EGU General Assembly 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Mapping landslides using drone's full-motion videos

Ionut Cosmin Sandric1,2, Viorel Ilinca2,3, Radu Irimia1,2, Zenaida Chitu2, Marta Jurchescu4, and Alin Plesoianu5
Ionut Cosmin Sandric et al.
  • 1University of Bucharest, Faculty of Geography, Bucharest, Romania
  • 2Research Institute of the University of Bucharest, Bucharest, Romania
  • 3Geological Institute of Romania, Romania
  • 4Institute of Geography, Romanian Academy, Romania
  • 5Esri Romania

Rapid mapping of landslides plays an important role in both science and emergency management communities. It helps people to take the appropriate decisions in quasi-real-time and to diminish losses. With the increasing advancement in high-resolution satellite and aerial imagery, this task also increased the spatial accuracy, providing more and more accurate maps of landslide locations. In accordance with the latest developments in the fields of unmanned aerial vehicles and artificial intelligence, the current study is focused on providing an insight into the process of mapping landslides from full-motion videos and by means of artificial intelligence. To achieve this goal, several drone flights were performed over areas located in the Romanian Subcarpathians, using Quadro-Copters (DJI Phantom 4 and DJI Mavic 2 Enterprise) equipped with a 12 MP RGB camera. The flights were planned and executed to reach an optimal number of pictures and videos, taken from various angles and heights over the study areas. Using Structure from Motion techniques, each dataset was processed and orthorectified. Similarly, each video was processed and transformed into a full-motion video, having coordinates allocated to each frame. Samples of specific landslide features were collected by hand, using the pictures and the video frames, and used to create a complete database necessary to train a Mask RCNN model. The samples were divided into two different datasets, having 80% of them used for the training process and the rest of 20% for the validation process. The model was trained over 50 epochs and it reached an accuracy of approximately 86% on the training dataset and about 82% on the validation dataset. The study is part of an ongoing project, SlideMap 416PED, financed by UEFISCDI, Romania. More details about the project can be found at

How to cite: Sandric, I. C., Ilinca, V., Irimia, R., Chitu, Z., Jurchescu, M., and Plesoianu, A.: Mapping landslides using drone's full-motion videos, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11179,, 2021.

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