Work

Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review

Year: 2024

Type: review

Abstract: This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution and research publication trends, (3) progress of remote sensing and learning algorithms, and (4) application of remote sensing techniques and learning models for landslide susceptibility mapping, detections, prediction, inventory and deformation monitoring, assessment, and extraction and management. The literature selections were based on keyword searches using title/abstract and keywords from Web of Science and Scopus. A total of 186 research articles published between 2011 and 2024 were critically reviewed to provide answers to research questions related to the recent advances in the use of remote sensing technologies combined with artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms. The review revealed that these methods have high efficiency in landslide detection, prediction, monitoring, and hazard mapping. A few current issues were also identified and discussed.

Source: Remote Sensing

DOI: 10.3390/rs16162947

Authors: Stephen Akosah, Ivan Gratchev, Dong-Hyun Kim, Syng-Yup Ohn

Institutions: Griffith University, Korea Aerospace University

Language: English

Topic: Landslides and related hazards

Subfield: Management, Monitoring, Policy and Law

Field: Environmental Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,069

Funder: Griffith University