• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.2
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• pp.109-115
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• 2014

The aim of the study is to present a method to build an octree and to query from it for huge 3D point clouds of which volumes correspond or surpass the main memory, based on the memory-efficient octree developed by Han(2013). To the end, the method directly refers to 3D point cloud stored in a file on a hard disk drive instead of referring to that duplicated in the main memory. In addition, the method can save time to rebuild octree by storing and restoring it from a file. The memory-referring method and the present file-referring one are analyzed using a dataset composed of 18 million points surveyed in a tunnel. In results, the memory-referring method enormously exceeded the speed of the file-referring one when generating octree and querying points. Meanwhile, it is remarkable that a still bigger dataset composed of over 300 million points could be queried by the file-referring method, which would not be possible by the memory-referring one, though an optimal octree destination level could not be reached. Furthermore, the octree rebuilding method proved itself to be very efficient by diminishing the restoration time to about 3% of the generation time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.481-486
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• 2014

This study aims at enhancing the performance of file-referring octree, suggested by Han(2014), for efficiently querying huge 3D point clouds, acquired by the 3D terrestrial laser scanning. Han's method(2014) has revealed a problem of heavy declining in query speed, when if it was applied on a very long tunnel, which is the lengthy and narrow shaped anisometric structure. Hereupon, the shape of octree has been analyzed of its influence on the query efficiency with the testing method of generating an independent octree in each isometric subdivision of 3D object boundary. This method tested query speed and main memory usage against the conventional single octree method by capturing about 300 million points in a very long tunnel. Finally, the testing method resulted in which twice faster query speed is taking similar size of memory. It is also approved that the conclusive factor influencing the query speed is the destination level, but the query speed can still increase with more proximity to isometric bounding shape of octree. While an excessive unbalance of octree shape along each axis can heavily degrade the query speed, the improvement of octree shape can be more effectively enhancing the query speed than increasement of destination level.