Authors: Wenwen Li*, Arizona State University, Chia-Yu Hsu, Arizona State University
Topics: Geographic Information Science and Systems, Cyberinfrastructure
Keywords: deep learning
Session Type: Paper
Start / End Time: 10:00 AM / 11:40 AM
Room: Grand Ballroom A, Astor, 2nd Floor
Presentation File: No File Uploaded
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.