Landslide Susceptibility Mapping by Fusing Convolutional Neural Networks and Vision Transformer

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00130300" target="_blank" >RIV/00216224:14310/23:00130300 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/23/1/88" target="_blank" >https://www.mdpi.com/1424-8220/23/1/88</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s23010088" target="_blank" >10.3390/s23010088</a>

Result language

angličtina

Original language name

Landslide Susceptibility Mapping by Fusing Convolutional Neural Networks and Vision Transformer

Original language description

Landslide susceptibility mapping (LSM) is an important decision basis for regional landslide hazard risk management, territorial spatial planning and landslide decision making. The current convolutional neural network (CNN)-based landslide susceptibility mapping models do not adequately take into account the spatial nature of texture features, and vision transformer (ViT)-based LSM models have high requirements for the amount of training data. In this study, we overcome the shortcomings of CNN and ViT by fusing these two deep learning models (bottleneck transformer network (BoTNet) and convolutional vision transformer network (ConViT)), and the fused model was used to predict the probability of landslide occurrence. First, we integrated historical landslide data and landslide evaluation factors and analysed whether there was covariance in the landslide evaluation factors. Then, the testing accuracy and generalisation ability of the CNN, ViT, BoTNet and ConViT models were compared and analysed. Finally, four landslide susceptibility mapping models were used to predict the probability of landslide occurrence in Pingwu County, Sichuan Province, China. Among them, BoTNet and ConViT had the highest accuracy, both at 87.78%, an improvement of 1.11% compared to a single model, while ConViT had the highest F1-socre at 87.64%, an improvement of 1.28% compared to a single model. The results indicate that the fusion model of CNN and ViT has better LSM performance than the single model. Meanwhile, the evaluation results of this study can be used as one of the basic tools for landslide hazard risk quantification and disaster prevention in Pingwu County.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10500 - Earth and related environmental sciences

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Volume of the periodical

23

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

1-19

UT code for WoS article

000909151800001

EID of the result in the Scopus database

2-s2.0-85145875859