• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.56-68
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• 2009

Ongoing and extensive urbanisation, which is frequently accompanied with careless construction works, may threaten important archaeological structures that are still buried in the urban areas. Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) methods are most promising alternatives for resolving buried archaeological structures in urban territories. In this work, three case studies are presented, each of which involves an integrated geophysical survey employing the surface three-dimensional (3D) ERT and GPR techniques, in order to archaeologically characterise the investigated areas. The test field sites are located at the historical centres of two of the most populated cities of the island of Crete, in Greece. The ERT and GPR data were collected along a dense network of parallel profiles. The subsurface resistivity structure was reconstructed by processing the apparent resistivity data with a 3D inversion algorithm. The GPR sections were processed with a systematic way, applying specific filters to the data in order to enhance their information content. Finally, horizontal depth slices representing the 3D variation of the physical properties were created. The GPR and ERT images significantly contributed in reconstructing the complex subsurface properties in these urban areas. Strong GPR reflections and highresistivity anomalies were correlated with possible archaeological structures. Subsequent excavations in specific places at both sites verified the geophysical results. The specific case studies demonstrated the applicability of ERT and GPR techniques during the design and construction stages of urban infrastructure works, indicating areas of archaeological significance and guiding archaeological excavations before construction work.

• Journal of agricultural medicine and community health
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• v.24 no.2
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• pp.331-350
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• 1999

This study was attempted to examine relationship between personality traits and job satisfaction of community health practitioners(CHPs) working in remote rural area in order to suggest some methods to enhance their lob performance and the degrees of job satisfaction. The General Personality Test and the revised version of Job Satisfaction Questionnaire were administered to 200 of 348 CHPs in the Kwangju-Chonnam area and then the percentages, means, standard deviations and Pearson's correlation coefficients of these data were obtained, ANOVA and logistic analysis were used. The results of study were as follows : 1. CHPs without religion were more satisfied with their salary than those with religion. 2. CHPs who hoped for continuous education showed higher scores than the others on necessary job, professional pride and autonomy. Those who chose for independent job showed higher scores than the others on both necessary job and professional pride. Those who hope for long duration showed higher scores than the others on both necessary job and professional pride. Those who were satisfied with the present occupation showed higher scores than the others on pay satisfaction, necessary job, professional pride, interaction, autonomy and demand from organization. 3. Their autonomy scores differed significantly according to work status, both interaction and autonomy scores did so according to the fields of the past job in CHP, and their autonomy scores according to location of clinics. Their interaction scores differed significantly according to the frequency of home visits per mouth, both the degrees of salary satisfaction and professional pride scores did so according to the frequency of counseling education per mouth, and their professional pride scores did so according to total income per year. 4. The levels of their responsibility and self-confidence showed the highest of all personality traits variables. 5. The professional pride score of CHPs showed the highest of all job satisfaction variables. 6. Dominance were mostly correlated with autonomy and responsibility were mostly associated with professional pride. Both emotional stability and self-confidence were mostly related necessary job. In conclusion, religion, location of clinics, clinical experience, opportunity for education, dominance, self-confidence, the duration of services hoped for, satisfaction with the present occupation, the field of past job and administrative affairs were found to be the important factors in the degrees of their job satisfaction. Therefore, the methods to consider these variables will be necessary to develop for enhancing the efficiency of their Job performance and the degrees of job satisfaction.