Abstract

This paper exploits the use of a popular deep learning model - the faster-RCNN - to support automatic terrain feature detection and classification using a mixed set of optimal remote sensing and natural images. Crater detection is used as the case study in this research since this geomorphological feature provides important information about surface aging. Craters, such as impact craters, also effect global changes in many aspects, such as geography, topography, mineral and hydrocarbon production, etc. The collected data were labeled and the network was trained through a GPU server. Experimental results show that the faster-RCNN model coupled with a widely used convolutional network ZF-net performs well in detecting craters on the terrestrial surface.

Original languageEnglish (US)
Title of host publicationProceedings of the 1st Workshop on GeoAI
Subtitle of host publicationAI and Deep Learning for Geographic Knowledge Discovery, GeoAI 2017
PublisherAssociation for Computing Machinery, Inc
Pages33-36
Number of pages4
ISBN (Electronic)9781450354981
DOIs
StatePublished - Nov 7 2017
Event1st Workshop on GeoAI: AI and Deep Learning for Geographic Knowledge Discovery, GeoAI 2017 - Los Angeles, United States
Duration: Nov 7 2017Nov 10 2017

Publication series

NameProceedings of the 1st Workshop on GeoAI: AI and Deep Learning for Geographic Knowledge Discovery, GeoAI 2017

Other

Other1st Workshop on GeoAI: AI and Deep Learning for Geographic Knowledge Discovery, GeoAI 2017
CountryUnited States
CityLos Angeles
Period11/7/1711/10/17

Keywords

  • Crater
  • Deep learning
  • Region proposal network
  • Terrain feature recognition

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Artificial Intelligence

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