. Massimiliano Gargiulo
Dr. Sergio Vitale
Dr. Matteo Ciotola
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.

• artificial intelligence
• deep learning
• convolutional Neural Networks (CNNs)
• generative Adversarial Networks (GANs)
• earth observation
• remote sensing
• image processing
• data fusion
• super resolution