Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data

Çömert R., Avdan U., Görüm T., Nefeslioglu H. A.

ENGINEERING GEOLOGY, vol.260, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 260
  • Publication Date: 2019
  • Doi Number: 10.1016/j.enggeo.2019.105264
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Object-based image analysis, UAV, Landslide, Kurucasile (Bartin), Cayeli (Rite), LIDAR DATA, CLASSIFICATION, PHOTOGRAPHS, INVENTORY
  • Istanbul Technical University Affiliated: Yes


The Black Sea Region of Turkey is one of the most landslide prone areas due to its high slope topography, heavy rainfall, and highly weathered hillslope material conditions. Preparation of landslide inventory maps is the first step in producing landslide susceptibility maps. Ground-based methods for mapping landslide occurrences are time-consuming and expensive. Additionally, landslide mapping based on satellite imageries and aerial photographs has some limitations, including climatic conditions, cost, and limited repetitive measurement capacity. Visual interpretation-based landslide mapping, which is based on satellite imageries and aerial photographs, is a time-consuming procedure that requires an experience-based expert opinion. Therefore, the data acquisition based on unmanned aerial vehicle (UAV) and landslide event inventory maps using an object-based classification approach can be superior to other methods in terms of speed and cost. In this study, we developed a semiautomatic model using object-based image analyses for rapid mapping of shallow landslides from the data obtained from UAVs after major landslide events in the Black Sea Region of Turkey. For this purpose, two test sites-Kurucasile (Bartin) and Cayeli (Rize)-were selected. Landslide mapping models were developed in the investigation sites, and the performance of the models was evaluated. The landslides' data obtained with the developed models were compared to the landslides' data produced by the experts. The comparison process revealed that landslides mapped by using UAV data have an accuracy rate higher than 86% according to the number of landslides and 83% according to the landslide area.