Machine Learning and Remote Sensing for Geohazards

Dear Colleagues,

Geohazards, or geological hazards, can be defined as “events caused by geological, geomorphological, and climatic conditions or processes that represent serious threats to human lives, property, and the natural and built environment”. According to the Emergency Events Database (https://public.emdat.be/), in 2021, about 250 geohazards occurred, claiming the lives of more than 150 people and affecting almost 20 million people in total. The detection and mapping of geological hazards are paramount activities for land management and risk reduction policies worldwide. Remote sensing technologies can be helpful due to their high spatial and temporal coverage, allowing relevant information to be obtained worldwide for the investigation, characterization, monitoring and modeling of geohazards. Together with remote sensing, Artificial Intelligence or machine learning represents a significant innovation for the analysis of geohazards. Such kinds of approaches have widely demonstrated their suitability in many scientific fields, being characterized by high accuracy and specific advantages in different study areas, and for different sets of factors. Machine learning is increasingly implemented on remotely sensed data, providing support to the processing of datasets; for the classification of imagery; or for the modeling of hazards, susceptibility or risk. This Special Issue of Remote Sensing invites papers that apply machine learning techniques to remotely sensed data to address challenges around geohazards. Topics of interest include, but are not limited to, the following:

• Application of remotely sensed data to physical- and statistical-based hazard and risk models;
• Processing of remote sensing data with machine learning algorithms;
• Machine learning classification of remote sensing data;
• Processing of RS time-series;
• Machine learning for the mapping and/or monitoring of geohazards;
• Landslide or subsidence analysis.

Dr. Pierluigi Confuorto
Dr. Federico Raspini
Dr. Matteo Del Soldato
Dr. Chiara Cappadonia
Dr. Simon Plank
Dr. Mariano Di Napoli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.