GeoAI and EO Big Data Driven Advances in Earth Environmental Science

Dear Colleagues,

With the blowout development of earth observation (EO) technologies (e.g., optical and microwave remote sensing, LiDAR, GNSS, and geospatial sensor web) in recent years, EO data have accumulated quickly to the petabyte-level, which provides the greatest opportunities yet for earth environmental science, but meanwhile pose the grandest challenges yet for the processing of these EO big data. Owing to the development and advancement of Artificial Intelligence (AI), especially Geospatial AI (GeoAI) methods and techniques (e.g., spatiotemporal machine learning and deep learning), the modeling, processing and analysis of the EO big data have arrived at a new paradigm. By integrating the EO big data and the GeoAI methods, more comprehensive and in-depth investigations into earth environmental science become possible.

This Special Issue aims at methodological or applied studies using GeoAI and EO big data for investigating the matter, energy, and information in the hydrosphere, lithosphere, biosphere, and atmosphere on the surface of the Earth. The scale can be local, regional, or global, but large scale and long time-series studies will be preferred. In addition, monitoring and analysis studies of the key thematic indicators for high-impact events or disasters such as droughts, floods, earthquakes, tsunamis, and volcanic eruptions are especially welcome.

Articles may address, but are not limited, to the following topics:

• Analysis and mining of EO (e.g., optical and microwave remote sensing, LiDAR, GNSS, and geospatial sensor web) big data;
• Novel GeoAI models and frameworks (e.g., spatiotemporal machine learning/deep learning) for modeling/processing/analyzing of EO big data;
• Retrievals of environmental variables (e.g., precipitation, land/sea surface temperature, soil moisture, aerosols, vegetation index, sea ice concentration, sea surface salinity, snow cover, chlorophyll-a concentration);
• Environmental variables monitoring and prediction;
• Postprocessing of environmental variable retrievals (e.g., multi-source data fusion, downscaling, and image restoration);
• Extracting information from EO big data (e.g., classification, segmentation, target detection, dynamic monitoring, and prediction);
• Natural hazards (e.g., drought, flood, waterlogging, wildfire, landslide, surge earthquake, tsunami, and volcanic eruption) monitoring and evaluation;
• Crop yield estimation;
• Land cover land use mapping and scenario prediction;
• Monitoring and analysis of high-impact events (e.g., epidemic outbreaks, oil spills, gas pipeline ruptures, carbon neutrality, and emission peak).

Dr. Min Huang
Dr. Changjiang Xiao
Prof. Dr. Nengcheng Chen
Dr. Runze Li
Prof. Dr. Orhan Altan
Guest Editors

Manuscript Submission Information

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