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Multitemporal UAV surveys for landslide mapping and characterization
Open accessAbstract
This paper presents the preliminary results of the IPL project 196 “Development and applications of a multi-sensor drone for geohazards monitoring and mapping.” The objective of the project is to test the applicability of a multi-sensor drone for the mapping and monitoring of different types of geohazards. The Department of Earth Sciences of the University of Florence has developed a new type of drone airframe. Several survey campaigns were performed in the village of Ricasoli, in the Upper Arno river Valley (Tuscany, Italy) with the drone equipped with an optical camera to understand the possibility of this rising technology to map and characterize landslides. The aerial images were combined and analyzed using Structure-from-Motion (SfM) software. The collected data allowed an accurate reconstruction and mapping of the detected landslides. Comparative analysis of the obtained DTMs also permitted the detection of some slope portions being prone to failure and to evaluate the area and volume of the involved mass.
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Cited by
Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records
Clasing, R., Muñoz, E., Arumí, J. L., & Parra, V. (2023) · Water · doi:10.3390/w15213813 · Cited by 16
Metadata
- Authors
- Rossi, G., Tanteri, L., Tofani, V., Vannocci, P., Moretti, S., & Casagli, N.
- Venue
- Landslides
- Published
- 2018-03-28
- Volume
- 15
- Issue
- 5
- Pages
- 1045-1052
- ISSN
- 1612-510X (ISSN-L)
- 1612-5118
- OpenAlex
- W2794658732
- Cited by
- 263
- References
- 0
- FWCI
- 50.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- CoChat. (2026). Citation check: Multitemporal UAV surveys for landslide mapping and characterization. https://app.cochat.ai/papers/10.1007/s10346-018-0978-0
- The paper
- Rossi, G., Tanteri, L., Tofani, V., Vannocci, P., Moretti, S., & Casagli, N. (2018). Multitemporal UAV surveys for landslide mapping and characterization. Landslides. https://doi.org/10.1007/s10346-018-0978-0
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
