Natural Hazard Mapping with Google Earth Engine

Dear Colleagues, 

In recent years, cloud computing infrastructures have contributed to the large diffusion of remote sensing data and applications in the scientific community.

Among the different cloud computing platforms, the Google Earth Engine (GEE) platform allows users to analyze both historical and recently acquired satellite imagery (e.g., Landsat 1-8, MODIS, Sentinel 1-5), as well as geospatial data set (e.g., reanalysis data from NCEP/NCAR). On the GEE platform, ready-to-use datasets are handled through JavaScript and Python libraries. Moreover, machine-learning techniques were also enabled by the recently added TensorFlow library.

In this Special Issue, we solicit studies using GEE to investigate and monitor natural hazards. In particular, manuscripts focusing on the following topics are welcome:

• innovative methods, techniques, and algorithms for the analysis of Earth observation datasets;
• new multi-temporal approaches toward satellite data analysis;
• investigations at a planetary scale;
• machine learning and artificial intelligence applications to multi-spectral, multi-temporal EO data;
• advanced APPs and tools aimed to monitor and map natural and environmental phenomena;
• advanced methods integrating GEE processing within more complex platforms;
• advanced APPs and GEE processing, supporting education in geosciences for scholars.

Dr. Nicola Genzano
Dr. Carolina Filizzola
Dr. Francesco Marchese
Prof. Dr. Valerio Tramutoli
Guest Editors

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