The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 125–131, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-125-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 125–131, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-125-2021

  28 Jun 2021

28 Jun 2021

OPEN URBAN AND FOREST DATASETS FROM A HIGH-PERFORMANCE MOBILE MAPPING BACKPACK – A CONTRIBUTION FOR ADVANCING THE CREATION OF DIGITAL CITY TWINS

S. Blaser, J. Meyer, and S. Nebiker S. Blaser et al.
  • Institute of Geomatics, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland

Keywords: Mobile Mapping, Urban, Forest, Open Dataset, Localization, Georeferencing, SfM, SLAM, Smart City, Digital Twin

Abstract. With this contribution, we describe and publish two high-quality street-level datasets, captured with a portable high-performance Mobile Mapping System (MMS). The datasets will be freely available for scientific use. Both datasets, from a city centre and a forest represent area-wide street-level reality captures which can be used e.g. for establishing cloud-based frameworks for infrastructure management as well as for smart city and forestry applications. The quality of these data sets has been thoroughly evaluated and demonstrated. For example, georeferencing accuracies in the centimetre range using these datasets in combination with image-based georeferencing have been achieved. Both high-quality multi sensor system street-level datasets are suitable for evaluating and improving methods for multiple tasks related to high-precision 3D reality capture and the creation of digital twins. Potential applications range from localization and georeferencing, dense image matching and 3D reconstruction to combined methods such as simultaneous localization and mapping and structure-from-motion as well as classification and scene interpretation. Our dataset is available online at: https://www.fhnw.ch/habg/bimage-datasets