Impervious Surfaces from High Resolution Aerial Imagery: Cities in Fresno County [Research Brief]

Details

• Creators:
  Ahn, Yushin ; Poythress, Richard
• Corporate Creators:
  San Jose State University. College of Business. Mineta Transportation Institute
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Moisture barriers
  • Image processing
  • Machine learning
  • Mapping
  • Impervious surfaces
  • Image classification
  • High resolution Aerial imagery
• Resource Type:
  Brief
• Geographical Coverage:
  California ; United States
• Edition:
  Research Brief
• Corporate Publisher:
  Mineta Transportation Institute
• Abstract:
  This research explores the use of various image classification methods on high-resolution aerial imagery to quantify the percentage of urban areas covered by impervious surfaces. Traditional pixel- and object-based classification methods are compared to novel deep learning classification tools.
• Format:
  PDF
• Funding:
  2257
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/74831/dot_74831_DS1.pdf
• File Type:
  [PDF - 1.07 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:3a35c48a36b41bb50674d04389a99f0f489ae1686f9cd38a187ef93561d3dac204f58a46279ad07ccfab6707718a942267aca44c59f476ccf08a95aa07fa3096