The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-4
19 Sep 2018
 | 19 Sep 2018


Z. Kugler, G. Szabó, H. M. Abdulmuttalib, C. Batini, H. Shen, A. Barsi, and G. Huang

Keywords: data quality, data dimensions, quality metrics, time, big data, crowd source

Abstract. Our rapidly changing world requires new sources of image based information. The quickly changing urban areas, the maintenance and management of smart cities cannot only rely on traditional techniques based on remotely sensed data, but also new and progressive techniques must be involved. Among these technologies the volunteer based solutions are getting higher importance, like crowd-sourced image evaluations, mapping by satellite based positioning techniques or even observations done by unskilled people. Location based intelligence has become an everyday practice of our life. It is quite enough to mention the weather forecast and traffic monitoring applications, where everybody can act as an observer and acquired data – despite their heterogeneity in quality – provide great value. Such value intuitively increases when data are of better quality. In the age of visualization, real-time imaging, big data and crowd-sourced spatial data have revolutionary transformed our general applications. Most important factors of location based decisions are the time-related quality parameters of the used data. In this paper several time-related data quality dimensions and terms are defined. The paper analyses the time sensitive data characteristics of image-based crowd-sourced big data, presents quality challenges and perspectives of the users. The data quality analyses focus not only on the dimensions, but are also extended to quality related elements, metrics. The paper discusses the connection of data acquisition and processing techniques, considering even the big data aspects. The paper contains not only theoretical sections, strong practice-oriented examples on detecting quality problems are also covered. Some illustrative examples are the OpenStreetMap (OSM), where the development of urbanization and the increasing process of involving volunteers can be studied. This framework is continuing the previous activities of the Remote Sensing Data Quality Working Group (ICWGIII/IVb) of the ISPRS in the topic focusing on the temporal variety of our urban environment.