Building landslide inventory with LiDAR data and deep learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F24%3A73628433" target="_blank" >RIV/61989592:15310/24:73628433 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/B978-0-12-823868-4.00014-3" target="_blank" >http://dx.doi.org/10.1016/B978-0-12-823868-4.00014-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/B978-0-12-823868-4.00014-3" target="_blank" >10.1016/B978-0-12-823868-4.00014-3</a>

Result language
angličtina
Original language name
Building landslide inventory with LiDAR data and deep learning
Original language description
The integration of data collection, cloud technologies, and artificial intelligence offers new opportunities to study Earth processes and landforms. These advances are bringing more comprehensive knowledge to the geosciences, enabling a detailed understanding of landforms and natural hazards from local to planetary scales. A shift from heuristic to data-based approaches is improving the objectivity of geoscientific analyses but is limited by data quality and availability, especially at regional scales. Landslides, influenced by geological and human factors, are an example of natural processes becoming disasters due to urbanization. Understanding past and present landslide conditions is crucial for future predictions, highlighting the importance of accurate landslide inventories. This study aims to train and evaluate deep learning models, specifically U-Net and DeepLabV3, for landslide detection and mapping in the Czech Republic using LiDAR data and landslide inventories. Results show that DeepLabV3 outperforms U-Net in accuracy, recall, and F1 score, suggesting its higher effectiveness in landslide detection. Challenges include data quality, resolution, and hillshade limitations, highlighting the need for high-quality input data for reliable AI-based geoscience applications.
Czech name
—
Czech description
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name
Earth Observation Applications to Landslide Mapping, Monitoring and Modeling
ISBN
978-0-12-823868-4
Number of pages of the result
12
Pages from-to
297-309
Number of pages of the book
311
Publisher name
Elsevier Ltd.
Place of publication
Netherlands
UT code for WoS chapter
—

Similar results(10)