The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 15–22, 2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 15–22, 2018

  30 Apr 2018

30 Apr 2018


F. Albrecht1, T. Blaschke1, S. Lang1, H. M. Abdulmutalib2, G. Szabó3, Á. Barsi3, C. Batini4, A. Bartsch5, Zs. Kugler3, D. Tiede1, and G. Huang6 F. Albrecht et al.
  • 1Dept. of Geoinformatics, University of Salzburg, Austria
  • 2Dubai Municipality, Dubai, UAE
  • 3Department of Photogrammetry and Geoinformatics Budapest University of Technology and Economics, Budapest, Hungary
  • 4University of Milano-Bicocca, Italy
  • 5Austrian Polar Research Institute, Austria
  • 6Chinese Academy of Surveying and Mapping, Beijing, China

Keywords: validation procedures, data quality dimensions, remote sensing lifecycle, quality criteria, disaster management, standardisation, ISO 19000

Abstract. The availability and accessibility of remote sensing (RS) data, cloud processing platforms and provided information products and services has increased the size and diversity of the RS user community. This development also generates a need for validation approaches to assess data quality. Validation approaches employ quality criteria in their assessment. Data Quality (DQ) dimensions as the basis for quality criteria have been deeply investigated in the database area and in the remote sensing domain. Several standards exist within the RS domain but a general classification – established for databases – has been adapted only recently. For an easier identification of research opportunities, a better understanding is required how quality criteria are employed in the RS lifecycle. Therefore, this research investigates how quality criteria support decisions that guide the RS lifecycle and how they relate to the measured DQ dimensions. Subsequently follows an overview of the relevant standards in the RS domain that is matched to the RS lifecycle. Conclusively, the required research needs are identified that would enable a complete understanding of the interrelationships between the RS lifecycle, the data sources and the DQ dimensions, an understanding that would be very valuable for designing validation approaches in RS.