Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Pedestrian network extraction from fused aerial imagery (orthoimages) and laser imagery (Lidar)

Kasemsuppakorn, P and Karimi, HA (2013) Pedestrian network extraction from fused aerial imagery (orthoimages) and laser imagery (Lidar). Photogrammetric Engineering and Remote Sensing, 79 (4). 369 - 379. ISSN 0099-1112

[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


A pedestrian network is a topological map that contains the geometric relationship between pedestrian path segments (e.g., sidewalk, crosswalk, footpath), which is needed in a variety of applications, such as pedestrian navigation services. However, current pedestrian networks are not widely available. In an effort to provide an automatic means for creating pedestrian networks, this paper presents a methodology for extracting pedestrian network from aerial and laser images. The methodology consists of data preparation and four steps: object filtering, pedestrian path region extraction, pedestrian network construction, and raster to vector conversion. An experiment, using ten images, was conducted to evaluate the performance of the methodology. Evaluation results indicate that the methodology can extract sidewalk, crosswalk, footpath, and building entrances; it collects pedestrian networks with 61 percent geometrical completeness, 67.35 percent geometrical correctness, 71 percent topological completeness and 51.38 percent topological correctness. © 2013 American Society for Photogrammetry and Remote Sensing.


Social Networking:
Share |


Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Kasemsuppakorn, P
Karimi, HAhkarimi@pitt.eduHKARIMI0000-0001-5331-5004
Date: 1 January 2013
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Photogrammetric Engineering and Remote Sensing
Volume: 79
Number: 4
Page Range: 369 - 379
DOI or Unique Handle: 10.14358/pers.79.4.369
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 0099-1112
Related URLs:
Date Deposited: 25 Jun 2013 16:39
Last Modified: 31 Jul 2020 14:55


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item