Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks

Dear Colleagues,

With the advancement of machine learning and artificial intelligence technologies in the current era of big data, scientists can acquire a better understanding of our surrounding environment by synergizing different datasets and using properly trained and validated algorithms, for example, satellite imageries and datasets, local and urban monitoring networks, fine-scale emission inventories, meteorological and atmospheric attributes, numerical modeling, and post-processed outputs. Measurements obtained from low-cost sensors and raw observational datasets can also be integrated into the model development process to fine-tune specific dependent parameters of the entire algorithmic framework, thus enhancing the validity and reliability of the developed algorithms. This is particularly useful when attempting to conduct large-scale spatial and temporal assessments, as well as associating relevant predicted results to enhance health qualities and implement relevant policies. Further, insights obtained from the algorithmic development process do not have any geographical limits, and the appropriate combination of various models with the latest data analysis tools has proved to return better retrieval results in the long run. Therefore, it is of particular interest to explore how digital advancement could gradually lead to more effective and systematic environmental retrieval and monitoring.

This Special Issue seeks to publish and promote new and innovate ideas in the development of trustworthy algorithmic frameworks for the purpose of environmental monitoring and environmental data analysis, as well as the application of these frameworks in practice to conduct large-scale trend analyses and assessments. Original research articles and literature reviews of relevant topics are highly welcome, contributing to a joint effort to steer technological advancement forward, and as a result create a sustainable world in the foreseeable future.

Dr. Hugo Wai Leung Mak
Dr. Francesc Pozo
Guest Editors

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• deep learning/machine learning algorithms
• environmental informatics and analyses
• algorithmic design in environmental retrieval
• atmospheric monitoring and assessment
• land use monitoring and assessment
• traffic monitoring and assessment
• large-scale spatial and temporal environmental dynamics
• data assimilation/fusion in large-scale model development
• artificial intelligence and big data analytics
• microsensor technology in environmental model development