The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 457–464, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-457-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 457–464, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-457-2021

  28 Jun 2021

28 Jun 2021

AUTOMATIC CHANGE DETECTION OF DIGITAL MAPS USING AERIAL IMAGES AND POINT CLOUDS

F. Dahle1, K. Arroyo Ohori2, G. Agugiaro2, and S. Briels3 F. Dahle et al.
  • 1Geoscience & Remote Sensing, Delft University of Technology, the Netherlands
  • 23D geoinformation, Delft University of Technology, the Netherlands
  • 3Readar, Utrecht, the Netherlands

Keywords: Change detection, Gradient boosting, XGBoost, Classification, Machine Learning

Abstract. 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.