Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks

Rasha Alshehhi, Prashanth Reddy Marpu, Wei Lee Woon, Mauro Dalla Mura

Research output: Contribution to journalArticle


Extraction of man-made objects (e.g., roads and buildings) from remotely sensed imagery plays an important role in many urban applications (e.g., urban land use and land cover assessment, updating geographical databases, change detection, etc). This task is normally difficult due to complex data in the form of heterogeneous appearance with large intra-class and lower inter-class variations. In this work, we propose a single patch-based Convolutional Neural Network (CNN) architecture for extraction of roads and buildings from high-resolution remote sensing data. Low-level features of roads and buildings (e.g., asymmetry and compactness) of adjacent regions are integrated with Convolutional Neural Network (CNN) features during the post-processing stage to improve the performance. Experiments are conducted on two challenging datasets of high-resolution images to demonstrate the performance of the proposed network architecture and the results are compared with other patch-based network architectures. The results demonstrate the validity and superior performance of the proposed network architecture for extracting roads and buildings in urban areas.

Original languageEnglish (US)
Pages (from-to)139-149
Number of pages11
JournalISPRS Journal of Photogrammetry and Remote Sensing
StatePublished - Aug 1 2017



  • Adjacent regions
  • Convolutional neural network
  • Extraction
  • Low-level features

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences

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