Incorporating open source data for Bayesian classification of urban land use from VHR stereo images

Mengmeng Li*, Kirsten M. De Beurs, Alfred Stein, Wietske Bijker

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)


This study investigates the incorporation of open source data into a Bayesian classification of urban land use from very high resolution (VHR) stereo satellite images. The adopted classification framework starts from urban land cover classification, proceeds to building-type characterization, and results in urban land use. For urban land cover classification, a preliminary classification distinguishes trees, grass, and shadow objects using a random forest at a fine segmentation level. Fuzzy decision trees derived from hierarchical Bayesian models separate buildings from other man-made objects at a coarse segmentation level, where an open street map provides prior building information. A Bayesian network classifier combining commonly used land use indicators and spatial arrangement is used for the urban land use classification. The experiments were conducted on GeoEye stereo images over Oklahoma City, USA. Experimental results showed that the urban land use classification using VHR stereo images performed better than that using a monoscopic VHR image, and the integration of open source data improved the final urban land use classification. Our results also show a way of transferring the adopted urban land use classification framework, developed for a specific urban area in China, to other urban areas. The study concludes that incorporating open source data by Bayesian analysis improves urban land use classification. Moreover, a pretrained convolutional neural network fine tuned on the UC Merced land use dataset offers a useful tool to extract additional information for urban land use classification.

Original languageEnglish
Article number8016329
Pages (from-to)4930-4943
Number of pages14
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number11
Publication statusPublished - Nov 2017
Externally publishedYes


  • Bayesian methods
  • convolutional neural networks (CNN)
  • fuzzy decision trees
  • open source data
  • urban land use
  • very high resolution (VHR) stereo images


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