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系統識別號 U0026-2701201916375800
論文名稱(中文) 結合遙測與社會感知於土地使用分類
論文名稱(英文) Land Use Classification from Integration of Remote Sensing and Social Sensing Data
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 107
學期 1
出版年 108
研究生(中文) 蘇來堯
研究生(英文) Adindha Surya Anugraha
電子信箱 adindha.surya@gmail.com
學號 P66067047
學位類別 碩士
語文別 英文
論文頁數 81頁
口試委員 指導教授-朱宏杰
口試委員-呂學展
口試委員-吳治達
中文關鍵字 none 
英文關鍵字 Human Behavior  Social Sensing  Remote Sensing  Decision Tree  Random Forest  Accuracy Assessment 
學科別分類
中文摘要 none
英文摘要 Nowadays, the demand of utilization of urban land use maps by urban authorities, researchers, and citizens, steadily increased since the past decades. Utilization urban land use map reveals the human activity issues especially for human behavior in cities. Discovering patterns of human behavior is useful for urban authorities and citizens, especially in traffic forecasting, social science and urban planning. To discover patterns of land use in cities, sensing and analyzing the remote and social sensing data need to be conducted.

New York City in the United States of America (USA) is selected as the study area in this research including Manhattan District, Brooklyn District and Queens District. Two types of openly accessible data, namely remote sensing data and social sensing data are selected as the datasets. The social sensing data are bike and taxi data, whereas remoted sensed imagery is the Sentinel 2 sattelite image data. This research contains two aims, to sense and analyze the human behavior pattern, and to classify land use from the integration of remote sensing and social sensing data. The human behavior in the city can be discovered by extracting the bike and taxi data and understanding where and when people are in the city based on Geographic Information System (GIS). The two peak times at weekday happen at 8-9 am and 6-7 pm for both bike and taxi usage. Meanwhile, the peak times at weekend happen at 5-6 pm for bike usage, while for taxi usage happen at 6-7 pm. The bike and taxi density maps are generated to display where people are located in the city during the peak times, and it also can be reflected as residential area, office area and entertainment area.

A decision tree and random forest are used to perform the urban land use classification for 5 classes and 6 classes in the city. Accuracy assessment of the urban land use classification map is required to test the quality of supervised classification result of decision tree and random forest. Therefore, the overall accuracy of the 5 classes land use classified map reach 85.33% for decision tree and 86.96% for random forest, whereas the 6 classes land use classified map for decision tree and random forest reach 83.33% and 85.67%, respectively. The result of overall accuracy shows if demonstrate the integration of remote sensing data and social sensing data are capable for urban land use classification accurately and random forest has better overall accuracy than decision tree.
論文目次 ABSTRACT I
ACKNOWLEDGMENT III
TABLE OF CONTENTS IV
LIST OF FIGURE VII
LIST OF TABLES IX
CHAPTER 1 : INTRODUCTION 1
1.1 Background 1
1.2 Previous Study 3
1.3 Research Aim 6
CHAPTER 2 : RESEARCH METHODOLOGY 7
2.1 Study Area 7
2.2 Datasets 8
2.2.1. Remote Sensing Data 8
2.2.2. Social Sensing Data 9
2.2.2.1 Bike Data 9
2.2.2.2 Taxi Data 12
2.2.2.3 Open Street Map 14
2.2.3 Land Use from Government 15
2.3. Class Definition 17
2.4. Study Case 19
2.5. Method 21
2.5.1. Point Distribution 23
2.5.2. Data Cleaning 25
2.5.3. Density Map 26
2.5.2.1. Bike and Taxi Density Map 27
2.5.2.2. POI Density Map 27
2.5.4. Decision Tree 30
2.5.5. Random Forest 31
2.5.6. Training & Testing Sampling 32
2.5.6.1. Training Sampling 32
2.5.6.2. Testing Sampling 33
2.5.7. Accuracy Assessment 34
CHAPTER 3 : RESULT AND DISCUSSION 36
3.1 Temporal Analysis of Bike & Taxi Data 36
3.1.1. Weekday Time 37
3.1.2. Weekend Time 38
3.2 Effect of Data Cleaning in Bike and Taxi Point Distribution 40
3.2.1. Weekday Time 41
3.2.2. Weekend Time 44
3.3 Spatial Analysis of Bike and Taxi Density Maps 46
3.3.1. Weekday Time 46
3.3.2. Weekend Time 50
3.4 Classification Maps 53
3.4.1. 5-Classes of Classified Map 53
3.4.2. 6-Classes of Classified Map 54
3.5 Accuracy Assessment 55
3.5.1. Confusion Matrix of 5-Classes of Classified Map 56
3.5.2. Confusion Matrix of 6-Classes of Classified Map 57
3.6 Effect of Social Sensing and Data Cleaning in Land Use Classified Map 58
3.7 Comparison of Decision Tree and Random Forest 66
3.8 Comparison of Result from 5-Classes and 6-Classes 67
3.9 Comparison of Different Parameter Setting 72
CHAPTER 4 : CONCLUDING REMARKS 75
4.1 Conclusion 75
4.2 Future Studies 77
REFERENCES 78
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