Deep Learning Approach for Rock Outcrops Identification

Coco Y.T. Kwok, Man Sing Wong, Hung Chak Ho, Frankie L.C. Lo, Florence W.Y. Ko

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The steep natural terrain in Hong Kong is susceptible to shallow, small to medium-sized landslides induced by rainfall. These landslides usually involve failures occurring within the top one to two meters of the surface mantle. Terrain features manifested on the ground surface, such as historical landslides, rock outcrops, tension cracks, depressions could affect the terrain's susceptibility to landsliding in future. Identification of these features by conventional means involves substantial resources for Aerial Photo Interpretation (API) and field mapping. A pilot study was carried out to explore the potential of using deep learning to enhance the efficiency in identifying rock outcrops at a territory-wide scale with a view to improving the landslide susceptibility analysis. A methodology of combining Convolutional Neural Network and remote sensing techniques has been developed to derive a very high-resolution map of rock outcrops exposed on the natural terrain throughout the entire Hong Kong territory. The developed algorithm considers the spatial relationship and texture of the surrounding pixels as well as the spectral signature of the pixel of remote sensing imageries. The identification of rock outcrops has been conducted using the orthophotos acquired in years 2012 and 2015, with the aid of SPOT satellite images acquired in year 2015 and airborne LiDAR data acquired in year 2010, and resulting in a five-meter spatial resolution rock outcrops map. The results were promising when validated with the results mapped by engineering geologists using API on the same sets of orthophotos. The developed algorithm provides an alternative for leveraging the balance between spectral and spatial resolution, and for mapping the natural surface features and enhancing the landslide susceptibility analysis.

Original languageEnglish
Title of host publication5th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2018 - Proceedings
EditorsQihao Weng, Paolo Gamba, Ni-Bin Chang, Guangxing Wang, Wanqiang Yao
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538666425
DOIs
Publication statusPublished - 31 Dec 2018
Event5th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2018 - Xi'an, China
Duration: 18 Jun 201820 Jun 2018

Publication series

Name5th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2018 - Proceedings

Conference

Conference5th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2018
CountryChina
CityXi'an
Period18/06/1820/06/18

Keywords

  • Aerial Images
  • Convolutional Neural Network
  • Deep Learning
  • Land Cover Mapping
  • Rock Outcrops

ASJC Scopus subject areas

  • Instrumentation
  • Signal Processing
  • Atmospheric Science
  • Earth-Surface Processes

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