Proceedings of the 29th International Conference on Advances in Geographic Information Systems最新文献_第6页

GeoPalette

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483914

Wenmiao Hu, Yifang Yin, Y. Tan, An Tran, Hans Kruppa, Roger Zimmermann

{"title":"GeoPalette","authors":"Wenmiao Hu, Yifang Yin, Y. Tan, An Tran, Hans Kruppa, Roger Zimmermann","doi":"10.1145/3474717.3483914","DOIUrl":"https://doi.org/10.1145/3474717.3483914","url":null,"abstract":"In recent years, Geo-information extraction from high-resolution satellite imagery has attracted a lot of attention. However, because of the high cost of image acquisition and annotation, there are limited datasets available. Compared to close-range imagery datasets, existing satellite datasets have a much lower number of images and cover only a few scenarios (cities, background environments, etc.). They may not be sufficient for training robust learning models that fit all environmental conditions or be representative enough for training regional models that optimize for local scenarios. In this study, we propose GeoPalette, a Generative Adversarial Network (GAN) based tool to generate additional synthetic training samples for boosting model performance when the training dataset is limited. Our experiments on road segmentation show that using additional synthetic data can improves the model performance mean Intersection over Union (mIoU) from 60.92% to 64.44%, when 1,000 real training pairs are available for learning, which reaches a similar level of performance as a model is standard-trained on 4,000 real pairs (64.59%), i.e., a 4-fold reduction in real dataset size.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"492 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":"116287244","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

Collaboratively inspect large-area sewer pipe networks using pipe robotic capsules 使用管道机器人胶囊协同检查大面积污水管网

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483948

Yu Gu, Wei Tu, Qingquan Li, Tianhong Zhao, Dingyi Zhao, Song Zhu, Jiasong Zhu

{"title":"Collaboratively inspect large-area sewer pipe networks using pipe robotic capsules","authors":"Yu Gu, Wei Tu, Qingquan Li, Tianhong Zhao, Dingyi Zhao, Song Zhu, Jiasong Zhu","doi":"10.1145/3474717.3483948","DOIUrl":"https://doi.org/10.1145/3474717.3483948","url":null,"abstract":"Sewer pipe is an essential infrastructure in the city as it undertakes the transportation and circulation of wastewater resources. But sewer pipe it is easy to have faults and cause serious secondary urban accidents, such as road holes and road collapse. Because of the complex underground circumstance, inspecting large-area sewer pipes using closed-circuit television or periscope television is difficult. In this study, we proposed a collaborative sewer pipe inspection approach by using novel low-cost pipe robotic capsules, which capture the images of the pipeline inner walls when floating with the water flow. A set of workers collaboratively drop and salvage capsules to cover a large-area pipe network. The routes of workers and pipe capsules are optimized by a meta-heuristic algorithm integrating local search and simulated annealing. The deep neural network is used to recognize faults from raw captured images. A field experiment in Shenzhen was conducted to evaluate the performance of the proposed approach. The results demonstrate that it outperforms the naive inspection method with a shorter travel distance and less waiting time. It is also effective for inspecting the large-area sewer pipe networks with an overall precision of 0.92. It will help us to eliminate the potential safety risk of the public and promote the level of urban governance.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"14 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":"124145615","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

Distributed Spatio-Temporal k Nearest Neighbors Join 分布式时空k近邻连接

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484209

Ruiyuan Li, Rubin Wang, Junwen Liu, Zisheng Yu, Huajun He, Tianfu He, Sijie Ruan, Jie Bao, Chao Chen, F. Gu, Liang Hong, Yu Zheng

{"title":"Distributed Spatio-Temporal k Nearest Neighbors Join","authors":"Ruiyuan Li, Rubin Wang, Junwen Liu, Zisheng Yu, Huajun He, Tianfu He, Sijie Ruan, Jie Bao, Chao Chen, F. Gu, Liang Hong, Yu Zheng","doi":"10.1145/3474717.3484209","DOIUrl":"https://doi.org/10.1145/3474717.3484209","url":null,"abstract":"The rapid development of positioning technology produces an extremely large volume of spatio-temporal data with various geometry types such as point, line string, polygon, or a mixed combination of them. As one of the most basic but time-consuming operations, k nearest neighbors join (kNN join) has attracted much attention. However, most existing works for kNN join either ignore temporal information or consider point data only. This paper proposes a novel and useful problem, i.e., ST-kNN join, which considers both spatial closeness and temporal concurrency. To support ST-kNN join over a huge amount of spatio-temporal data with any geometry types efficiently, we propose a novel distributed solution based on Apache Spark. Specifically, our method adopts a two-round join framework. In the first round join, we propose a new spatio-temporal partitioning method that achieves spatio-temporal locality and load balance at the same time. We also propose a lightweight index structure, i.e., Time Range Count Index (TRC-index), to enable efficient ST-kNN join. In the second round join, to reduce the data transmission among different machines, we remove duplicates based on spatio-temporal reference points before shuffling local results. Extensive experiments are conducted using three real big datasets, showing that our method is much more scalable and achieves 9X faster than baselines. A demonstration system is deployed and the source code is released.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"13 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":"125994899","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

Brownian Bridge Interpolation for Human Mobility? 人类移动的布朗桥插值?

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483942

John Krumm

引用次数: 3

Improving Maps Auto-Complete Through Query Expansion (Demo Paper) 通过查询扩展改进地图自动补全(演示文件)

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484218

Shekoofeh Mokhtari, Alex Rusnak, Tsheko Mutungu, Dragomir Yankov

引用次数: 0

Parallel Co-location Pattern Mining based on Neighbor-Dependency Partition and Column Calculation 基于相邻依赖划分和列计算的并行同址模式挖掘

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483984

Peizhong Yang, Lizhen Wang, Xiaoxuan Wang, Lihua Zhou, Hongmei Chen

{"title":"Parallel Co-location Pattern Mining based on Neighbor-Dependency Partition and Column Calculation","authors":"Peizhong Yang, Lizhen Wang, Xiaoxuan Wang, Lihua Zhou, Hongmei Chen","doi":"10.1145/3474717.3483984","DOIUrl":"https://doi.org/10.1145/3474717.3483984","url":null,"abstract":"A co-location pattern is a subset of spatial features whose instances are frequently located together in proximate areas. Mining co-location patterns can discover spatial dependencies in spatial datasets and have particular value in many applications. However, it is challengeable to discover co-location patterns from massive spatial datasets, due to the expensive computational cost. In this paper, we present a novel parallel co-location pattern mining approach. First, dividing spatial neighbor relationships into some neighbor-dependency partitions enables to perform mining task on each partition independently in parallel. Then, a column-based calculation approach is proposed to replace the time-consuming generation of table instances for calculating the prevalence of patterns. To further reduce the search space of patterns on each partition, two pruning strategies are suggested. We implement the parallel co-location pattern mining algorithm based on neighbor-dependency partition and column calculation via MapReduce, named PCPM-NDPCC. Substantial experiments are conducted on real and synthetic datasets to examine the performance of PCPM-NDPCC. Experimental results reveal that PCPM-NDPCC has a significant improvement in efficiency than baseline algorithms and shows better scalability for massive spatial data processing.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"90 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":"132437316","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

Simulation-based Evacuation Planning for Urban Areas 基于模拟的城市地区疏散规划

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483963

Theodoros Chondrogiannis, Panagiotis Bouros, Winfried Emser

引用次数: 1

CrowdMap CrowdMap

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484269

Sitao Min, Ritesh Ahuja, Yingzhe Liu, Abbas Zaidi, Catherine Phu, Luciano Nocera, Cyrus Shahabi

引用次数: 2

How and to what extent does the spatial and temporal discretization schema affect GIS-based hydrological modelling? 空间和时间离散化模式如何以及在多大程度上影响基于gis的水文建模?

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484268

Honglin Zhu, Qiming Zhou

{"title":"How and to what extent does the spatial and temporal discretization schema affect GIS-based hydrological modelling?","authors":"Honglin Zhu, Qiming Zhou","doi":"10.1145/3474717.3484268","DOIUrl":"https://doi.org/10.1145/3474717.3484268","url":null,"abstract":"The justification of the spatial and temporal discretization schema is a critical step in the development of numerical hydrological models. Currently, the challenge remains in balancing the error and uncertainty induced by the algorithm and the mass calculation caused by the increase of the division of computational units. Thus, it is necessary to investigate an appropriate discretization scheme, which not only adequately represents the spatial heterogeneity characteristics, but also maintains a sufficiently high computational efficiency, with the constraints of the data validity and availability. This poster paper proposed a numerical hydrological model using different spatial and temporal discretization schema. Results show that the running time revealed an increase by an order of magnitude with the refinement of the grid size. The results also show that that the discretization schema impose various influences on different hydrological processes. For the infiltration process, the effect of the spatial and temporal resolution depend on the soil type; for the runoff process, the amount of the runoff was less affected but the time to runoff was significantly influenced. Establishing a standardized method to optimize the range of the spatial-temporal resolution for different the models and environmental scenarios, however, still remains challenge and is the future investigations.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"6 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":"117056487","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

Spatio-Temporal Graph Convolutional Networks for Traffic Forecasting: Spatial Layers First or Temporal Layers First? 用于交通预测的时空图卷积网络:空间层优先还是时间层优先?

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484207

Yuen Hoi Lau, R. C. Wong

{"title":"Spatio-Temporal Graph Convolutional Networks for Traffic Forecasting: Spatial Layers First or Temporal Layers First?","authors":"Yuen Hoi Lau, R. C. Wong","doi":"10.1145/3474717.3484207","DOIUrl":"https://doi.org/10.1145/3474717.3484207","url":null,"abstract":"Traffic forecasting is an important and challenging problem for intelligent transportation systems due to the complex spatial dependencies among neighboring roads and changing road conditions in different time periods. Spatio-temporal graph convolutional networks (STGCNs) are usually adopted to forecast traffic features in a road network. Some STGCN models involves spatial layers first and then temporal layers and some other models involves these layers in a reverse order. This creates an interesting research question on whether the ordering of the spatial layers (or temporal layers) first in an existing STGCN model could improve the forecasting performance. To the best of our knowledge, we are the first to study this interesting research problem, which creates a deep insight as a guideline to the research community on how to design STGCN models. We conducted extensive experiments to study a number of representative STCGN models for this research problem. We found that these models with spatial layers constructed before temporal layers has a higher chance to outperform that with temporal layers constructed first, which suggests the future design principle of STGCN models.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"32 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":"124015465","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