2018 26th International Conference on Geoinformatics最新文献

Research on Dynamic Evaluation of Urban Community Livability Based on Multi-Source Spatio-Temporal Data 基于多源时空数据的城市社区宜居性动态评价研究

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-28 DOI: 10.1109/GEOINFORMATICS.2018.8557086

Meiling Ning, Yang Yu, Huijuan Jiang, Q. Gao

{"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}

引用次数: 3

Reconstructing 3D Scenes from UAV Images Using a Structure-from-Motion Pipeline 利用运动结构管道从无人机图像重建三维场景

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-01 DOI: 10.1109/GEOINFORMATICS.2018.8557153

Xueman Zhang, Zhong Xie

{"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}

引用次数: 1

A Highly Efficient Method for Training Sample Selection in Remote Sensing Classification 一种高效的遥感分类训练样本选择方法

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-01 DOI: 10.1109/GEOINFORMATICS.2018.8557085

Chao Yang, Qingquan Li, Guofeng Wu, Junyi Chen

{"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}

引用次数: 2

A Study to Compare Three Different Spatial Downscaling Algorithms of Annual TRMM 3B43 Precipitation TRMM 3B43年降水空间降尺度算法的比较研究

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-01 DOI: 10.1109/GEOINFORMATICS.2018.8557151

Wenhao Xie, Shanzhen Yi, C. Leng

{"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}

引用次数: 0

Can Crowdsourcing Support Remote Sensing Image Classification? 众包能支持遥感图像分类吗?

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-01 DOI: 10.1109/GEOINFORMATICS.2018.8557144

Huan Li, Hong Yang, Chao Zeng

{"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}

引用次数: 1

High-Frequency Jitter Detection by Registration Error Curve of High-Resolution Multi-Spectral Satellite Image 高分辨率多光谱卫星图像配准误差曲线的高频抖动检测

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-01 DOI: 10.1109/GEOINFORMATICS.2018.8557172

Kun Hu, Yongjun Zhang, W. Liu

引用次数: 3

A Remote Monitoring System for Water Quality Based on GPRS in Poor Signal Environment-Poyang Lake for Example 信号差环境下基于GPRS的水质远程监测系统——以鄱阳湖为例

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-01 DOI: 10.1109/GEOINFORMATICS.2018.8557181

M. He, Chaoyang Fang, Qi Huang, Jilin Yan

引用次数: 0

Present Situation and Trend of Remote Sensing Land Use/Cover Classification Extraction 遥感土地利用/覆被分类提取的现状与趋势

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-01 DOI: 10.1109/GEOINFORMATICS.2018.8557159

Tingfang Jia, Yi Luo, Juan Chen, Wen Dong

{"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}

引用次数: 1

Design and Development of 3D Urban Planning Management System Based on Oblique Image Technology 基于斜像技术的三维城市规划管理系统的设计与开发

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-01 DOI: 10.1109/GEOINFORMATICS.2018.8557176

Liyan Ren, Yingcheng Li, Jincheng Xiao, Zhongyuan Geng, Enquan Wang, Tao Wang

引用次数: 1

Detecting and Analyzing Flight Unstable Approaches with QAR Big Data 基于QAR大数据的飞行不稳定进近探测与分析

2018 26th International Conference on Geoinformatics Pub Date : 2018-06-01 DOI: 10.1109/GEOINFORMATICS.2018.8557146

Chen Wu, Huabo Sun, Yang Jiao, Jiayi Xie, Binbin Lu

引用次数: 2