Synergistic Use of Time-Series Remote Sensing, Deep Learning, and AI for Land Transformation Monitoring

Dear Colleagues,

This Special Issue explores the powerful combination of time-series remote sensing data and deep learning artificial intelligence (AI) to monitor and analyze land transformation processes. Global landscapes are undergoing unprecedented changes due to factors like urbanization, climate change, deforestation, and agricultural intensification. These shifts demand advanced, reliable tools to track land use and land cover dynamics over time. Time-series remote sensing provides a wealth of consistent, long-term data, unaffected by short-term disruptions, capturing trends across seasons and years. Deep learning AI complements this by offering sophisticated methods for pattern recognition, predictive modeling, and automated data processing, enabling precise detection and interpretation of complex land changes. Together, they form a transformative approach to understanding land transformation and supporting sustainable management practices.

The objective of this Special Issue is to compile pioneering research that showcases how these technologies synergize to address critical land transformation challenges. It fits squarely within the scope of Land, emphasizing innovative methodologies, practical applications, and theoretical advancements. By integrating these tools, researchers can unlock new ways to monitor environmental shifts, inform urban planning, and enhance resource conservation efforts on local and global scales.

Potential topics include the following:

• Time-series remote sensing for detecting and mapping land use and land cover changes;
• Deep learning AI-driven classification and forecasting of land transformation trends;
• Integration of multi-sensor data to improve monitoring accuracy and resolution;
• Deep learning techniques for detailed land cover mapping and temporal analysis;
• SAR imagery applications for land transformation and natural hazard monitoring;
• Natural hazard detection and assessment using time-series and AI approaches;
• Scalable AI-remote sensing frameworks for large-scale land transformation studies;
• Case studies demonstrating real-world impacts and management strategies.

We invite submissions of original research papers, in-depth review articles, and comprehensive case studies that highlight the practical and scientific value of these tools. Contributions are encouraged from diverse disciplines, including environmental science, geospatial analysis, computer science, and urban studies, to foster interdisciplinary solutions for a sustainable future.

Dr. Mahdi Panahi
Guest Editor

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.