Christoph Kinkeldey

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Analysis of land cover change is one of the major challenges in the remote sensing and GIS domain, especially when multi-temporal or multi-sensor analyses are conducted. One of the reasons is that errors and inaccuracies from multiple datasets (for instance caused by sensor bias or spatial misregistration) accumulate and can lead to a high amount of(More)
Noise annotation lines are a promising technique to visualize thematic uncertainty in maps. However, their potential has not yet been evaluated in user studies. In two experiments we assessed the usability of this technique with respect to a different number of uncertainty levels as well as the influence of two design aspects of noise annotation lines: the(More)
We report findings from a web-based experiment on noise annotation lines, a method to represent attribute uncertainty. We tested and compared three design aspects of noise annotation lines and evaluated how different design variations influence user performance. We systematically varied the number of uncertainty categories, noise width, and noise grain. Our(More)
Extensive research on geodata uncertainty has been conducted in the past decades, mostly related to modeling, quantifying, and communicating uncertainty. But findings on if and how users can incorporate this information into spatial analyses are still rare. In this paper we address these questions with a focus on land cover change analysis. We conducted(More)
Classified remotely sensed data serves as the basis for various types of city models. Since the requirements concerning the correctness of these models are rapidly growing, the demands for a significant assurance of their quality increase as well. Standard methods for the a posteriori evaluation of classified data have successfully been applied but they do(More)
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