Document Type : Research Paper
Authors
1 Shiraz University
2 Dep. of Watershed & Arid Zone Management Gorgan University of Agricultural Sciences & Natural Resources Gorgan, Iran
3 Széchenyi István University
Abstract
Introduction:
Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes, which are often triggered by intensive rainfall.
Materials and methods:
Recent advances in acquiring images from unmanned aerial vehicles (UAV) and deep learning (DL) methods inherited from computer vision have made it feasible to propose semi-automated soil landform detection methodologies for large areas at an unprecedented spatial resolution. In this study, we evaluate the potential of two cutting-edge DL segmentation models, the vanilla U-Net model, and the Attention Deep Supervision Multi-Scale U-Net model, applied to UAV-derived products, to map landslides and sinkholes in a semi-arid environment, the “Golestan Province” (north-east Iran)
Results and discussion:
Results show that our framework can successfully map landslides in a challenging environment (with an F1-score of 69 %), and that topographical derivates from UAV-derived DSM decrease the capacity of mapping sinkholes of the models calibrated with optical data.
Conclusions:
Since this kind of soil erosion is the main origin of some major soil erosion including gully initiation and extension, applying new technology namely, UAV and deep learning is highly important and recommended.
Keywords
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