Time-Related Quality Dimensions of Urban Remotely Sensed Big Data

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 crowdsourced 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 datadespite their heterogeneity in qualityprovide great value. Such value intuitively increases when data are of better quality. In the age of visualization, real-time imaging, big data and crowdsourced 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.This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-4-315-2018 | © Authors 2018. CC BY 4.0 License.

Remote sensing data quality model: from data sources to lifecycle phases

“…Based on the prior papers of the working group (Batini et al 2017, Albrecht et al 2018, Barsi et al 2018, Kugler et al 2018 this paper aims at giving a brief overview of existing data quality schemes described by standards. Followed by the establishment of a quality model scheme for RS domain based on Batini's concept for information technology domain (Batini and Scannapieco 2016).…”