Automatic change detection of digital maps using aerial images and point clouds

Research output: Contribution to journalConference articleScientificpeer-review

1 Citation (Scopus)
44 Downloads (Pure)


In many countries digital maps are generally created and provided by Cadastre, Land Registry or National Mapping Agencies. These maps must be accurate and well maintained. However, in most cases, the update process of these maps is still done by hand, often using satellite or aerial imagery. Supporting this process via automatic change detection based on traditional classification algorithms is difficult due to the high level of noise in the data, such as introduced by temporary changes (e.g. cars being parked). This paper describes a method to detect changes between two time steps using 2.5D data and to transfer these insights to a digital map. For every polygon in the map, several attributes are collected from the input data, which are used to train a machine-learning model based on gradient boosting. A case study in Haarlem, in the Netherlands, was conducted to test the performance of this proposed approach. Results show that this methodology can recognize a substantial amount of changes and can support - and speed up - the manual updating process.

Original languageEnglish
Pages (from-to)457-464
Number of pages8
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue numberB2-2021
Publication statusPublished - 2021
Event2021 24th ISPRS Congress Commission II: Imaging Today, Foreseeing Tomorrow - Virtual, Online, France
Duration: 5 Jul 20219 Jul 2021


  • Change detection
  • Classification
  • Gradient boosting
  • Machine Learning
  • XGBoost


Dive into the research topics of 'Automatic change detection of digital maps using aerial images and point clouds'. Together they form a unique fingerprint.

Cite this