Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 447-453, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-447-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 447-453, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-447-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2017

12 Sep 2017

DATA QUALITY IN REMOTE SENSING

C. Batini1, T. Blaschke2, S. Lang2, F. Albrecht2, H. M. Abdulmutalib3, Á. Barsi4, G. Szabó4, and Zs. Kugler4 C. Batini et al.
  • 1University of Milano-Bicocca, Italy
  • 2Dept. of Geoinformatics, University of Salzburg, Austria
  • 3Dubai Municipality, Dubai, UAE
  • 4Department of Photogrammetry and Geoinformatics Budapest University of Technology and Economics, Budapest, Hungary

Keywords: Data dimensions, Data quality dimensions, big Earth data

Abstract. The issue of data quality (DQ) is of growing importance in Remote Sensing (RS), due to the widespread use of digital services (incl. apps) that exploit remote sensing data. In this position paper a body of experts from the ISPRS Intercommission working group III/IVb “DQ” identifies, categorises and reasons about issues that are considered as crucial for a RS research and application agenda. This ISPRS initiative ensures to build on earlier work by other organisations such as IEEE, CEOS or GEO, in particular on the meritorious work of the Quality Assurance Framework for Earth Observation (QA4EO) which was established and endorsed by the Committee on Earth Observation Satellites (CEOS) but aims to broaden the view by including experts from computer science and particularly database science. The main activities and outcomes include: providing a taxonomy of DQ dimensions in the RS domain, achieving a global approach to DQ for heterogeneous-format RS data sets, investigate DQ dimensions in use, conceive a methodology for managing cost effective solutions on DQ in RS initiatives, and to address future challenges on RS DQ dimensions arising in the new era of the big Earth data.