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

Complementary Fusion of Deep Network and Tree Model for ETA Prediction 深度网络与树模型互补融合的ETA预测

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3488237

Yurui Huang, Jie Zhang, HengDa Bao, Yang Yang, Jian Yang

引用次数: 1

Semantic Compression with Region Calculi in Nested Hierarchical Grids 基于区域演算的嵌套分层网格语义压缩

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483965

Joseph Zalewski, P. Hitzler, K. Janowicz

引用次数: 2

The Raptor Join Operator for Processing Big Raster + Vector Data 用于处理大栅格+矢量数据的猛禽连接算子

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483971

Samriddhi Singla, A. Eldawy, Tina Diao, Ayan Mukhopadhyay, E. Scudiero

{"title":"The Raptor Join Operator for Processing Big Raster + Vector Data","authors":"Samriddhi Singla, A. Eldawy, Tina Diao, Ayan Mukhopadhyay, E. Scudiero","doi":"10.1145/3474717.3483971","DOIUrl":"https://doi.org/10.1145/3474717.3483971","url":null,"abstract":"Pre-processing spatial data for machine learning applications often includes combining different datasets into a form usable by the machine learning algorithms. Spatial data is generally available in two representations, raster and vector. The best data science and machine learning applications need to combine multiple datasets of both representations which is a data and compute intensive problem. This paper proposes a formal raster-vector join operator, Raptor Join, that can bridge the gap between raster and vector data. It is modeled as a relational join operator in Spark that can be easily combined with other operators, while also offering the advantage of in-situ processing. To implement the Raptor join operator efficiently, we propose a novel Flash index that has a low memory requirement and can process the entire operation with one data scan. We run an extensive experimental evaluation on large scale satellite data with up-to a trillion pixels, and big vector data with up-to hundreds of millions of segments and billions of points, and show that the proposed method can scale to big data with up-to three orders of magnitude performance gain over baselines.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"49 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":"122488991","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

Semantically Diverse Paths with Range and Origin Constraints 具有范围和原点约束的语义不同路径

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483985

Xu Teng, Goce Trajcevski, Andreas Züfle

引用次数: 5

Improving Building Segmentation Using Uncertainty Modeling and Metadata Injection 利用不确定性建模和元数据注入改进建筑物分割

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483918

Hanxiang Hao, Sriram Baireddy, Kevin J. LaTourette, Latisha R. Konz, Moses W. Chan, M. Comer, E. Delp

{"title":"Improving Building Segmentation Using Uncertainty Modeling and Metadata Injection","authors":"Hanxiang Hao, Sriram Baireddy, Kevin J. LaTourette, Latisha R. Konz, Moses W. Chan, M. Comer, E. Delp","doi":"10.1145/3474717.3483918","DOIUrl":"https://doi.org/10.1145/3474717.3483918","url":null,"abstract":"Automatic building segmentation is an important task for satellite imagery analysis and scene understanding. Most existing segmentation methods focus on the case where the images are taken from directly overhead (i.e., low off-nadir/viewing angle). These methods often fail to provide accurate results on satellite images with larger off-nadir angles due to the higher noise level and lower spatial resolution. In this paper, we propose a method that is able to provide accurate building segmentation for satellite imagery captured from a large range of off-nadir angles. Based on Bayesian deep learning, we explicitly design our method to learn the data noise via aleatoric and epistemic uncertainty modeling. Satellite image metadata (e.g., off-nadir angle and ground sample distance) is also used in our model to further improve the result. We show that with uncertainty modeling and metadata injection, our method achieves better performance than the baseline method, especially for noisy images taken from large off-nadir angles1.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"27 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":"128519830","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

Origin-destination (OD) analysis based on big taxi trajectory data with XStar (Demo Paper) 基于XStar的出租车大轨迹数据原点-终点(OD)分析(演示论文)

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483925

Xiang Li, Yijun He

引用次数: 0

Geo-Attention Network for Traffic Condition Prediction and Travel Time Estimation 基于地理关注网络的交通状况预测与出行时间估计

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3488383

Jie Li, Wanyi Zhou, Zebin Chen, Yue-jiao Gong

引用次数: 0

Hierarchical Positional Approach for ETA Prediction ETA预测的层次定位方法

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3488240

Tomoki Saito, Shinichi Tanimoto, Fumihiko Takahashi

引用次数: 0

Dual-Attention Multi-Scale Graph Convolutional Networks for Highway Accident Delay Time Prediction 公路事故延迟时间预测的双注意多尺度图卷积网络

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484259

I-Ying Wu, Fandel Lin, Hsun-Ping Hsieh

{"title":"Dual-Attention Multi-Scale Graph Convolutional Networks for Highway Accident Delay Time Prediction","authors":"I-Ying Wu, Fandel Lin, Hsun-Ping Hsieh","doi":"10.1145/3474717.3484259","DOIUrl":"https://doi.org/10.1145/3474717.3484259","url":null,"abstract":"Traffic-related forecasting plays a critical role in determining transportation policy, unlike traditional approaches, which can only make decisions based on statistical results or historical experience. Through machine learning, we are able to capture the potential interactions between urban dynamics and find their mutual interactions in a spatial context. However, despite a plethora of traffic-related studies, few works have explored predicting the impact of congestion. Therefore, this paper focuses on predicting how a car accident leads to traffic congestion, especially the length of time it takes for the congestion to occur. Accordingly, we propose a novel model named Dual-Attention Multi-Scale Graph Convolutional Networks (DAMGNet) to address this issue. In this proposed model, heterogeneous data such as accident information, urban dynamics, and various highway network characteristics are considered and combined. Next, the context encoder encodes the accident data, and the spatial encoder captures the hidden features between multi-scale Graph Convolutional Networks (GCNs). With our designed dual attention mechanism, the DAMGNet model is able to effectively learn the correlation between features. The evaluations conducted on a real-world dataset prove that our DAMGNet has a significant improvement in RMSE and MAE over other comparative methods.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"75 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":"124287823","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

Feat-SKSJ: Fast and Exact Algorithm for Top-k Spatial-Keyword Similarity Join Fast - sksj: Top-k空间关键字相似度连接的快速精确算法

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483629

Daichi Amagata, Shohei Tsuruoka, Yusuke Arai, T. Hara

{"title":"Feat-SKSJ: Fast and Exact Algorithm for Top-k Spatial-Keyword Similarity Join","authors":"Daichi Amagata, Shohei Tsuruoka, Yusuke Arai, T. Hara","doi":"10.1145/3474717.3483629","DOIUrl":"https://doi.org/10.1145/3474717.3483629","url":null,"abstract":"Due to the proliferation of GPS-enabled mobile devices and IoT environments, location-based services are generating a large number of objects that contain both spatial and keyword information, and spatial-keyword databases are receiving much attention. This paper addresses the problem of top-k spatial-keyword similarity join, which outputs k object pairs with the highest similarity. This query is a primitive operator for important applications, including duplicate detection, recommendation, and clustering. The main bottleneck of the top-k spatial-keyword similarity join is to compute the similarity of a given object pair. To avoid this computation as much as possible, a state-of-the-art algorithm utilizes a filter that can skip the exact similarity computation of a given pair. However, this algorithm suffers from a loose threshold at the first stage, a high filtering cost, and the impossibility of filtering many pairs in a batch. We propose Feat-SKSJ, which removes these drawbacks and quickly outputs the exact result. Extensive experiments on real datasets show that Feat-SKSJ is significantly faster than the state-of-the-art algorithm.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"198 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":"124441420","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