Multi-Dimensionality of Uncertainty in big Geospatial Sensor Data

Abstract

Developments in sensor technology have contributed immensely to the growth of big geospatial sensor data. Moreover, advances in telecommunications have made it possible to use in-situ sensors to capture and transfer data about the environment in near real-time. The combination of various sensor types within a predefined geographic space and the possibility of making measurements at high temporal resolution contributes to a better understanding of our environment while also generating big geospatial sensor data. Big data from multi-sensor networks, particularly those that capture dynamic characteristics of the environment, have not been spared the challenges that face other types of big data. Specifically, the quality of data and the propagation of uncertainty through the multi-sensor data processing workflows have remained a major concern in the big geospatial sensor data research community. Attempts to document, quantify and communicate the uncertainty associated with sensor data and related sensor network outputs have been made mainly in the context of individual projects. This paper aims to document the state-of-art in defining uncertainty with regard to multi-sensor geospatial data. In particular, we analyse the current literature to outline different types of uncertainty, and document methods for handling uncertainty in the different stages of multi-sensor geospatial data collection, processing and delivery.