TY - JOUR
T1 - Landslide mapping using two main deep-learning convolution neural network (CNN) streams combined by the Dempster-Shafer (DS) model
AU - Ghorbanzadeh, Omid
AU - Meena, S.R.
AU - Shahabi Sorman Abadi, Hejar
AU - Tavakoli Piralilou, Sepideh
AU - Zhiyong, Lv
AU - Blaschke, Thomas
PY - 2020/12/10
Y1 - 2020/12/10
N2 - Beyond the direct hazards of earthquakes, the deposited mass of earthquake-induced landslide (EQIL) in the riverbeds causing the river to thrust upward. The EQIL inventories are generated mostly by traditional or semi-supervised mapping approaches which required parameter's tuning or binary threshold decision in practical application. In this study, we investigated the impact of optical data from the PlanetScope sensor and topographic factors from the ALOS sensor on EQIL mapping using a deep-learning convolution neural network (CNN). Thus, six training datasets were prepared and used to evaluate the performance of the CNN model using only optical data and using this data along with each and all topographic factors across the west coast of the Trishuli River in Nepal. For the first time, the Dempster—Shafer (DS) model was applied for combining the resulting maps from each CNN stream that trained with different datasets. Finally, seven different resulting maps were compared against a detailed and accurate inventory of landslide polygons by a mean intersection-over-union (mIOU). Our results confirm that using the training dataset of the spectral information along with the topographic factor of the slope is helpful to distinguish the landslide bodies from other similar features such as barren lands and consequently increase the mapping accuracy. The improvement of the mIOU was a range from approximately zero to more than 17%. Moreover, the DS model can be considered as an optimizer method to combine the results from different scenarios.
AB - Beyond the direct hazards of earthquakes, the deposited mass of earthquake-induced landslide (EQIL) in the riverbeds causing the river to thrust upward. The EQIL inventories are generated mostly by traditional or semi-supervised mapping approaches which required parameter's tuning or binary threshold decision in practical application. In this study, we investigated the impact of optical data from the PlanetScope sensor and topographic factors from the ALOS sensor on EQIL mapping using a deep-learning convolution neural network (CNN). Thus, six training datasets were prepared and used to evaluate the performance of the CNN model using only optical data and using this data along with each and all topographic factors across the west coast of the Trishuli River in Nepal. For the first time, the Dempster—Shafer (DS) model was applied for combining the resulting maps from each CNN stream that trained with different datasets. Finally, seven different resulting maps were compared against a detailed and accurate inventory of landslide polygons by a mean intersection-over-union (mIOU). Our results confirm that using the training dataset of the spectral information along with the topographic factor of the slope is helpful to distinguish the landslide bodies from other similar features such as barren lands and consequently increase the mapping accuracy. The improvement of the mIOU was a range from approximately zero to more than 17%. Moreover, the DS model can be considered as an optimizer method to combine the results from different scenarios.
KW - Earthquake-induced landslide (EQIL)
KW - hydropower
KW - landslide-induced lakes
KW - topographical factors
KW - Trishuli River
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-GOLD
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2021/isi/jmeenalan.pdf
U2 - 10.1109/JSTARS.2020.3043836
DO - 10.1109/JSTARS.2020.3043836
M3 - Article
AN - SCOPUS:85097944124
VL - 14
SP - 452
EP - 463
JO - IEEE Journal of selected topics in applied earth observations and remote sensing
JF - IEEE Journal of selected topics in applied earth observations and remote sensing
SN - 1939-1404
ER -