Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models
出版年份 2020 全文链接
标题
Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models
作者
关键词
-
出版物
Remote Sensing
Volume 12, Issue 3, Pages 346
出版商
MDPI AG
发表日期
2020-01-22
DOI
10.3390/rs12030346
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
- (2019) Omid Ghorbanzadeh et al. Remote Sensing
- Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China
- (2019) Yi Wang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN
- (2019) Philipp Schuegraf et al. ISPRS International Journal of Geo-Information
- End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++
- (2019) Daifeng Peng et al. Remote Sensing
- Landslide mapping from multi-sensor data through improved change detection-based Markov random field
- (2019) Ping Lu et al. REMOTE SENSING OF ENVIRONMENT
- Review on landslide susceptibility mapping using support vector machines
- (2018) Yu Huang et al. CATENA
- A review of statistically-based landslide susceptibility models
- (2018) Paola Reichenbach et al. EARTH-SCIENCE REVIEWS
- The size, distribution, and mobility of landslides caused by the 2015 M w 7.8 Gorkha earthquake, Nepal
- (2018) Kevin Roback et al. GEOMORPHOLOGY
- A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images
- (2018) Ren N. Keyport et al. International Journal of Applied Earth Observation and Geoinformation
- How far are we from the use of satellite rainfall products in landslide forecasting?
- (2018) M.T. Brunetti et al. REMOTE SENSING OF ENVIRONMENT
- Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery
- (2018) et al. SENSORS
- Topography-driven satellite imagery analysis for landslide mapping
- (2018) M. Alvioli et al. Geomatics Natural Hazards & Risk
- Quantitative Assessment of Digital Image Correlation Methods to Detect and Monitor Surface Displacements of Large Slope Instabilities
- (2018) Valentin Bickel et al. Remote Sensing
- Application of Machine Learning to Classification of Volcanic Deformation in Routinely Generated InSAR Data
- (2018) N. Anantrasirichai et al. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
- A global slope unit-based method for the near real-time prediction of earthquake-induced landslides
- (2018) Hakan Tanyas et al. GEOMORPHOLOGY
- A multidisciplinary approach for the investigation of a rock spreading on an urban slope
- (2017) R. Tomás et al. Landslides
- Correlation of satellite image time-series for the detection and monitoring of slow-moving landslides
- (2017) André Stumpf et al. REMOTE SENSING OF ENVIRONMENT
- Performance-based, seismically-induced landslide hazard mapping of Western Oregon
- (2017) Mahyar Sharifi-Mood et al. SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
- Ten quick tips for machine learning in computational biology
- (2017) Davide Chicco BioData Mining
- Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
- (2017) Xiao Xiang Zhu et al. IEEE Geoscience and Remote Sensing Magazine
- Combined use of statistical and DInSAR data analyses to define the state of activity of slow-moving landslides
- (2016) Michele Calvello et al. Landslides
- Influencing factor analysis and displacement prediction in reservoir landslides − a case study of Three Gorges Reservoir (China)
- (2016) Tehnicki Vjesnik-Technical Gazette
- Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
- (2016) Liangpei Zhang et al. IEEE Geoscience and Remote Sensing Magazine
- Contour Connection Method for automated identification and classification of landslide deposits
- (2015) Ben A. Leshchinsky et al. COMPUTERS & GEOSCIENCES
- A critical review of rock slope failure mechanisms: The importance of structural geology
- (2015) Doug Stead et al. JOURNAL OF STRUCTURAL GEOLOGY
- Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method
- (2013) Vahid Moosavi et al. GEOMORPHOLOGY
- Geographic Object-Based Image Analysis – Towards a new paradigm
- (2013) Thomas Blaschke et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping
- (2013) Natan Micheletti et al. Mathematical Geosciences
- Deep seated gravitational slope deformations in the European Alps
- (2013) G.B. Crosta et al. TECTONOPHYSICS
- Landslide inventory maps: New tools for an old problem
- (2012) Fausto Guzzetti et al. EARTH-SCIENCE REVIEWS
- Landslide zoning over large areas from a sample inventory by means of scale-dependent terrain units
- (2012) Michele Calvello et al. GEOMORPHOLOGY
- Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis
- (2011) T. R. Martha et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images
- (2011) A.C. Mondini et al. REMOTE SENSING OF ENVIRONMENT
- Object-oriented mapping of landslides using Random Forests
- (2011) André Stumpf et al. REMOTE SENSING OF ENVIRONMENT
- Use of LIDAR in landslide investigations: a review
- (2010) Michel Jaboyedoff et al. NATURAL HAZARDS
- Learning from Imbalanced Data
- (2009) Haibo He et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview
- (2008) Cees J. van Westen et al. ENGINEERING GEOLOGY
- The classification, recording, databasing and use of information about building damage caused by subsidence and landslides
- (2008) A.H. Cooper QUARTERLY JOURNAL OF ENGINEERING GEOLOGY AND HYDROGEOLOGY
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