BUILDING INTER-DISCIPLINARY COMPETENCE IN IMAGE ANALYSIS AND SPATIAL STATISTICS THROUGH COLLABORATIVE ONLINE LEARNING AND REMOTE FIELD TRIPS

R. Williams1, A. Purser2, S. Lund3

1University of Edinburgh (UNITED KINGDOM)
2Jacobs University (GERMANY)
3Kingston University (UNITED KINGDOM)
The use of using robotic vehicles to explore the extreme environments of Earth and the solar system has greatly increased over the last 5 years. Where once extreme environments were inaccessible, Remote Operated Vehicles (ROVs) and Mars Rovers now collect large image data sets. In addition to image collection techniques, the fields of computer processing and data transfer are also developing rapidly. Computer techniques now allow for much greater statistical investigation of features from images, and for data sets to be remotely accessible via the internet from international institutions simultaneously.

Aimed at new PhD students, masters or undergraduates, An Introduction to image analysis and spatial statistics’ takes an experimental approach to explore how researchers can best manage, analyse and disseminate these emerging data sets. In this rolling, 10 week, remote learning course we introduce inter-collegiate students from Earth and Planetary science disciplines to three statistical packages of use in image analysis:

‘ImageJ’ - A versatile program for quantifying point counts and areas of coverage within images.

‘PaSSAGE 2’ - A program for the analysis of transect data, including co- variance analysis.

SPSS - We introduce population analysis with the ANOVA facilities offered by SPSS, with comparable freely available packages also introduced.

In all cases, examples are given from both Earth and Planetary science disciplines for use in the work. The course is freely accessible to anyone in a partner institute of the DFG Heimholz ROBEX Alliance for robotic exploration of extreme environments. The course resources will additionally be available via download to any other interested users, though without access to the tutor/student component of the course.

Drawing on the experiential learning approach, we introduce image analysis, experimenting with applying spatial statistics in a safe environment, before students begin to utilise their learning and explore wider applications. Housed in Blackboard, collaborative learning is promoted through a-synchronous discussion, blogs and wiki building, with a unique ‘field trip’, where work groups dial in to gather real-time data from a deep sea Remote Operated Vehicle (ROV). Synchronous Skype tutorials and a-synchronous quizzes scaffold and continually assess learning from key reading materials. Short blocks of methods work guide the (predominately non-native English) participants through applicability and style in write-up requirements, so they can present statistics clearly and engagingly within journal articles. Within the10 week course, we aim to give participants confidence in these statistical toolsets and the aptitude to employ them in their own research work.