Application of Artificial Intelligence in Land Use and Land Cover Mapping—3rd Version

Dear Colleagues,

The integration of artificial intelligence (AI) and remote sensing has revolutionized land use and land cover (LULC) mapping, significantly enhancing our ability to monitor and analyze landscape changes at multiple spatial and temporal scales. The rapid advancements in AI-driven methodologies, including machine learning (ML) and deep learning (DL), have enabled automated feature extraction, improved classification accuracy, and robust predictive modeling of environmental and anthropogenic changes.

With the increasing availability of high-resolution satellite imagery, data from unmanned aerial vehicles (UAVs), and cloud computing platforms such as Google Earth Engine (GEE) and AWS, researchers now have unprecedented opportunities to leverage AI for large-scale geospatial analysis. Recent innovations, including generative adversarial networks (GANs), diffusion models, and large language models (LLMs), further expand AI's capabilities in remote sensing applications. These advancements facilitate synthetic data generation, transfer learning, and real-time environmental monitoring, providing critical insights for sustainable land management and climate adaptation strategies.

Given the success of the previous volumes of this Special Issue, this third edition aims to capture cutting-edge developments in AI applications for LULC mapping, highlighting novel methodologies, interdisciplinary approaches, and emerging trends in AI-based geospatial analysis. It aims to explore innovative AI techniques that enhance the accuracy, efficiency, and automation of LULC classification and change detection. We encourage submissions involving multi-source remote sensing data fusion and interpretation, automated and high-precision land cover classification, AI-driven change detection and predictive modelling, cloud computing for geospatial processing and analysis, AI-assisted policy recommendations for land management, and socio-environmental impact assessments using AI-enhanced remote sensing.

We welcome a diverse range of submissions, including original research articles, reviews, letters, technical notes, and highlight articles, covering the following topics:

• Deep learning for hyperspectral and multispectral data analysis;
• Advanced machine learning for LULC classification;
• Transfer learning and domain adaptation techniques;
• Object-based image analysis and pattern recognition;
• Automated change detection and prediction;
• AI-based land cover transition modeling;
• Synthetic dataset generation for training AI models;
• Cloud computing and big data analytics;
• AI for climate change and sustainable development;
• AI-driven deforestation and land degradation monitoring;
• Large language models (LLMs) in geospatial intelligence.

By bringing together experts in AI and remote sensing, this Special Issue aims to advance our understanding, foster collaboration, and highlight groundbreaking methodologies of geospatial intelligence and environmental sustainability.

Dr. Sawaid Abbas
Prof. Dr. Janet Nichol
Dr. Faisal M. Qamer
Dr. Hamid Mehmood
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.

• land use and land cover (LULC) mapping
• artificial intelligence (AI) in remote sensing
• machine learning and deep learning
• cloud computing (GEE, AWS)
• object detection in remote sensing images
• hyperspectral and multispectral data analysis
• change detection and monitoring
• big data analytics in Earth observation
• generative AI for geospatial applications
• large language models (LLMs) for geospatial intelligence
• urban morphology and landscape analysis
• sustainable development goals (SDGs) and AI

• Application of Artificial Intelligence in Land Use and Land Cover Mapping II in Remote Sensing (10 articles)