Pub Date : 2018-06-28DOI: 10.1109/GEOINFORMATICS.2018.8557086
Meiling Ning, Yang Yu, Huijuan Jiang, Q. Gao
With the rapid development of urban economy, urban residents pay more attention to their living environment, livability has become a hot spot of the current era. Community environment is the basis for the survival and development of residents. Its advantages and disadvantages are related not only to the physical and mental health of residents, but also to the level of urban economic development and community construction. From four aspects of life convenience, travel convenience, residential safety and environmental comfort, combined with taxi trajectory data, POI data, geographical conditions census data and other multi-source data, measure the equilibrium distribution of basic public service facilities within the community by spatial mean, construct dynamic assessment method of urban community livability based on time interval community hot spot and community activity, it overcomes the shortcomings of single data source and long evaluation time in the past community evaluation. The evaluation index system of community livability in Wuhan is determined, using entropy method to calculate the weight of indicators at all levels and livable index weight at each period, obtains community livable index, analyze and evaluate community livability in the main urban areas of Wuhan from time and space level. It can provide decision-making basis for urban construction department to build livable communities, so as to improve the quality of life of urban residents, and provide help for the daily life of residents, buying and renting a house.
{"title":"Research on Dynamic Evaluation of Urban Community Livability Based on Multi-Source Spatio-Temporal Data","authors":"Meiling Ning, Yang Yu, Huijuan Jiang, Q. Gao","doi":"10.1109/GEOINFORMATICS.2018.8557086","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557086","url":null,"abstract":"With the rapid development of urban economy, urban residents pay more attention to their living environment, livability has become a hot spot of the current era. Community environment is the basis for the survival and development of residents. Its advantages and disadvantages are related not only to the physical and mental health of residents, but also to the level of urban economic development and community construction. From four aspects of life convenience, travel convenience, residential safety and environmental comfort, combined with taxi trajectory data, POI data, geographical conditions census data and other multi-source data, measure the equilibrium distribution of basic public service facilities within the community by spatial mean, construct dynamic assessment method of urban community livability based on time interval community hot spot and community activity, it overcomes the shortcomings of single data source and long evaluation time in the past community evaluation. The evaluation index system of community livability in Wuhan is determined, using entropy method to calculate the weight of indicators at all levels and livable index weight at each period, obtains community livable index, analyze and evaluate community livability in the main urban areas of Wuhan from time and space level. It can provide decision-making basis for urban construction department to build livable communities, so as to improve the quality of life of urban residents, and provide help for the daily life of residents, buying and renting a house.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127074499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557153
Xueman Zhang, Zhong Xie
This paper aims to apply grid-based motion statistics strategy for 3D reconstruction and promote the reconstruction results. Hence, a 3D reconstruction system that utilizes normal collections of unmanned aerial vehicle images using a Structure-from-Motion pipeline is provided. A typical incremental SfM is performed in this paper. It starts from an initial two-view reconstruction (the seed) that is iteratively extended by adding new views and 3D points, using pose estimation and triangulation. Later on, Bundle Adjustment (BA) is performed to minimize the accumulated error (drift). It is shown that reconstruction results have been improved and grid-based motion statistics strategy significantly improve the completeness and accuracy by mitigating drift effects. In addition, to evaluate our approach without ground truth, several different measures have been estimated. To assess the result of feature correspondence estimation and its effect on the SfM reconstruction result, this paper has measured the residual of the robust estimation and the root mean square error of the residuals of the SfM scene. While the incremental system has many advantages in robustness and accuracy, the efficiency remains its crucial challenge. This remains a problem to resolved in future works.
{"title":"Reconstructing 3D Scenes from UAV Images Using a Structure-from-Motion Pipeline","authors":"Xueman Zhang, Zhong Xie","doi":"10.1109/GEOINFORMATICS.2018.8557153","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557153","url":null,"abstract":"This paper aims to apply grid-based motion statistics strategy for 3D reconstruction and promote the reconstruction results. Hence, a 3D reconstruction system that utilizes normal collections of unmanned aerial vehicle images using a Structure-from-Motion pipeline is provided. A typical incremental SfM is performed in this paper. It starts from an initial two-view reconstruction (the seed) that is iteratively extended by adding new views and 3D points, using pose estimation and triangulation. Later on, Bundle Adjustment (BA) is performed to minimize the accumulated error (drift). It is shown that reconstruction results have been improved and grid-based motion statistics strategy significantly improve the completeness and accuracy by mitigating drift effects. In addition, to evaluate our approach without ground truth, several different measures have been estimated. To assess the result of feature correspondence estimation and its effect on the SfM reconstruction result, this paper has measured the residual of the robust estimation and the root mean square error of the residuals of the SfM scene. While the incremental system has many advantages in robustness and accuracy, the efficiency remains its crucial challenge. This remains a problem to resolved in future works.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115135512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557085
Chao Yang, Qingquan Li, Guofeng Wu, Junyi Chen
Remote sensing classification is an important way to obtain land cover information, and the selection of classification training samples for most of the classification method is an expensive and time-consuming task. However, the traditional training samples selection method is a direct selection based on two-dimensional (2D) images, therefore, training sample selection efficiency is always low in the regions with complex terrain and landscape fragmentation, and the ROI (region of interest) separability is unsatisfactory for classification. This study aims at the low efficiency and low ROI separability for traditional training sample selection method put forward a new training sample selection method using a three-dimensional (3D) terrain model that was created by OLI image fusion digital elevation model (DEM) to select ROIs, which departs from the traditional method based on a two-dimensional image. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the proposed method obtained ROI separability that was greater than 1.9, and with most reaching 2.0; while the ROI separability of traditional method still had unqualified situation, which showed the new method was more effective.
{"title":"A Highly Efficient Method for Training Sample Selection in Remote Sensing Classification","authors":"Chao Yang, Qingquan Li, Guofeng Wu, Junyi Chen","doi":"10.1109/GEOINFORMATICS.2018.8557085","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557085","url":null,"abstract":"Remote sensing classification is an important way to obtain land cover information, and the selection of classification training samples for most of the classification method is an expensive and time-consuming task. However, the traditional training samples selection method is a direct selection based on two-dimensional (2D) images, therefore, training sample selection efficiency is always low in the regions with complex terrain and landscape fragmentation, and the ROI (region of interest) separability is unsatisfactory for classification. This study aims at the low efficiency and low ROI separability for traditional training sample selection method put forward a new training sample selection method using a three-dimensional (3D) terrain model that was created by OLI image fusion digital elevation model (DEM) to select ROIs, which departs from the traditional method based on a two-dimensional image. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the proposed method obtained ROI separability that was greater than 1.9, and with most reaching 2.0; while the ROI separability of traditional method still had unqualified situation, which showed the new method was more effective.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115390125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557151
Wenhao Xie, Shanzhen Yi, C. Leng
High spatial resolution, high accuracy precipitation data is essential for understanding basin-scale hydrology and the spatiotemporal distribution of regional precipitation. Since satellite precipitation products are often too coarse for practical applications, it is necessary to develop spatial downscaling algorithms. In this study, we investigated three downscaling algorithms based on the Multiple Linear Regression (MLR), Random Forest (RF), and Geographic Weighted Regression (GWR), respectively. They were used to downscale annual precipitation from 2005 to 2016 from the Tropical Rainfall Measuring Mission (TRMM) from 25 km $times 25$ km to 1 km $times$ 1km. Ground observations were used to validate the accuracy of the downscaled precipitation. The results showed that (1) GWR can capture precipitation spatial distribution of the original TRMM but MLR and RF can only capture global trend without residual correction. While after residual correction, MLR and RF also can capture spatial distribution of the original TRMM. (2) Residual correction was indispensable for the MLR-based and RF-based downscaling algorithms but not recommend for the GWR-based algorithm. (3) GWR and MLR were easy to overfit while RF can avoid overfitting well. When no overfitting existed, the GWR-based algorithms had the best accuracy among three algorithms. But with the number of predictors increasing, the accuracy of MLR-based and GWR-based algorithms would decrease but the accuracy of RF-based algorithms would increase which would eventually make the RF-based algorithms have the best accuracy among three algorithms. (4) The MLR-based, RF-based, and GWR-based algorithms improved the resolution of the original TRMM 3B43 at cost of reducing its accuracy.
{"title":"A Study to Compare Three Different Spatial Downscaling Algorithms of Annual TRMM 3B43 Precipitation","authors":"Wenhao Xie, Shanzhen Yi, C. Leng","doi":"10.1109/GEOINFORMATICS.2018.8557151","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557151","url":null,"abstract":"High spatial resolution, high accuracy precipitation data is essential for understanding basin-scale hydrology and the spatiotemporal distribution of regional precipitation. Since satellite precipitation products are often too coarse for practical applications, it is necessary to develop spatial downscaling algorithms. In this study, we investigated three downscaling algorithms based on the Multiple Linear Regression (MLR), Random Forest (RF), and Geographic Weighted Regression (GWR), respectively. They were used to downscale annual precipitation from 2005 to 2016 from the Tropical Rainfall Measuring Mission (TRMM) from 25 km $times 25$ km to 1 km $times$ 1km. Ground observations were used to validate the accuracy of the downscaled precipitation. The results showed that (1) GWR can capture precipitation spatial distribution of the original TRMM but MLR and RF can only capture global trend without residual correction. While after residual correction, MLR and RF also can capture spatial distribution of the original TRMM. (2) Residual correction was indispensable for the MLR-based and RF-based downscaling algorithms but not recommend for the GWR-based algorithm. (3) GWR and MLR were easy to overfit while RF can avoid overfitting well. When no overfitting existed, the GWR-based algorithms had the best accuracy among three algorithms. But with the number of predictors increasing, the accuracy of MLR-based and GWR-based algorithms would decrease but the accuracy of RF-based algorithms would increase which would eventually make the RF-based algorithms have the best accuracy among three algorithms. (4) The MLR-based, RF-based, and GWR-based algorithms improved the resolution of the original TRMM 3B43 at cost of reducing its accuracy.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123131771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557144
Huan Li, Hong Yang, Chao Zeng
There is great value to extract artificial surface from remote sensing images to understand urban expansion dynamics. With crowdsourcing data like Open Street Map (OSM), a great amount of labeled training data can be used as input of many supervised classification methods like Neural Network. This study explores the potential application of combining crowdsourcing data and remote sensing images in artificial surface extraction. A 1000 km2 area of a Landsat 8 image in Beijing, the capital city of China, is chosen as the case study. Comparing with a spectral method Normalized Differential Building Index (NDBI) and an unsupervised method ISODATA, the freely available labeled building foot scripts by OSM are used as training datasets for several supervised classification methods including Maximum Likelihood Classification (MLC), Supporting Vector Machine (SVM), and Neural Network (NN). The estimation by OSM point features with building-like attributes shows that the accuracies of the five classification methods NDBI, ISODATA, MLC, SVM, and NN are 8.51 %, 45.39%, 75.18%, 85.11 %, and 93.62% respectively. This means that the combination of crowdsourcing and remote sensing has a very potential value for satellites applications like artificial surface extraction.
从遥感影像中提取人工地表对了解城市扩张动态具有重要价值。开放街道地图(Open Street Map, OSM)等众包数据，可以将大量带标签的训练数据作为神经网络等许多监督分类方法的输入。本研究探讨了众包数据与遥感影像相结合在人工地表提取中的潜在应用。在中国首都北京，选择了一个1000平方公里的Landsat 8图像作为案例研究。通过与光谱法归一化差分建筑指数(NDBI)和无监督方法ISODATA的比较，利用OSM方法获得的标记建筑脚脚脚本作为最大似然分类(MLC)、支持向量机(SVM)和神经网络(NN)等几种监督分类方法的训练数据集。基于类建筑属性的OSM点特征估计表明，NDBI、ISODATA、MLC、SVM和NN 5种分类方法的准确率分别为8.51%、45.39%、75.18%、85.11%和93.62%。这意味着众包和遥感的结合对于人造地表提取等卫星应用具有非常潜在的价值。
{"title":"Can Crowdsourcing Support Remote Sensing Image Classification?","authors":"Huan Li, Hong Yang, Chao Zeng","doi":"10.1109/GEOINFORMATICS.2018.8557144","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557144","url":null,"abstract":"There is great value to extract artificial surface from remote sensing images to understand urban expansion dynamics. With crowdsourcing data like Open Street Map (OSM), a great amount of labeled training data can be used as input of many supervised classification methods like Neural Network. This study explores the potential application of combining crowdsourcing data and remote sensing images in artificial surface extraction. A 1000 km2 area of a Landsat 8 image in Beijing, the capital city of China, is chosen as the case study. Comparing with a spectral method Normalized Differential Building Index (NDBI) and an unsupervised method ISODATA, the freely available labeled building foot scripts by OSM are used as training datasets for several supervised classification methods including Maximum Likelihood Classification (MLC), Supporting Vector Machine (SVM), and Neural Network (NN). The estimation by OSM point features with building-like attributes shows that the accuracies of the five classification methods NDBI, ISODATA, MLC, SVM, and NN are 8.51 %, 45.39%, 75.18%, 85.11 %, and 93.62% respectively. This means that the combination of crowdsourcing and remote sensing has a very potential value for satellites applications like artificial surface extraction.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117237526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557172
Kun Hu, Yongjun Zhang, W. Liu
With improvement of spatial resolution of the satellite optical sensors, the influence of high-frequency attitude jitter of the satellite platform on image geometric and radiometric quality has become more and more seriously. It will obviously decrease the image absolute positioning accuracy, the charge coupled device (CCD) geometric splicing accuracy and the image clarity. Based on the design features of time-division multi-spectral sensor of the Chinese Mapping Satellite-I, a high-frequency jitter detection method by dense matching and image registration error curve is proposed in this paper. The technique processing of jitter detection, the modeling and solution method of registration error curve and construction method of high-frequency jitter model are illustrated in details. Experiments and result analysis of dense matching, image registration and jitter curve extraction are conducted on the multi-spectral image of Chinese Mapping Satellite-I to validate the correctness of the proposed approach.
{"title":"High-Frequency Jitter Detection by Registration Error Curve of High-Resolution Multi-Spectral Satellite Image","authors":"Kun Hu, Yongjun Zhang, W. Liu","doi":"10.1109/GEOINFORMATICS.2018.8557172","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557172","url":null,"abstract":"With improvement of spatial resolution of the satellite optical sensors, the influence of high-frequency attitude jitter of the satellite platform on image geometric and radiometric quality has become more and more seriously. It will obviously decrease the image absolute positioning accuracy, the charge coupled device (CCD) geometric splicing accuracy and the image clarity. Based on the design features of time-division multi-spectral sensor of the Chinese Mapping Satellite-I, a high-frequency jitter detection method by dense matching and image registration error curve is proposed in this paper. The technique processing of jitter detection, the modeling and solution method of registration error curve and construction method of high-frequency jitter model are illustrated in details. Experiments and result analysis of dense matching, image registration and jitter curve extraction are conducted on the multi-spectral image of Chinese Mapping Satellite-I to validate the correctness of the proposed approach.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122315341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557181
M. He, Chaoyang Fang, Qi Huang, Jilin Yan
The role of sensor network technology in remote environmental monitoring and data collection have long been recognized especially for environmental monitoring, which can realize real-time, continuous and efficient monitoring. Now, most research on the application of sensor networks have focused on the application of sensor networks in different industries, but they all have neglected the application of sensors in different conditions in the same industry. In this paper, the main objective is remote monitoring of water quality in poor signal environment and the correspond novel approach is proposed. An experiment is conducted to validate the proposed approach and the result indicates that the proposed approach is effective for remote monitoring of water quality in areas with poor signals in terms of the continuity and effectiveness of monitoring results.
{"title":"A Remote Monitoring System for Water Quality Based on GPRS in Poor Signal Environment-Poyang Lake for Example","authors":"M. He, Chaoyang Fang, Qi Huang, Jilin Yan","doi":"10.1109/GEOINFORMATICS.2018.8557181","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557181","url":null,"abstract":"The role of sensor network technology in remote environmental monitoring and data collection have long been recognized especially for environmental monitoring, which can realize real-time, continuous and efficient monitoring. Now, most research on the application of sensor networks have focused on the application of sensor networks in different industries, but they all have neglected the application of sensors in different conditions in the same industry. In this paper, the main objective is remote monitoring of water quality in poor signal environment and the correspond novel approach is proposed. An experiment is conducted to validate the proposed approach and the result indicates that the proposed approach is effective for remote monitoring of water quality in areas with poor signals in terms of the continuity and effectiveness of monitoring results.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122662850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557159
Tingfang Jia, Yi Luo, Juan Chen, Wen Dong
To further advance the automatic process of land use/cover (LULC) classification extraction through remote sensing (RS) images, by reading many literatures, we summarized the problems, research difficulties and development trends in the process of information extraction and classification of land use. Overall, LULC Classification and extraction based on RS images include 3 tasks: data source selection, sampling design, classification method selection and classifier performance evaluation. These tasks are all important, that is, interdependence and mutual influence. The OBIC method has become a popular method of L ULC classification because it makes full use of geographic information system (GIS) technology to process spatial, spectral and textural features in RS images. There are many OBIC algorithms, especially the Machine learning (ML) algorithms offers the potential for effectiveness and efficiency, such as Random forest (RF), Support vector machine (SVM) and so on. The Object-based image classification (OBIC) method involves three stages: segmentation, feature-selection and classification. A large number of studies have proved that there are many problems in each task of the LCLU classification extraction method based on RS images. These problems include design of sample sampling strategy, determination of optimal image segmentation parameters and optimization of parameter of classification algorithm and so on. At present, solving these problems requires frequent human-computer interaction also has a great negative influence on the automatic extraction process of remote sensing classification. U sing GIS technology to promote the automatic extraction of remote sensing classification has become a trend of the development of remote sensing classification method.
{"title":"Present Situation and Trend of Remote Sensing Land Use/Cover Classification Extraction","authors":"Tingfang Jia, Yi Luo, Juan Chen, Wen Dong","doi":"10.1109/GEOINFORMATICS.2018.8557159","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557159","url":null,"abstract":"To further advance the automatic process of land use/cover (LULC) classification extraction through remote sensing (RS) images, by reading many literatures, we summarized the problems, research difficulties and development trends in the process of information extraction and classification of land use. Overall, LULC Classification and extraction based on RS images include 3 tasks: data source selection, sampling design, classification method selection and classifier performance evaluation. These tasks are all important, that is, interdependence and mutual influence. The OBIC method has become a popular method of L ULC classification because it makes full use of geographic information system (GIS) technology to process spatial, spectral and textural features in RS images. There are many OBIC algorithms, especially the Machine learning (ML) algorithms offers the potential for effectiveness and efficiency, such as Random forest (RF), Support vector machine (SVM) and so on. The Object-based image classification (OBIC) method involves three stages: segmentation, feature-selection and classification. A large number of studies have proved that there are many problems in each task of the LCLU classification extraction method based on RS images. These problems include design of sample sampling strategy, determination of optimal image segmentation parameters and optimization of parameter of classification algorithm and so on. At present, solving these problems requires frequent human-computer interaction also has a great negative influence on the automatic extraction process of remote sensing classification. U sing GIS technology to promote the automatic extraction of remote sensing classification has become a trend of the development of remote sensing classification method.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122887408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557176
Liyan Ren, Yingcheng Li, Jincheng Xiao, Zhongyuan Geng, Enquan Wang, Tao Wang
The traditional 2D urban planning management system with its low data visualization and weak spatial analysis capabilities is inadequate in meeting the needs of modern urban development. To address this issue, this paper reports on a 3D urban planning management system that was built using oblique image and 3D GIS technologies to create 3D scenes, but full using 2D planning information and urban planning scenarios to assist users in their decision-making. The system was built on Topword platform, using component development technology in combination with oblique imagery application for 3D modeling of a portion of the district of Hailaer in China. This paper introduces the architecture, key technologies, functions and application of the system, which is proven not only to display real-word information in 3D but also provides 3D spatial analysis and visualization tools for urban planning.
{"title":"Design and Development of 3D Urban Planning Management System Based on Oblique Image Technology","authors":"Liyan Ren, Yingcheng Li, Jincheng Xiao, Zhongyuan Geng, Enquan Wang, Tao Wang","doi":"10.1109/GEOINFORMATICS.2018.8557176","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557176","url":null,"abstract":"The traditional 2D urban planning management system with its low data visualization and weak spatial analysis capabilities is inadequate in meeting the needs of modern urban development. To address this issue, this paper reports on a 3D urban planning management system that was built using oblique image and 3D GIS technologies to create 3D scenes, but full using 2D planning information and urban planning scenarios to assist users in their decision-making. The system was built on Topword platform, using component development technology in combination with oblique imagery application for 3D modeling of a portion of the district of Hailaer in China. This paper introduces the architecture, key technologies, functions and application of the system, which is proven not only to display real-word information in 3D but also provides 3D spatial analysis and visualization tools for urban planning.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117100454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-01DOI: 10.1109/GEOINFORMATICS.2018.8557146
Chen Wu, Huabo Sun, Yang Jiao, Jiayi Xie, Binbin Lu
Stable approach is vital for flight safety, and unstable approach is one of the main causes of flight accidents. This study aims to detect flight unstable approaches (FUA) with the quick access recorder (QAR) big data, and analyze the spatio-temporal patterns via exploratory data analysis (EDA) technologies. Results show that the dominant factor of FUA incidents is overrun of airspeed. FUA incidents occurred the most frequently in Shanghai, especially on January 8th and 23th. With combining the meteorological data, we found that the FUA incidents closely relate to weather of spatially varying effects. These findings make practical senses in preventing FUA incidents and safeguarding flights.
{"title":"Detecting and Analyzing Flight Unstable Approaches with QAR Big Data","authors":"Chen Wu, Huabo Sun, Yang Jiao, Jiayi Xie, Binbin Lu","doi":"10.1109/GEOINFORMATICS.2018.8557146","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557146","url":null,"abstract":"Stable approach is vital for flight safety, and unstable approach is one of the main causes of flight accidents. This study aims to detect flight unstable approaches (FUA) with the quick access recorder (QAR) big data, and analyze the spatio-temporal patterns via exploratory data analysis (EDA) technologies. Results show that the dominant factor of FUA incidents is overrun of airspeed. FUA incidents occurred the most frequently in Shanghai, especially on January 8th and 23th. With combining the meteorological data, we found that the FUA incidents closely relate to weather of spatially varying effects. These findings make practical senses in preventing FUA incidents and safeguarding flights.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117301802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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