Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第10页

Inferring semantics from geometry: the case of street networks 从几何推断语义学:街道网络的情况

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820822

P. Corcoran, Musfira Jilani, P. Mooney, M. Bertolotto

引用次数: 23

Time-efficient significance measure for discovering spatiotemporal co-occurrences from data with unbalanced characteristics 从具有不平衡特征的数据中发现时空共现的时效性显著性测度

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820871

Berkay Aydin, Vijay Akkineni, R. Angryk

引用次数: 13

Clarifying the role of distance in friendships on twitter: discovery of a double power-law relationship 澄清距离在twitter上友谊中的作用:双重幂律关系的发现

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-10-20 DOI: 10.1145/2820783.2820841

Won-Yong Shin, Jaehee Cho, A. Everett

引用次数: 1

semMatch: road semantics-based accurate map matching for challenging positioning data semMatch:基于道路语义的精确地图匹配，用于具有挑战性的定位数据

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-10-13 DOI: 10.1145/2820783.2820824

Heba Aly, M. Youssef

{"title":"semMatch: road semantics-based accurate map matching for challenging positioning data","authors":"Heba Aly, M. Youssef","doi":"10.1145/2820783.2820824","DOIUrl":"https://doi.org/10.1145/2820783.2820824","url":null,"abstract":"Map matching has been used to reduce the noisiness of the location estimates by aligning them to the road network on a digital map. A growing number of applications, e.g. energy-efficient localization and cellular provider side localization, depend on the availability of only sparse and coarse-grained positioning data; leading to a challenging map matching process. In this paper, we present semMatch: a system that can provide accurate HMM-based map matching for challenging positioning traces. semMatch leverages the smartphone's inertial sensors to detect different road semantics; such as speed bumps, tunnels, and turns; and uses them in a mathematically-principled way as hints to overcome the sparse, noisy, and coarse-grained input positioning data, improving the HMM map matching accuracy and efficiency. To do that, semMatch applies a series of preprocessing modules to handle the noisy locations. The filtered location data is then processed by the core of semMatch system using a novel incremental HMM algorithm that combines a semantics-enriched digital map and the car's ambient road semantics in its estimation process. We have evaluated semMatch using traces collected from different cities covering more than 150km under different harsh scenarios including coarse-grained cellular-based positioning data, sparse GPS traces with extremely low sampling rate, and noisy traces with a large number of back-and-force transitions. The results show that semMatch significantly outperforms traditional map matching algorithms under all scenarios, with an enhancement of at least 416% and 894% in precision and recall respectively in the most difficult cases. This highlights its promise as a next generation map matching algorithm for challenging environments.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131541666","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: 63

DeepSat: a learning framework for satellite imagery DeepSat:一个卫星图像的学习框架

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-09-11 DOI: 10.1145/2820783.2820816

Saikat Basu, S. Ganguly, S. Mukhopadhyay, Robert DiBiano, Manohar Karki, R. Nemani

{"title":"DeepSat: a learning framework for satellite imagery","authors":"Saikat Basu, S. Ganguly, S. Mukhopadhyay, Robert DiBiano, Manohar Karki, R. Nemani","doi":"10.1145/2820783.2820816","DOIUrl":"https://doi.org/10.1145/2820783.2820816","url":null,"abstract":"Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of satellite image analytics has also been inhibited by the lack of a single labeled high-resolution dataset with multiple class labels. The contributions of this paper are twofold -- (1) first, we present two new satellite datasets called SAT-4 and SAT-6, and (2) then, we propose a classification framework that extracts features from an input image, normalizes them and feeds the normalized feature vectors to a Deep Belief Network for classification. On the SAT-4 dataset, our best network produces a classification accuracy of 97.95% and outperforms three state-of-the-art object recognition algorithms, namely - Deep Belief Networks, Convolutional Neural Networks and Stacked Denoising Autoencoders by ~11%. On SAT-6, it produces a classification accuracy of 93.9% and outperforms the other algorithms by ~15%. Comparative studies with a Random Forest classifier show the advantage of an unsupervised learning approach over traditional supervised learning techniques. A statistical analysis based on Distribution Separability Criterion and Intrinsic Dimensionality Estimation substantiates the effectiveness of our approach in learning better representations for satellite imagery.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127763591","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: 291

Fast exact shortest path and distance queries on road networks with parametrized costs 具有参数化成本的路网快速精确最短路径和距离查询

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-09-10 DOI: 10.1145/2820783.2820856

Julian Dibbelt, Ben Strasser, D. Wagner

引用次数: 11

Optimal time-dependent sequenced route queries in road networks 路网中最优时序路线查询

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-09-07 DOI: 10.1145/2820783.2820846

Camila F. Costa, M. Nascimento, J. Macêdo, Y. Theodoridis, N. Pelekis, Javam C. Machado

引用次数: 16

PlanetSense: a real-time streaming and spatio-temporal analytics platform for gathering geo-spatial intelligence from open source data PlanetSense:一个实时流和时空分析平台，用于从开源数据中收集地理空间情报

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-07-19 DOI: 10.1145/2820783.2820882

Gautam Thakur, B. Bhaduri, J. Piburn, Kelly M. Sims, R. Stewart, M. Urban

{"title":"PlanetSense: a real-time streaming and spatio-temporal analytics platform for gathering geo-spatial intelligence from open source data","authors":"Gautam Thakur, B. Bhaduri, J. Piburn, Kelly M. Sims, R. Stewart, M. Urban","doi":"10.1145/2820783.2820882","DOIUrl":"https://doi.org/10.1145/2820783.2820882","url":null,"abstract":"Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, we propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components -- i) GeoData Cloud -- a data architecture for storing and managing disparate datasets; ii) Mechanism to harvest real-time streaming data; iii) Data analytics framework; iv) Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient population and building occupancy at scale.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116377830","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: 30

Incorporating road networks into territory design 将道路网络纳入版图设计

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-04-29 DOI: 10.1145/2820783.2820800

Nitin Ahuja, Matthias Bender, P. Sanders, Christian Schulz, Andreas Wagner

引用次数: 9