Automatic generation of geospatial dataset (MS 1759:2004) derived from total station data / Mohd Amry Johan Mohd Ali
A study towards automatic generation of geospatial dataset (MS 1759: 2004) derived from total station data is carried out. In this study, one hundred and thirty one (131) questionnaires were gathered from the licensed land surveyors in Malaysia who are the major contributors in carrying out engineer...
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Online Access: | http://ir.uitm.edu.my/id/eprint/27733/ http://ir.uitm.edu.my/id/eprint/27733/1/TM_MOHD%20AMRY%20JOHAN%20MOHD%20ALI%20AP%2016_5.pdf |
| Summary: | A study towards automatic generation of geospatial dataset (MS 1759: 2004) derived from total station data is carried out. In this study, one hundred and thirty one (131) questionnaires were gathered from the licensed land surveyors in Malaysia who are the major contributors in carrying out engineering survey work using total station. The method of processing total station data using the current software in the market generate different data format such as RTF file, ASCII file and drawing file. It is not capable for direct usage in GIS environment. If the datasets were to be used in a GIS environment, further processing which involves expertise and manual editing need to be carried out as to comply with the GIS standard data format (MS 1759:2004). The process incurred time, cost and labour intensive. An automation towards generating GIS data format (MS 1759:2004) from total station data is introduced. The automated system acquires engineering survey datasets from Civil Design and Survey (CDS) software and American Standard Code for Interchange (ASCII) format used by land surveyors and converts it to GIS data format. In the qualitative approached, there is an excellent match between the plotted dataset. As for the quantitative approach, it was found that the system is 80% faster than the current practice. As for the discrepancies check for the horizontal (X,Y) and reduced level value (Z) root mean square error (RMSE) are 0.006 m and 0.010 m respectively. The developed system will be of great advantage towards automated production of MS 1759:2004 dataset. |
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