The important thing to remember when gathering evidence is that the more evidence the better - that is, the more evidence you gather to demonstrate your skills, the more confident an assessor can be that you have learned the skills not just at one point in time, but are continuing to apply and develop those skills (as opposed to just learning for the test!). Furthermore, one piece of evidence that you collect will not usualy demonstrate all the required criteria for a unit of competency, whereas multiple overlapping pieces of evidence will usually do the trick!
From the Wiki University
What evidence can you provide to prove your understanding of each of the following citeria?
Determine appropriate digital image processing techniques.
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Appropriate image, merger and modelling techniques are determined according to organisational requirements and project specifications. Completed |
Evidence:
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Appropriate data collection and analysis techniques in remote sensing process are determined according to project requirements. Completed |
Evidence:
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Suitable digital image processing techniques and digital image data formats are selected. Completed |
Evidence:
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Additional characteristics of image and metadata are included. Completed |
Evidence:
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OHS issues are considered at all times. Completed |
Evidence:
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Skills and knowledge are updated to accommodate changes in operating environment and equipment. Completed |
Evidence:
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Select suitable computing platforms and software systems.
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Spatial computing platforms and software systems are assessed for suitability in line with the project specification. Completed |
Evidence:
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Availability of suitable data is verified with the potential suppliers. Completed |
Evidence:
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Constraints on use of spatial data are assessed against specification. Completed |
Evidence:
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Commercially available image processing systems are assessed to determine appropriate components, menu items, characteristics and statistics. Completed |
Evidence:
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Conduct image enhancements and manipulations.
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Transformation routines using image calculations are conducted. Completed |
Evidence:
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Edge enhancements and smoothing filters are applied with the use of convolution matrices. Completed |
Evidence:
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Image transformation is performed with channels of brightness, greenness and wetness. Completed |
Evidence:
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Imagery for distribution is determined. Completed |
Evidence:
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Perform supervised and unsupervised classifications on datasets.
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Thematic classifications and relative differentiations between supervised and unsupervised classification algorithms are determined. Completed |
Evidence:
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Supervised classifications of algorithms are conducted with the use of training areas. Completed |
Evidence:
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Hard copy outputs are produced according to specifications. Completed |
Evidence:
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Error analysis is applied to perform an approximate accuracy assessment of classifications. Completed |
Evidence:
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Conduct data merger and GIS integration.
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Components of integration and merging techniques are summarised. Completed |
Evidence:
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Techniques of use for the GIS data are documented. Completed |
Evidence:
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