Proceedings of the 29th International Conference on Advances in Geographic Information Systems最新文献_第5页

Spatial-Net: A Self-Adaptive and Model-Agnostic Deep Learning Framework for Spatially Heterogeneous Datasets 空间网络:空间异构数据集的自适应和模型不可知深度学习框架

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483970

Yiqun Xie, X. Jia, Han Bao, Xun Zhou, Jia Yu, Rahul Ghosh, Praveen Ravirathinam

{"title":"Spatial-Net: A Self-Adaptive and Model-Agnostic Deep Learning Framework for Spatially Heterogeneous Datasets","authors":"Yiqun Xie, X. Jia, Han Bao, Xun Zhou, Jia Yu, Rahul Ghosh, Praveen Ravirathinam","doi":"10.1145/3474717.3483970","DOIUrl":"https://doi.org/10.1145/3474717.3483970","url":null,"abstract":"Knowledge discovery from spatial data is essential for many important societal applications including crop monitoring, solar energy estimation, traffic prediction and public health. This paper aims to tackle a key challenge posed by spatial data - the intrinsic spatial heterogeneity commonly embedded in their generation processes - in the context of deep learning. In related work, the early rise of convolutional neural networks showed the promising value of explicit spatial-awareness in deep architectures (i.e., preservation of spatial structure among input cells and the use of local connection). However, the issue of spatial heterogeneity has not been sufficiently explored. While recent developments have tried to incorporate awareness of spatial variability (e.g., SVANN), these methods either rely on manually-defined space partitioning or only support very limited partitions (e.g., two) due to reduction of training data. To address these limitations, we propose a Spatial-Net to simultaneously learn a space-partitioning scheme and a deep network architecture with a Significance-based Grow-and-Collapse (SIG-GAC) framework. SIG-GAC allows collaborative training between partitions and uses an exponential reduction tree to control the network size. Experiments using real-world datasets show that Spatial-Net can automatically learn the pattern underlying heterogeneous spatial process and greatly improve model performance.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"1 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":"127531128","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: 9

Learning Functional Properties of Rooms in Indoor Space from Point Cloud Data: A Deep Learning Approach 从点云数据中学习室内空间中房间的功能属性:一种深度学习方法

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483974

Guoray Cai, Yimu Pan

{"title":"Learning Functional Properties of Rooms in Indoor Space from Point Cloud Data: A Deep Learning Approach","authors":"Guoray Cai, Yimu Pan","doi":"10.1145/3474717.3483974","DOIUrl":"https://doi.org/10.1145/3474717.3483974","url":null,"abstract":"This paper presents a method to derive functional labels of rooms from the spatial configuration of room objects detected from 3D point clouds representation. The method was inspired by the intuition that spatial configuration of room objects has intimate link with the intended functional purposes. To explore the possibility of inferring the room usage information from its spatial configuration, we designed and trained a deep learning model to learn the important features of spatial configuration of room scenes and examined the predictive power of the model in inferring room usage. We present an experiment on using the model to to predict room function category on Standford 3D (S3DIS) dataset, and achieved reasonable performance. Analysis of accuracy and confusion rates allows us to draw a number insight on the separability of rooms among top level categories (such as offices, conference rooms, lounge, hallways, and storage rooms). Our findings suggested that our method is promising, with an accuracy of 91.8% on predicting room function categories. Future work should further validate and refine our method using data with more balanced training samples on the range of room types as they become available.","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":"114717916","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

Efficient topology-aware simplification of large triangulated terrains 大型三角地形的高效拓扑感知简化

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3484261

Yunting Song, Riccardo Fellegara, F. Iuricich, L. Floriani

{"title":"Efficient topology-aware simplification of large triangulated terrains","authors":"Yunting Song, Riccardo Fellegara, F. Iuricich, L. Floriani","doi":"10.1145/3474717.3484261","DOIUrl":"https://doi.org/10.1145/3474717.3484261","url":null,"abstract":"A common first step in the terrain processing pipeline of large Triangulated Irregular Networks (TINs) is simplifying the TIN to make it manageable for further processing. The major problem with TIN simplification algorithms is that they create or remove critical points in an uncontrolled way. Topology-aware operators have been defined to solve this issue by coarsening a TIN without affecting the topology of its underlying terrain, i.e., without modifying critical simplices describing pits, saddles, peaks, and their connectivity. While effective, existing algorithms are sequential in nature and are not scalable enough to perform well with large terrains on multicore systems. Here, we consider the problem of topology-aware simplification of very large meshes. We define a topology-aware simplification algorithm on a compact and distributed data structure for triangle meshes, namely the Terrain trees. Terrain trees reduce both the memory and time requirements of the simplification procedure by adopting a batched processing strategy of the mesh elements. Furthermore, we define a new parallel topology-aware simplification algorithm that takes advantage of the spatial domain decomposition at the basis of Terrain trees. Scalability and efficiency are experimentally demonstrated on real-world TINs originated from topographic and bathymetric LiDAR data. Our experiments show that topology-aware simplification on Terrain trees uses 40% less memory and half the time than the same approach implemented on the most compact and efficient connectivity-based data structure for TINs. Beyond that, our parallel algorithm on the Terrain trees reaches a 12x speedup when using 20 threads.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"47 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":"121815378","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

GEM 宝石

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1007/978-3-540-72816-0_9181

Setu Shah, Venkata Vamsikrishna Meduri, Mohamed Sarwat

引用次数: 0

Barrier-Free Pedestrian Routing with Contraction Hierarchies 具有收缩层次结构的无障碍行人路由

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3486797

Ruoying Li, Sabine Storandt, Uli Müller, David Weber

{"title":"Barrier-Free Pedestrian Routing with Contraction Hierarchies","authors":"Ruoying Li, Sabine Storandt, Uli Müller, David Weber","doi":"10.1145/3474717.3486797","DOIUrl":"https://doi.org/10.1145/3474717.3486797","url":null,"abstract":"We present a holistic approach for pedestrian routing that allows computing shortest paths that may have indoor and outdoor sections. Such routes arise, for example, when the destination is not just an address but a specific store in a large mall, or when one needs to get to a certain track at a large train station. Currently, map services as Google Maps or OSRM do not offer such functionality. We identify and overcome three main challenges for answering such complex route planning queries: (i) Pedestrian routing requires fine-grained data, as the location of stairs and elevators, building dventrances, building footprints, and elevation/level information. A single missing staircase can change the length of the computed path severely. (ii) Indoor routing has to be integrated carefully into classical path planning to allow the computation of sensible routes that may enter and exit buildings. (iii) Given the large amount of data to be considered in a query, acceleration techniques need to be applied in order to achieve interactive query times. Retrieving barrier-free routes for wheelchairs is also our important use case.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"64 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":"116608300","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

Matching Buildings Between 2.5D Maps and Dash Cam Images for Building Identification 基于2.5D地图和行车记录仪图像的建筑物识别匹配

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483966

Yukinari Awano, T. Nishimura

{"title":"Matching Buildings Between 2.5D Maps and Dash Cam Images for Building Identification","authors":"Yukinari Awano, T. Nishimura","doi":"10.1145/3474717.3483966","DOIUrl":"https://doi.org/10.1145/3474717.3483966","url":null,"abstract":"Technology that matches buildings between dash cam images and digital maps (GPS) assists in the identification of buildings. The identification enables to collect city information as well as the textures for 3D building models. However, the matching technology has two challenges. First, GPS locations can be highly inaccurate in cities that have tall buildings, which means the GPS sometimes needs to be relocated to capture the correct building features for matching. Second, it can be tricky to set the position and orientation of the camera by making correspondence of certain features of buildings in images and maps. In this work, we propose a method of matching buildings in dash cam images and 2.5D maps that uses the height information of the buildings equivalent to the LoD1 in the CityGML format. For the first challenge, we relocated GPS locations by using a map-matching method. For the second challenge, we adjust positions and orientations by matching the building edges in the images and maps after extracting the edges with a deep-learning-based detection model. Tests using real-world datasets demonstrated that our proposed method matched the buildings better than the baseline.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"1 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":"115676360","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

CSD-CMAD: Coupling Similarity and Diversity for Clustering Multivariate Astrophysics Data 多变量天体物理数据聚类的耦合相似性和多样性

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483989

Xu Teng, Thomas Beckler, Bradley Gannon, Benjamin Huinker, Gabriel Huinker, Koushhik Kumar, Christina Marquez, Jacob Spooner, Goce Trajcevski, Prabin Giri, A. Dotter, J. Andrews, S. Coughlin, Y. Qin, J. G. Serra-Perez, N. Tran, Jaime Roman-Garja, K. Kovlakas, E. Zapartas, S. Bavera, D. Misra, T. Fragos

{"title":"CSD-CMAD: Coupling Similarity and Diversity for Clustering Multivariate Astrophysics Data","authors":"Xu Teng, Thomas Beckler, Bradley Gannon, Benjamin Huinker, Gabriel Huinker, Koushhik Kumar, Christina Marquez, Jacob Spooner, Goce Trajcevski, Prabin Giri, A. Dotter, J. Andrews, S. Coughlin, Y. Qin, J. G. Serra-Perez, N. Tran, Jaime Roman-Garja, K. Kovlakas, E. Zapartas, S. Bavera, D. Misra, T. Fragos","doi":"10.1145/3474717.3483989","DOIUrl":"https://doi.org/10.1145/3474717.3483989","url":null,"abstract":"Traditionally, clustering of multivariate data aims at grouping objects described with multiple heterogeneous attributes based on a suitable similarity (conversely, distance) function. One of the main challenges is due to the fact that it is not straightforward to directly apply mathematical operations (e.g., sum, average) to the feature values, as they stem from heterogeneous contexts. In this work we take the challenge a step further and tackle the problem of clustering multivariate datasets based on jointly considering: (a) similarity among a subset of the attributes; and (b) distance-based diversity among another subset of the attributes. Specifically, we focus on astrophysics data, where the snapshots of the stellar evolution for different stars contain over 40 distinct attributes corresponding to various physical and categorical (e.g., 'black hole') attributes. We present CSD-CAMD -- a prototype system for Coupling Similarity and Diversity for Clustering Astrophysics Multivariate Datasets. It provides a flexibility for the users to select their preferred subsets of attributes; assign weight (to reflect their relative importance on the clustering); and select whether the impact should be in terms of proximity or distance. In addition, CSD-CAMD allows for selecting a clustring algorithm and enables visualization of the outcome of clustering.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"19 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":"122823087","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

Hierarchical Neural Architecture Search for Travel Time Estimation 旅行时间估计的层次神经结构搜索

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483913

G. Jin, Huan Yan, Fuxian Li, Yong Li, Jincai Huang

引用次数: 9

An acoustic and psycho-acoustic experimental setup for analysing urban soundscapes 用于分析城市声景的声学和心理声学实验装置

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483916

Mohamed Amin Hammami, Christophe Claramunt

引用次数: 0

Robust Routing Using Electrical Flows 使用电流的鲁棒路由

Proceedings of the 29th International Conference on Advances in Geographic Information Systems Pub Date : 2021-11-02 DOI: 10.1145/3474717.3483961

A. Sinop, Lisa Fawcett, Sreenivas Gollapudi, Kostas Kollias

{"title":"Robust Routing Using Electrical Flows","authors":"A. Sinop, Lisa Fawcett, Sreenivas Gollapudi, Kostas Kollias","doi":"10.1145/3474717.3483961","DOIUrl":"https://doi.org/10.1145/3474717.3483961","url":null,"abstract":"Generating alternative routes in road networks is an application of significant interest for online navigation systems. A high quality set of diverse alternate routes offers two functionalities - a) support multiple (unknown) preferences that the user may have; and b) robust to changes in network conditions. We address the latter in this paper. The main techniques that produce alternative routes in road networks are the penalty and the plateau methods, with the former providing high quality results but being too slow for practical use and the latter being fast but suffering in terms of quality. In this work we propose a novel method to produce alternative routes that is fundamentally different from the aforementioned approaches. Our algorithm borrows concepts from electrical flows and their decompositions. We evaluate our method against the penalty and plateau methods, showing that it is as fast as the plateau method while also recovering much of the headroom towards the quality of the penalty method. The metrics we use to evaluate performance include the stretch (the average cost of the routes), the diversity, and the robustness (the connectivity between the origin and destination) of the induced set of routes.","PeriodicalId":340759,"journal":{"name":"Proceedings of the 29th International Conference on Advances in Geographic Information Systems","volume":"61 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":"116985097","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: 4