Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery

Abstract

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities.

Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models

“…Finding the extent of an existing landslide is difficult using this approach, as a landslide is better represented by a heterogeneous polygon (i.e., a collection of pixels). Detection of landslides activity using image correlation [55,56] and change detection [39,57] are also included in pixel-based methods, but they require a time-series of multi-temporal images.…”

“…Anantrasirichai et al [49] were able to use CNN for automatic detection of volcanic ground deformation from Sentinel-1 images. In another study, Chen et al [39] have used CNN to identify areas which have changed in a stack of bi-temporal images, and subsequently used spatio-temporary context analysis to identify landslides. Ghorbanzadeh et al [29] compared different machine learning methods along with CNN for landslide detection in the higher Himalayas.…”

Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application

“…Therefore, the conclusion made here could be biased. Other ways of expanding the current landslide catalogue can depend on automatic landslide detection methods based on remote sensing images (Nichol and Wong, 2005;Chen et al, 2018), internet new sources (as all landslides with a relevant impact on society will be reported on internet new sources), and automatic web data mining methods (Battistini et al, 2013;Goswami et al, 2018).…”

“…One emerging area relies on modelling. Some studies have used modelled soil moisture data for landslide applications (Ponziani et al, 2012;Ciabatta et al, 2016;Zhao et al, 2019a, b). However, to our knowledge, there is a lack of existing studies using modelled soil moisture from state-of-theart land surface models (LSMs) for landslide studies, such as the Noah LSM (Ek et al, 2003) and the Community Land Model (CLM) (Oleson et al, 2010).…”

Landslide Extraction From Sentinel-2 Image in Siwalik of Surkhet District, Nepal

“…The method used in this study is Object-Based Image Analysis (OBIA) (Hay, Castilla, 2006) which was developed in order to do the automatic extraction of image features. OBIA had been used to extract regular features like buildings (Karna, Bhardwaj, 2014), irregular features like tree canopy (Gustafson et al, 2018) or landslides (Chen et al, 2018), and also in landuse land cover classification (Cai et al, 2019).…”