Proceedings of the 29th International Conference on Advances in Geographic Information Systems最新文献_第10页

OSM Ski Resort Routing OSM滑雪胜地路线

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483628

Wenzel Friedsam, Robin Hieber, Alexander Kharitonov, Tobias Rupp

引用次数: 2

Text-Based Delay Prediction in a Public Transport Monitoring System 基于文本的公交监控系统延误预测

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483630

A. Jastrzębska, W. Homenda

{"title":"Text-Based Delay Prediction in a Public Transport Monitoring System","authors":"A. Jastrzębska, W. Homenda","doi":"10.1145/3474717.3483630","DOIUrl":"https://doi.org/10.1145/3474717.3483630","url":null,"abstract":"Computing technologies have already established their place in various areas of public transport control in smart cities. While the analysis of signals coming from various sensors is executed at a very high level of sophistication, information expressed by humans in natural language is still not being used in a way that takes advantage of its full potential. Existing research on text mining in public transport monitoring is focused mainly on event detection. In this paper, we present a novel approach to vehicle delay prediction based on text data. The proposed method fuses information coming from standard sources (sensors) with text messages, to construct a regression model, that predicts delays for previously unseen messages describing road conditions. The method has been implemented based on an existing public transport monitoring system in Warsaw, Poland. In the paper, we discuss it briefly. Delay prediction based on information expressed in natural language will not replace standard methods for delay prediction that involve the use of vehicle sensors. However, it offers an attractive alternative to mine for knowledge from sources such as social media.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126663522","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

GEM: An Efficient Entity Matching Framework for Geospatial Data GEM:一种高效的地理空间数据实体匹配框架

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483973

Setu Shah, Venkata Vamsikrishna Meduri, Mohamed Sarwat

{"title":"GEM: An Efficient Entity Matching Framework for Geospatial Data","authors":"Setu Shah, Venkata Vamsikrishna Meduri, Mohamed Sarwat","doi":"10.1145/3474717.3483973","DOIUrl":"https://doi.org/10.1145/3474717.3483973","url":null,"abstract":"Identifying various mentions of the same real-world locations is known as spatial entity matching. GEM is an end-to-end Geospatial EM framework that matches polygon geometry entities in addition to point geometry type. Blocking, feature vector creation, and classification are the core steps of our system. GEM comprises of an efficient and lightweight blocking technique, GeoPrune, that uses the geohash encoding mechanism. We re-purpose the spatial proximality operators from Apache Sedona to create semantically rich spatial feature vectors. The classification step in GEM is a pluggable component, which consumes a unique feature vector and determines whether the geolocations match or not. We conduct experiments with three classifiers upon multiple large-scale geospatial datasets consisting of both spatial and relational attributes. GEM achieves an F-measure of 1.0 for a point x point dataset with 176k total pairs, which is 42% higher than a state-of-the-art spatial EM baseline. It achieves F-measures of 0.966 and 0.993 for the point x polygon dataset with 302M total pairs, and the polygon x polygon dataset with 16M total pairs respectively.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126156395","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

SALON

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.2307/j.ctt5hjq5s.13

Yue Hu, Sijie Ruan, Yuting Ni, Huajun He, J. Bao, Ruiyuan Li, Yu Zheng

引用次数: 7

ICFinder ICFinder

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483633

Zheng Zhu, Huimin Ren, Sijie Ruan, Boyang Han, J. Bao, Ruiyuan Li, Yanhua Li, Yu Zheng

引用次数: 7

Predicting the spread of COVID-19 in China with human mobility data 利用人员流动数据预测COVID-19在中国的传播

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483952

Shangbin Wu, Xiaoliang Fan, Longbiao Chen, Ming Cheng, Cheng Wang

引用次数: 3

Updating Street Maps using Changes Detected in Satellite Imagery 利用卫星图像检测到的变化更新街道地图

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-10-13 DOI: 10.1145/3474717.3483651

F. Bastani, Songtao He, Satvat Jagwani, Mohammad Alizadeh, Harinarayanan Balakrishnan, S. Chawla, Sam Madden, M. Sadeghi

{"title":"Updating Street Maps using Changes Detected in Satellite Imagery","authors":"F. Bastani, Songtao He, Satvat Jagwani, Mohammad Alizadeh, Harinarayanan Balakrishnan, S. Chawla, Sam Madden, M. Sadeghi","doi":"10.1145/3474717.3483651","DOIUrl":"https://doi.org/10.1145/3474717.3483651","url":null,"abstract":"Accurately maintaining digital street maps is labor-intensive. To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps. An end-to-end map update system would first process geospatial data sources to extract insights, and second leverage those insights to update and improve the map. However, prior work largely focuses on the first step of this pipeline: these map extraction methods infer road networks from scratch given geospatial data sources (in effect creating entirely new maps), but do not address the second step of leveraging this extracted information to update the existing map data. In this paper, we first explain why current map extraction techniques yield low accuracy when extended to update existing maps. We then propose a novel method that leverages the progression of satellite imagery over time to substantially improve accuracy. Our approach first compares satellite images captured at different times to identify portions of the physical road network that have visibly changed, and then updates the existing map accordingly. We show that our change-based approach reduces error rates four-fold.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115312036","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: 5

Online Adaptation of Parameters using GRU-based Neural Network with BO for Accurate Driving Model 基于gru神经网络的BO在线参数自适应精确驱动模型

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-09-24 DOI: 10.1145/3474717.3483632

Zhanhong Yang, Satoshi Masuda, Michiaki Tatsubori

{"title":"Online Adaptation of Parameters using GRU-based Neural Network with BO for Accurate Driving Model","authors":"Zhanhong Yang, Satoshi Masuda, Michiaki Tatsubori","doi":"10.1145/3474717.3483632","DOIUrl":"https://doi.org/10.1145/3474717.3483632","url":null,"abstract":"Testing self-driving cars in different areas requires surrounding cars with accordingly different driving styles such as aggressive or conservative styles. Calibrating a driving model (DM) makes the simulated driving behavior closer to human-driving behavior, and enable the simulation of human-driving cars. Conventional DM-calibrating methods do not take into account that the parameters in a DM vary while driving. These \"fixed\" calibrating methods cannot reflect an actual interactive driving scenario. In this paper, we propose a DM-calibration method for measuring human driving styles to reproduce real car-following behavior more accurately. The method includes 1) an objective entropy weight method for measuring and clustering human driving styles, and 2) online adaption of DM parameters based on deep learning by combining Bayesian optimization and a gated recurrent unit neural network. We conducted experiments to evaluate the proposed method, and the results indicate that it can be easily used to measure human driver styles. The experiments also showed that we can calibrate a corresponding DM in a virtual testing environment with up to 26% more accuracy than with fixed calibration methods.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":" 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132159195","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

Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning 自动特征-主题配对:空间表征学习中的语义对齐和嵌入空间

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-09-22 DOI: 10.1145/3474717.3484212

Dongjie Wang, Kunpeng Liu, David A. Mohaisen, Pengyang Wang, Chang-Tien Lu, Yanjie Fu

{"title":"Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning","authors":"Dongjie Wang, Kunpeng Liu, David A. Mohaisen, Pengyang Wang, Chang-Tien Lu, Yanjie Fu","doi":"10.1145/3474717.3484212","DOIUrl":"https://doi.org/10.1145/3474717.3484212","url":null,"abstract":"Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127923344","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: 5

POI Alias Discovery in Delivery Addresses using User Locations 使用用户位置在交付地址中发现POI别名

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-09-20 DOI: 10.1145/3474717.3483950

Tianfu He, Guochun Chen, Chuishi Meng, Huajun He, Zheyi Pan, Yexin Li, Sijie Ruan, Huimin Ren, Ye Yuan, Ruiyuan Li, Junbo Zhang, Jie Bao, Hui He, Yu Zheng

引用次数: 3