Application of Machine Learning in Land Use and Land Cover

Dear Colleagues,

This Special Issue entitled “Application of Machine Learning in Land Use and Land Cover” explores the transformative impact of machine learning (ML) techniques on the assessment, monitoring, and forecasting of land use and land cover (LULC) dynamics. As urbanization, deforestation, and climate change continue to exert pressure on terrestrial ecosystems, accurate and timely LULC information becomes increasingly essential for effective environmental management and policy-making.

This Special Issue showcases a diverse range of studies that demonstrate how ML algorithms—such as supervised and unsupervised learning, deep learning, and ensembled methods—are leveraged to analyze remote sensing data, enhance classification accuracy, and derive insights into land use changes over time. The application of these techniques enables researchers to overcome traditional limitations, such as the manual interpretation of satellite imagery or the challenges in processing large datasets.

Key topics addressed in this Special Issue include the following:

• Methodological Advances: Innovations in ML methodologies tailored for LULC applications, emphasizing algorithm performance, data preprocessing, and feature selection.
• Case Studies: Real-world applications of ML in various geographical contexts, showcasing how these approaches can effectively detect land use changes, classify LULC types, and improve the accuracy of land cover maps.
• Integration with Big Data: The role of big data and cloud computing in supporting comprehensive LULC assessments, enabling the integration of multi-temporal, multi-spectral, and high-resolution datasets.
• Predictive Modeling: Studies focused on predicting future LULC scenarios based on historical data, providing valuable insights for resource management and sustainable development.
• Challenges and Future Directions: Discussions on the challenges faced in ML applications, such as data quality, interpretability, and scalability, along with suggestions for future research directions that can enhance the robustness of ML methodologies in environmental studies.
• Through this Special Issue, we aim to highlight the significant potential of machine learning in advancing our understanding and management of land use and land cover, fostering interdisciplinary collaboration among researchers, and practitioners in the fields of environmental science, geography, and urban planning.

Dr. Xidong Chen
Dr. Xiao Zhang
Guest Editors

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• machine learning
• land cover classification
• land cover change detection
• remote sensing
• geography