Artificial Intelligence 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 2023, about 199 geohazards occurred, claiming the life of more than 65000 people and affecting almost 38 million people in total. The detection and mapping of geological hazards are paramount activities for land management and risk reduction policies around the world. Remote sensing technologies can be of benefit due to a high spatial and temporal coverage, allowing relevant information centered around the investigation, characterization, monitoring, and modeling of geohazards to be obtained. Alongside remote sensing, artificial intelligence and machine learning represent a significant innovation for the analysis of geohazards. This kind of approaches has 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 being increasingly implemented on remotely sensed data, providing support to the processing of datasets; the classification of imagery; the modeling of hazards, susceptibilities, or risks; the analysis of time series; and the rapid implementation of big data. This Remote Sensing Special Issue invites papers that apply machine learning techniques to remotely sensed data to address challenges around geohazards. This includes topics such as:

• The application of remotely sensed data to physically and statistically based hazard and risk models;
• The processing of remote sensing data with machine learning algorithms;
• The machine learning classification of remote sensing data;
• The processing of RS time series;
• Machine learning for the mapping and/or monitoring of geohazards;
• Landslide or subsidence analysis.

Dr. Pierluigi Confuorto
Dr. Silvia Bianchini
Dr. Chiara Martinello
Dr. Davide Festa
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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• modeling
• monitoring
• landslides
• subsidence
• geohazard
• susceptibility
• risk analysis
• GIS
• machine learning

• Machine Learning and Remote Sensing for Geohazards in Remote Sensing (10 articles)