Advancements in Land Cover Classification and Machine Learning Techniques for Urban Areas Using Remote Sensing Big Data

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About this Research Topic

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Background

The research in land cover classification and machine learning for urban areas using remote sensing data also addresses challenges related to remote sensing big data. With the increasing availability of high-resolution satellite imagery and other remote sensing technologies, there is a need to efficiently process and analyze massive amounts of data. Remote sensing big data refers to the extensive data generated by various sensors and platforms, including satellite imagery, airborne sensors, and LiDAR. This data presents computational and analytical challenges due to its volume, variety, and velocity. Traditional techniques struggle with such large datasets. Thus, this Research Topic focuses on developing scalable algorithms and methodologies to handle remote sensing big data effectively. Machine learning techniques are vital for automated and efficient analysis of large volumes of remote sensing data. Advanced algorithms and parallel processing approaches are utilized to manage and extract valuable information, enabling accurate land cover classification and analysis in urban areas.

This Research Topic aims to address challenges in land cover classification and machine learning techniques for urban areas using remote sensing data, proposing advancements to improve accuracy, explainability, and efficiency in analysis.

To achieve this, advanced methodologies and algorithms are needed to effectively classify and analyze complex and dynamic land cover patterns in urban environments. Machine learning techniques automate the analysis of large volumes of remote sensing data. Recent advances involve integrating multiple data sources like multispectral imagery, LiDAR data, and auxiliary datasets to enhance classification accuracy. Object-based classification and deep learning techniques capture detailed and contextual information from remote sensing data. The goal also encompasses overcoming challenges posed by remote sensing big data, utilizing scalable algorithms and parallel processing techniques. These advancements ensure efficient processing and analysis of large datasets, addressing computational and analytical obstacles associated with remote sensing big data. By achieving these objectives, this Research Topic is expected to provide accurate and timely information on land cover dynamics in urban areas, facilitating improved urban planning, environmental monitoring, and sustainable development.

The scope of this Research Topic encompasses advancements in land cover classification and machine learning techniques for urban areas using remote sensing big data. We welcome contributions that focus on the following themes:
• Development and evaluation of novel and explainable machine learning algorithms for land cover classification in urban areas.
• Integration of multiple data sources, such as multispectral imagery, LiDAR data, ASR data and auxiliary datasets, to improve classification accuracy.
• Exploration of object-based classification and deep learning techniques for capturing detailed and contextual information from remote sensing data.
• Application of advanced feature extraction and selection methods to enhance land cover classification performance.
• Investigation of change detection algorithms for monitoring land cover dynamics in urban environments.
• Assessment and validation techniques for evaluating the accuracy and reliability of land cover classification results.
Manuscripts should present innovative approaches and substantial results, and provide insights into the application of these techniques in urban planning, environmental monitoring, and sustainable development.

Keywords: Land cover classification, Machine learning, Remote sensing big data, Satellite Image processing, Feature extraction, Change detection, Classification algorithms, Deep learning, High-resolution imagery, LiDAR data, SAR data, Spectral analysis, Urban planning and management

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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