2013 Fourth International Conference on Computing for Geospatial Research and Application最新文献

Complex GIS Query Based on Rule Engine 基于规则引擎的复杂GIS查询

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.28

Liu Chen-fan, Liu Hai-yan

引用次数: 0

Superpixel Clustering and Planar Fit Segmentation of 3D LIDAR Point Clouds 三维激光雷达点云的超像素聚类与平面拟合分割

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.2

H. Mahmoudabadi, Timothy Shoaf, M. Olsen

{"title":"Superpixel Clustering and Planar Fit Segmentation of 3D LIDAR Point Clouds","authors":"H. Mahmoudabadi, Timothy Shoaf, M. Olsen","doi":"10.1109/COMGEO.2013.2","DOIUrl":"https://doi.org/10.1109/COMGEO.2013.2","url":null,"abstract":"Terrestrial laser scanning (TLS, also called ground based Light Detection and Ranging, LIDAR) is an effective data acquisition method capable of high precision, detailed 3D models for surveying natural environments. However, despite the high density, and quality, of the data itself, the data acquired contains no direct intelligence necessary for further modeling and analysis - merely the 3D geometry (XYZ), 3-component color (RGB), and laser return signal strength (I) for each point. One common task for LIDAR data processing is the selection of an appropriate methodology for the extraction of geometric features from the irregularly distributed point clouds. Such recognition schemes must accomplish both segmentation and classification. Planar (or other geometrically primitive) feature extraction is a common method for point cloud segmentation, however, current algorithms are computationally expensive and often do not utilize color or intensity information. In this paper we present an efficient algorithm, that takes advantage of both colorimetric and geometric data as input and consists of three principal steps to accomplish a more flexible form of feature extraction. First, we employ a Simple Linear Iterative Clustering (SLIC) super pixel algorithm for clustering and dividing the colorimetric data. Second, we use a plane-fitting technique on each significantly smaller cluster to produce a set of normal vectors corresponding to each super pixel. Last, we utilize a Least Squares Multi-class Support Vector Machine (LSMSVM) to classify each cluster as either \"ground\", \"wall\", or \"natural feature\". Despite the challenging problems presented by the occlusion of features during data acquisition, our method effectively generates accurate (>85%) segmentation results by utilizing the color space information, in addition to the standard geometry, during segmentation.","PeriodicalId":383309,"journal":{"name":"2013 Fourth International Conference on Computing for Geospatial Research and Application","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121461197","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: 10

DEM Generation with SAR Interferometry Based on Weighted Wavelet Phase Unwrapping 基于加权小波相位解包裹的SAR干涉DEM生成

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.14

M. Rahnemoonfar, Beth Plale

引用次数: 2

Analysis of Spatial Autocorrelation for Traffic Accident Data Based on Spatial Decision Tree 基于空间决策树的交通事故数据空间自相关分析

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.19

Bimal Ghimire, Shrutilipi Bhattacharjee, S. Ghosh

引用次数: 6

Storm System Database: A Big Data Approach to Moving Object Databases 风暴系统数据库:移动对象数据库的大数据方法

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.30

Brian Olsen, Mark McKenney

{"title":"Storm System Database: A Big Data Approach to Moving Object Databases","authors":"Brian Olsen, Mark McKenney","doi":"10.1109/COMGEO.2013.30","DOIUrl":"https://doi.org/10.1109/COMGEO.2013.30","url":null,"abstract":"Rainfall data is often collected by measuring the amount of precipitation collected in a physical container at a site. Such methods provide precise data for those sites, but are limited in granularity to the number and placement of collection devices. We use radar images of storm systems that are publicly available and provide rainfall estimates for large regions of the globe, but at the cost of loss of precision. We present a moving object database called Storm DB that stores decibel measurements of rain clouds as moving regions, i.e., we store a single rain cloud as a region that changes shape and position over time. Storm DB is a prototype system that answers rain amount queries over a user defined time duration for any point in the continental United States. In other words, a user can ask the database for the amount of rainfall that fell at any point in the US over a specified time window. Although this single query seems straightforward, it is complicated due to the expected size of the dataset: storm clouds are numerous, radar images are available in high resolution, and our system will collect data over a large timeframe, thus, we expect the number and size of moving regions representing storm clouds to be large. To implement our proposed query, we bring together the following concepts: (i) image processing to retrieve storm clouds from radar images, (ii) interpolation mechanisms to construct moving regions with infinite temporal resolution from region snapshots, (iii) transformations to compute exact point in moving polygon queries using 2-dimensional rather than 3-dimensional algorithms, (iv) GPU algorithms for massively parallel computation of the duration that a point lies inside a moving polygon, and (v) map/reduce algorithms to provide scalability. The resulting prototype lays the groundwork for building big data solutions for moving object databases.","PeriodicalId":383309,"journal":{"name":"2013 Fourth International Conference on Computing for Geospatial Research and Application","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116788786","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: 7

Road Segmentation in Aerial Images by Exploiting Road Vector Data 利用道路矢量数据对航拍图像进行道路分割

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.4

Jiangye Yuan, A. Cheriyadat

引用次数: 26

The Personality of Venues: Places and the Five-Factors ('Big Five') Model of Personality 场所的个性:场所与个性的五因素(“大五”)模型

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.12

Vlad Tanasescu, Christopher B. Jones, Gualtiero Colombo, M. Chorley, S. M. Allen, R. Whitaker

引用次数: 12

Asking Spatial Questions to Identify GIS Functionality 提出空间问题以识别GIS功能

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.18

Song Gao, Michael F. Goochild

引用次数: 17

Similarity-Based Compression of GPS Trajectory Data 基于相似度的GPS轨迹数据压缩

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.15

Jeremy Birnbaum, H. Meng, Jeong-Hyon Hwang, C. Lawson

引用次数: 15

A Framework for Efficient and Convenient Evaluation of Trajectory Compression Algorithms 一种高效方便的轨迹压缩算法评价框架

2013 Fourth International Conference on Computing for Geospatial Research and Application Pub Date : 2013-07-22 DOI: 10.1109/COMGEO.2013.5

Jonathan Muckell, Paul W. Olsen, Jeong-Hyon Hwang, S. Ravi, C. Lawson

{"title":"A Framework for Efficient and Convenient Evaluation of Trajectory Compression Algorithms","authors":"Jonathan Muckell, Paul W. Olsen, Jeong-Hyon Hwang, S. Ravi, C. Lawson","doi":"10.1109/COMGEO.2013.5","DOIUrl":"https://doi.org/10.1109/COMGEO.2013.5","url":null,"abstract":"Trajectory compression algorithms eliminate redundant information in the history of a moving object. Such compression enables efficient transmission, storage, and processing of trajectory data. Although a number of compression algorithms have been proposed in the literature, no common benchmarking platform for evaluating their effectiveness exists. This paper presents a benchmarking framework for efficiently, conveniently, and accurately comparing trajectory compression algorithms. This framework supports various compression algorithms and metrics defined in the literature, as well as three synthetic trajectory generators that have different trade-offs. It also has a highly extensible architecture that facilitates the incorporation of new compression algorithms, evaluation metrics, and trajectory data generators. This paper provides a comprehensive overview of trajectory compression algorithms, evaluation metrics and data generators in conjunction with detailed discussions on their unique benefits and relevant application scenarios. Furthermore, this paper describes challenges that arise in the design and implementation of the above framework and our approaches to tackling these challenges. Finally, this paper presents evaluation results that demonstrate the utility of the benchmarking framework.","PeriodicalId":383309,"journal":{"name":"2013 Fourth International Conference on Computing for Geospatial Research and Application","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126520831","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: 6