Multi-Sensor Earth Observation and Machine Learning for Land Use and Land Cover Analysis

Dear Colleagues,

The monitoring of land use and land cover (LULC) has become increasingly critical for addressing global environmental challenges, including climate change, biodiversity loss, food security, and sustainable resource management. Recent advances in remote sensing technologies—from high-resolution satellite systems and hyperspectral sensors to SAR and LiDAR platforms—have revolutionized our ability to observe Earth's surface at unprecedented spatial and temporal resolutions. Concurrently, artificial intelligence (AI) and machine learning techniques have emerged as powerful tools for processing vast quantities of remote sensing data, enabling automated classification, change detection, and predictive modeling with remarkable accuracy.

Despite these technological advances, significant challenges remain in translating remote sensing and AI capabilities into actionable information for environmental monitoring, policy-making, and sustainable land management. The integration of multi-source remote sensing data, development of robust AI algorithms that generalize across diverse landscapes, and validation of products against ground truth data continue to require innovation.

The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights into the integration of remote sensing technologies and AI methods for advancing land use and land cover monitoring. We seek contributions that demonstrate innovative approaches, present novel applications, or address methodological challenges in this rapidly evolving field, particularly those with implications for sustainable land management and environmental decision-making.

This Special Issue will welcome manuscripts that link the following themes:

• Novel AI and deep learning architectures for LULC classification and change detection;
• Multi-sensor data fusion approaches combining optical, SAR, LiDAR, and hyperspectral imagery;
• Time-series analysis for monitoring agricultural practices, deforestation, urban heat islands and urbanization dynamics;
• Validation and uncertainty assessment of AI-driven LULC products;
• Integration of public Earth observation data from platforms such as Copernicus Sentinel missions, Landsat, and commercial satellites;
• Explainable AI and interpretability in remote sensing applications;
• Operational monitoring systems for environmental policy support and sustainable development goals;

We look forward to receiving your original research articles and reviews.

Dr. Ivan Racetin
Dr. Andrija Krtalić
Dr. Ljiljana Seric
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.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly 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 2600 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.

• urban growth monitoring
• deforestation monitoring
• explainable AI (XAI)
• land use and land cover (LULC) mapping
• ecosystem assessment
• multi-source satellite data
• AI-driven land cover classification
• urban heat islands detection and monitoring