Predicting landslides using contour aligning convolutional neural networks
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Hajimoradlou, Ainaz
issued: 2019-11-19T16:39:10Z
issued: 2019
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Landslides are movement of soil and rock under the influence of gravity. They are common phenomena that cause significant human and economic losses every year. To reduce the impact of landslides, experts have developed tools to identify areas that are more likely to generate landslides. We propose a novel statistical approach for predicting landslides using deep convolutional networks. Using a standardized dataset of georeferenced images consisting of slope, elevation, land cover, lithology, rock age, and rock family as inputs, we deliver a landslide susceptibility map as output. We call our model a Locally Aligned Convolutional Neural Network, LACNN, as it follows the ground surface at multiple scales to predict possible landslide occurrence for a single point. To validate our method, we compare it to several baselines, including linear regression, a neural network, and a convolutional network, using log-likelihood error and Receiver Operating Characteristic curves on the test set. We show that our model performs better than the other proposed baselines, suggesting that such deep convolutional models are effective in heterogenous datasets for improving landslide susceptibility maps, which has the potential to reduce the human and economic cost of these events.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/72317
note: Predicting Landslides Using Contour Aligning ConvolutionalNeural NetworksbyAinaz HajimoradlouB.Sc., Sharif University of Technology, 2017a thesis submitted in partial fulfillmentof the requirements for the degree ofMaster of Scienceinthe faculty of graduate and postdoctoral studies(Computer Science)The University of British Columbia(Vancouver)November 2019© Ainaz Hajimoradlou, 2019The following individuals certify that they have read, and recommend to the Faculty of Grad-uate and Postdoctoral Studies for acceptance, the thesis entitled:Predicting Landslides Using Contour Aligning Convolutional Neural Net-workssubmitted by Ainaz Hajimoradlou in partial fulfillment of the requirements for the degree ofMaster of Science in Computer Science.Examining Committee:David Poole, Computer ScienceSupervisorJim Little, Computer ScienceAdditional ExamineriiAbstractLandslides are movement of soil and rock under the influence of gravity. They are commonphenomena that cause significant human and economic losses every year. To reduce the impactof landslides, experts have developed tools to identify areas that are more likely to generatelandslides. We propose a novel statistical approach for predicting landslides using deep con-volutional networks. Using a standardized dataset of georeferenced images consisting of slope,elevation, land cover, lithology, rock age, and rock family as inputs, we deliver a landslide sus-ceptibility map as output. We call our model a Locally Aligned Convolutional Neural Network,LACNN, as it follows the ground surface at multiple scales to predict possible landslide occur-rence for a single point. To validate our method, we compare it to several baselines, includinglinear regression, a neural network, and a convolutional network, using log-likelihood error andReceiver Operating Characteristic curves on the test set. We show that our model performsbetter than the other proposed baselines, suggesting that such deep convolutional models areeffective in heterog…
has type: Thesis/Dissertation
date issued: 2020-05
is shown at: 10.14288/1.0385548
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/72317