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Open AccessEditorial

Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management

1
Data Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain
2
Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 301; https://doi.org/10.3390/rs12020301
Received: 9 January 2020 / Accepted: 9 January 2020 / Published: 16 January 2020
This editorial summarizes the performance of the special issue entitled Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management, which was published at MDPI’s Remote Sensing journal. The special issue took place in years 2018 and 2019 and accepted a total of nine papers from authors of thirteen different countries. So far, these papers have dealt with 116 cites. Earthquakes, landslides, floods, wildfire and soil salinity were the topics analyzed. New methods were introduced, with applications of the utmost relevance.
Keywords: data mining; big data; remote sensing; GIS; spatio-temporal analysis; information fusion; natural hazards data mining; big data; remote sensing; GIS; spatio-temporal analysis; information fusion; natural hazards
MDPI and ACS Style

Martínez-Álvarez, F.; Bui, D.T. Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management. Remote Sens. 2020, 12, 301.

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