Comparing experts and novices in Martian surface feature change detection and identification

Wardlaw, J., Sprinks, J. ORCID: 0000-0003-3781-5484, Houghton, R., Muller, J.-P., Sidiropoulos, P., Bamford, S. and Marsh, S., 2018. Comparing experts and novices in Martian surface feature change detection and identification. International Journal of Applied Earth Observation and Geoinformation, 64, pp. 354-364. ISSN 1569-8432

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Change detection in satellite images is a key concern of the Earth Observation field for environmental and climate change monitoring. Satellite images also provide important clues to both the past and present surface conditions of other planets, which cannot be validated on the ground. With the volume of satellite imagery continuing to grow, the inadequacy of computerised solutions to manage and process imagery to the required professional standard is of critical concern. Whilst studies find the crowd sourcing approach suitable for the counting of impact craters in single images, images of higher resolution contain a much wider range of features, and the performance of novices in identifying more complex features and detecting change, remains unknown. This paper presents a first step towards understanding whether novices can identify and annotate changes in different geomorphological features. A website was developed to enable visitors to flick between two images of the same location on Mars taken at different times and classify 1) if a surface feature changed and if so, 2) what feature had changed from a pre-defined list of six. Planetary scientists provided “expert” data against which classifications made by novices could be compared when the project subsequently went public. Whilst no significant difference was found in images identified with surface changes by expert and novices, results exhibited differences in consensus within and between experts and novices when asked to classify the type of change. Experts demonstrated higher levels of agreement in classification of changes as dust devil tracks, slope streaks and impact craters than other features, whilst the consensus of novices was consistent across feature types; furthermore, the level of consensus amongst regardless of feature type. These trends are secondary to the low levels of consensus found, regardless of feature type or classifier expertise. These findings demand the attention of researchers who want to use crowd-sourcing for similar scientific purposes, particularly for the supervised training of computer algorithms, and inform the scope and design of future projects.

Item Type: Journal article
Publication Title: International Journal of Applied Earth Observation and Geoinformation
Creators: Wardlaw, J., Sprinks, J., Houghton, R., Muller, J.-P., Sidiropoulos, P., Bamford, S. and Marsh, S.
Publisher: Elsevier B.V.
Date: February 2018
Volume: 64
ISSN: 1569-8432
S030324341730123XPublisher Item Identifier
Rights: © 2017 Elsevier B.V. All rights reserved.
Divisions: Schools > School of Architecture, Design and the Built Environment
Record created by: Jill Tomkinson
Date Added: 04 Jan 2018 14:20
Last Modified: 04 Feb 2020 11:09

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