IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v119y2023i1d10.1007_s11069-023-06145-0.html
   My bibliography  Save this article

RIPF-Unet for regional landslides detection: a novel deep learning model boosted by reversed image pyramid features

Author

Listed:
  • Bangjie Fu

    (Central South University)

  • Yange Li

    (Central South University)

  • Zheng Han

    (Central South University
    Hunan Provincial Key Laboratory for Disaster Prevention and Mitigation of Rail Transit Engineering Structures)

  • Zhenxiong Fang

    (Central South University)

  • Ningsheng Chen

    (Chinese Academy of Sciences)

  • Guisheng Hu

    (Chinese Academy of Sciences)

  • Weidong Wang

    (Central South University)

Abstract

Rapid detection of landslides using remote sensing images plays a key role in hazard assessment and mitigation. Many deep convolutional neural network-based models have been proposed for this purpose; however, for small-scale landslide detection, excessive convolution and pooling process may cause potential texture information loss, which can lead to misclassification of landslide target. In this paper, we present a novel UNet model for the automatic detection of landslides, wherein the reversed image pyramid features (RIPFs) are adapted to mitigate the information loss caused by a succession of convolution and pooling. The proposed RIPF-Unet model is trained and validated using the open-source landslides dataset of the Bijie area, Guizhou Province, China, wherein the precision of the proposed model is observed to increase by 3.5% and 4.0%, compared to the conventional UNet and UNet + + model, respectively. The proposed RIPF-Unet model is further applied to the case of the Longtoushan region after the 2014 Ms.6.5 Ludian earthquake. Results show that the proposed model achieves a 96.63% accuracy for detecting landslides using remote sensing images. And the RIPF-Unet model is also advanced in its compact parameter size; notably, it is 31% lighter compared to the UNet + + model.

Suggested Citation

  • Bangjie Fu & Yange Li & Zheng Han & Zhenxiong Fang & Ningsheng Chen & Guisheng Hu & Weidong Wang, 2023. "RIPF-Unet for regional landslides detection: a novel deep learning model boosted by reversed image pyramid features," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 701-719, October.
  • Handle: RePEc:spr:nathaz:v:119:y:2023:i:1:d:10.1007_s11069-023-06145-0
    DOI: 10.1007/s11069-023-06145-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-06145-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-023-06145-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Viet-Ha Nhu & Ayub Mohammadi & Himan Shahabi & Baharin Bin Ahmad & Nadhir Al-Ansari & Ataollah Shirzadi & John J. Clague & Abolfazl Jaafari & Wei Chen & Hoang Nguyen, 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment," IJERPH, MDPI, vol. 17(14), pages 1-23, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abhik Saha & Vasanta Govind Kumar Villuri & Ashutosh Bhardwaj, 2022. "Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India," Land, MDPI, vol. 11(10), pages 1-27, October.
    2. Siti Norsakinah Selamat & Nuriah Abd Majid & Aizat Mohd Taib, 2023. "A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia," Sustainability, MDPI, vol. 15(1), pages 1-21, January.
    3. Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Farbod Farhangi & Soo-Mi Choi, 2021. "COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms," IJERPH, MDPI, vol. 18(18), pages 1-21, September.
    4. Sheela Bhuvanendran Bhagya & Anita Saji Sumi & Sankaran Balaji & Jean Homian Danumah & Romulus Costache & Ambujendran Rajaneesh & Ajayakumar Gokul & Chandini Padmanabhapanicker Chandrasenan & Renata P, 2023. "Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps," Land, MDPI, vol. 12(2), pages 1-29, February.
    5. Uzodigwe Emmanuel Nnanwuba & Shengwu Qin & Oluwafemi Adewole Adeyeye & Ndichie Chinemelu Cosmas & Jingyu Yao & Shuangshuang Qiao & Sun Jingbo & Ekene Mathew Egwuonwu, 2022. "Prediction of Spatial Likelihood of Shallow Landslide Using GIS-Based Machine Learning in Awgu, Southeast/Nigeria," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    6. Patricia Arrogante-Funes & Adrián G. Bruzón & Fátima Arrogante-Funes & Rocío N. Ramos-Bernal & René Vázquez-Jiménez, 2021. "Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment," IJERPH, MDPI, vol. 18(22), pages 1-21, November.
    7. Shuai Li & Zhongyun Ni & Yinbing Zhao & Wei Hu & Zhenrui Long & Haiyu Ma & Guoli Zhou & Yuhao Luo & Chuntao Geng, 2022. "Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake," IJERPH, MDPI, vol. 19(6), pages 1-30, March.
    8. Purna Bahadur Thapa & Saurav Lamichhane & Khagendra Prasad Joshi & Aayoush Raj Regmi & Divya Bhattarai & Hari Adhikari, 2023. "Landslide Susceptibility Assessment in Nepal’s Chure Region: A Geospatial Analysis," Land, MDPI, vol. 12(12), pages 1-20, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:119:y:2023:i:1:d:10.1007_s11069-023-06145-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.