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Biblioteca

MARC Lista (tabellare)
Proceedings of the 8th ACM SIGSPATIAL International Workshop on Security Response using GIS / / Edited by Yan Huang, Jean-Claude Thill, Hui Zhang, Danhuai Guo, Yi Liu, Wei Xu, Bin Chen
Proceedings of the 8th ACM SIGSPATIAL International Workshop on Security Response using GIS / / Edited by Yan Huang, Jean-Claude Thill, Hui Zhang, Danhuai Guo, Yi Liu, Wei Xu, Bin Chen
Pubbl/distr/stampa Association for Computing Machinery, 2023
Descrizione fisica 1 online resource (59 p.;)
Altri autori (Persone) HuangYan
ThillJean-Claude
ZhangHui
GuoDanhuai
LiuYi
XuWei
ChenBin
Collana ACM Conferences
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione |||
Altri titoli varianti EM-GIS '23
Record Nr. UNISA-996566865403316
Association for Computing Machinery, 2023
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Proceedings of the 8th ACM SIGSPATIAL International Workshop on Security Response using GIS / / Edited by Yan Huang, Jean-Claude Thill, Hui Zhang, Danhuai Guo, Yi Liu, Wei Xu, Bin Chen
Proceedings of the 8th ACM SIGSPATIAL International Workshop on Security Response using GIS / / Edited by Yan Huang, Jean-Claude Thill, Hui Zhang, Danhuai Guo, Yi Liu, Wei Xu, Bin Chen
Pubbl/distr/stampa Association for Computing Machinery, 2023
Descrizione fisica 1 online resource (59 p.;)
Altri autori (Persone) HuangYan
ThillJean-Claude
ZhangHui
GuoDanhuai
LiuYi
XuWei
ChenBin
Collana ACM Conferences
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione |||
Altri titoli varianti EM-GIS '23
Record Nr. UNINA-9910765624303321
Association for Computing Machinery, 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Spatial Data and Intelligence : 5th China Conference, SpatialDI 2024, Nanjing, China, April 25-27, 2024, Proceedings
Spatial Data and Intelligence : 5th China Conference, SpatialDI 2024, Nanjing, China, April 25-27, 2024, Proceedings
Autore Meng Xiaofeng
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (364 pages)
Altri autori (Persone) ZhangXueying
GuoDanhuai
HuDi
ZhengBolong
ZhangChunju
Collana Lecture Notes in Computer Science Series
ISBN 981-9729-66-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Spatiotemporal Data Analysis -- Multi-view Contrastive Clustering with Clustering Guidance and Adaptive Auto-encoders -- 1 Introduction -- 2 Related Work -- 2.1 Multi-view Clustering -- 2.2 Contrastive Learning -- 3 The Framework of MAAC Network -- 3.1 Adaptive Graph Auto-encoder -- 3.2 Contrastive Fusion -- 3.3 Graph Clustering -- 3.4 Clustering Guidance Strategy -- 4 Experiments -- 4.1 Datasets and Evaluation Measure -- 4.2 Implementation Details -- 4.3 Baselines -- 4.4 Results -- 4.5 Ablation Experiment -- 4.6 Cluster Visualization -- 5 Conclusion -- References -- Cloud-Edge Collaborative Continual Adaptation for ITS Object Detection -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Definition -- 3.2 Problem Statement -- 4 The Framework for Continual Adaptation in Traffic Object Detection Based on Cloud-Edge Collaboration -- 4.1 Cloud Component: Dynamic Domain Adaptation -- 4.2 Edge Component: Efficient Model Synchronization -- 5 Experiments -- 5.1 Datasets -- 5.2 Experimental Settings -- 5.3 Methods for Comparison -- 5.4 Experimental Results -- 6 Conclusion -- References -- Understanding Spatial Dependency Among Spatial Interactions -- 1 Introduction -- 2 Methodological Framework -- 2.1 Spatial Dependency Metrics for Spatial Interactions -- 2.2 Factors Influencing Second-Order Spatial Autocorrelation -- 2.3 Spatial Econometric Interaction Modeling -- 3 Experiments and Results -- 3.1 Research Area and Data -- 3.2 Measuring Spatial Dependency Among Spatial Interactions -- 3.3 Exploring Factors Influencing Second-Order Spatial Autocorrelation -- 3.4 Modeling Spatial Interactions Incorporating Spatial Dependency -- 4 Conclusions -- References -- An Improved DBSCAN Clustering Method for AIS Trajectories Incorporating DP Compression and Discrete Fréchet Distance -- 1 Introduction.
2 Related Works -- 3 Methodology -- 3.1 Framework of the Research -- 3.2 Trajectory Data Pre-processing -- 3.3 Computation of Distance Matrix -- 3.4 Unsupervised KNN + Kneed -- 4 Experiments -- 4.1 Dataset and Experimental Environment -- 4.2 Data Pre-processing -- 4.3 Comparison of Similarity Measures -- 4.4 Description of Clusters -- 5 Conclusions -- References -- Structure and Semantic Contrastive Learning for Nodes Clustering in Heterogeneous Information Networks -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Adjacency Matrix Reconstruction -- 3.2 Cleaning Feature -- 3.3 Contrastive Learning -- 3.4 Objective Function -- 4 Experiments -- 4.1 Datasets and Baselines -- 4.2 Comparison Methods -- 4.3 Ablation Experiments -- 4.4 Parameter Analysis -- 5 Conclusion -- References -- An Accuracy Evaluation Method for Multi-source Data Based on Hexagonal Global Discrete Grids -- 1 Introductory -- 2 Multi-source Data Grids -- 2.1 Selection of Grid Levels -- 2.2 Gridded Representation of Vector Data -- 2.3 Gridded Representation of Remotely Sensed Data -- 3 Accuracy Evaluation System for Gridded Data -- 3.1 Accuracy Evaluation Index System for Vector Data Gridding -- 3.2 Accuracy Evaluation Index System of Remote Sensing Data Gridding -- 4 Experimental Results and Analysis -- 4.1 Conversion Results and Uncertainty Assessment of Remotely Sensed Data -- 4.2 Transformation and Uncertainty Assessment of Vector Data -- 5 Summarize -- References -- Applying Segment Anything Model to Ground-Based Video Surveillance for Identifying Aquatic Plant -- 1 Introduction -- 2 Methodology -- 2.1 Overview -- 2.2 Detection and Segmentation -- 2.3 Area Estimation -- 3 Study Area and Data -- 4 Experiment -- 4.1 Computational Environment -- 4.2 Detection and Segmentation -- 4.3 Area Estimation -- 5 Conclusion -- References -- Spatiotemporal Data Mining.
Mining Regional High Utility Co-location Pattern -- 1 Introduction -- 2 Related Work -- 3 Related Concepts and Problem Statement -- 4 Algorithm -- 5 Experiments -- 5.1 Analysis of Mining Results -- 5.2 Efficiency Analysis -- 6 Conclusion -- References -- Local Co-location Pattern Mining Based on Regional Embedding -- 1 Introduction -- 2 Related Works -- 3 Basic Concepts -- 4 Mining Framework -- 4.1 Regional Embedding -- 4.2 Region Functional Annotation -- 4.3 Mining Semantic LCPs -- 4.4 Algorithm Analysis -- 5 Experimental Evaluation -- 5.1 Description of Datasets -- 5.2 Region Partitioning and Function Annotation Based on Regional Embedding -- 5.3 Case Study of Semantic LCP Mining -- 6 Conclusion -- References -- RCPMRLM: A Regional Co-location Pattern Mining Method Based on Representation Learning Model -- 1 Introduction -- 2 Related Concepts -- 2.1 Regional Co-location Patterns Mining -- 2.2 Word Embeddings Representation Model -- 2.3 Similarity Measurement -- 2.4 Clustering Method -- 3 Study Area and Data -- 4 Regional Co-location Pattern Mining Method Based on Representation Learning Model (RCPMRLM) -- 5 Algorithm Complexity Analysis -- 6 Results -- 6.1 Clustering Results -- 6.2 RCPMRLM Mining Results -- 7 Summary -- References -- Construction of a Large-Scale Maritime Elements Semantic Schema Based on Heterogeneous Graph Models -- 1 Introduction -- 2 Related Work -- 2.1 Overview of Related Work on Knowledge Graphs -- 2.2 Overview of Related Work on Ship Behavior Mining -- 3 Technical Framework and Dataset -- 3.1 Technical Framework -- 3.2 Dataset -- 3.3 Introduction to Application Scenarios of the Framework -- 4 Experimental Results and Analysis -- 4.1 Ship Type Prediction -- 4.2 Similar Berth Recommendation -- 5 Conclusion and Future Work -- References.
OCGATL: One-Class Graph Attention Networks with Transformation Learning for Anomaly Detection for Argo Data -- 1 Introduction -- 2 Related Work -- 2.1 Graph Anomaly Detection -- 2.2 One-Class Classification -- 2.3 Contrastive Learning -- 3 Preliminaries -- 4 Methodology -- 4.1 Graph Construction -- 4.2 OCGATL Model -- 5 Experiments -- 5.1 Simulation -- 5.2 Argo Real Data Experiment -- 6 Conclusion -- References -- RGCNdist2vec: Using Graph Convolutional Networks and Distance2Vector to Estimate Shortest Path Distance Along Road Networks -- 1 Introduction -- 2 Related Work -- 2.1 Shortest Path Distance Calculation -- 2.2 Graph Neural Network -- 3 Definitions and Solutions -- 3.1 Definition -- 3.2 Solution -- 4 Road Network Shortest Path Estimation Method -- 4.1 RGCNdist2vec -- 4.2 RGCNdist2vec-Encoder -- 4.3 RGCNdist2vec-Decoder -- 5 Sampling Method -- 5.1 Subgraph Sampling Method -- 5.2 Sampling Method Between Subgraphs -- 5.3 Whole Graph Sampling Method -- 6 Experiment -- 6.1 Experimental Introduction -- 6.2 Ablation Experiment -- 6.3 Effect Experiment -- 6.4 Efficiency Experiment -- 7 Conclusion -- References -- Self-supervised Graph Neural Network Based Community Search over Heterogeneous Information Networks -- 1 Introduction -- 2 Related Work -- 3 Community Search Model and Algorithm over HIN -- 3.1 Node Attribute Score Calculation -- 3.2 Community Search -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Effectiveness Evaluation -- 4.3 Ablation Study -- 4.4 Community Sensitivity Analysis -- 5 Conclusion -- References -- Measurement and Research on the Conflict Between Residential Space and Tourism Space in Pianyan Ancient Township -- 1 Introduction -- 2 Overview of the Study Area and Data Sources -- 2.1 Overview of the Study Area -- 2.2 Data Sources -- 3 Multi-intelligence Body Simulation -- 3.1 Environmental Information Module.
3.2 Implementation Module -- 3.3 Information Storage Module -- 3.4 Module for Analyzing Simulation Results -- 4 System Implementation and Simulation Experiments -- 4.1 Spatial Form of the Ancient Town of Pianyan -- 4.2 Spatial Conflict Identification -- 4.3 Optimization Strategies -- 5 Conclusion -- References -- Spatiotemporal Data Prediction -- Spatio-Temporal Sequence Prediction of Diversion Tunnel Based on Machine Learning Multivariate Data Fusion -- 1 Introduction -- 2 Methodology -- 2.1 ARIMA Model -- 2.2 Order of ARIMA Model -- 2.3 The LSSVM Model -- 2.4 The DLSSVM Model -- 2.5 DLSSVM Kernel Function -- 2.6 ARIMA-Bi-DLSSVM Modeling -- 3 Real Experiment for Monitoring Data -- 3.1 Monitoring Data -- 3.2 Data Processing -- 4 Results and Analysis -- 4.1 Prediction Effect of ARIMA-b-DLSSVM -- 4.2 Comparison of Prediction Effect of Several Models -- 5 Conclusion -- References -- DyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Transformer-Based Traffic Forecasting -- 2.2 Adaptive GNN-Based Traffic Forecasting -- 3 Preliminaries -- 3.1 Definitions -- 3.2 Problem Formulation -- 4 The Proposed Model -- 5 S-TPE: Spatial-Temporal Position Embedding -- 5.1 Temporal Position Embedding (TPE) -- 5.2 Spatial Position Embedding (SPE) Based on Random Walk -- 6 DASCL: Dynamic Adaptive Spatial-Temporal Correlations Learning -- 6.1 Encoder -- 6.2 Decoder -- 7 Experimental Analysis -- 7.1 Datasets -- 7.2 Baseline Methods -- 7.3 Experimental Settings -- 7.4 Experimental Results and Analysis -- 7.5 Interpretability Analysis -- 8 Conclusion -- References -- Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Forward Propagation of the CGATM -- 3.2 Optimization of the CGATM.
4 Experimental Results and Analysis.
Record Nr. UNISA-996594168003316
Meng Xiaofeng  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Spatial Data and Intelligence : 5th China Conference, SpatialDI 2024, Nanjing, China, April 25–27, 2024, Proceedings / / edited by Xiaofeng Meng, Xueying Zhang, Danhuai Guo, Di Hu, Bolong Zheng, Chunju Zhang
Spatial Data and Intelligence : 5th China Conference, SpatialDI 2024, Nanjing, China, April 25–27, 2024, Proceedings / / edited by Xiaofeng Meng, Xueying Zhang, Danhuai Guo, Di Hu, Bolong Zheng, Chunju Zhang
Autore Meng Xiaofeng
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (364 pages)
Disciplina 005.3
Altri autori (Persone) ZhangXueying
GuoDanhuai
HuDi
ZhengBolong
ZhangChunju
Collana Lecture Notes in Computer Science
Soggetto topico Application software
Computer networks
Electronic digital computers - Evaluation
Computer systems
Information storage and retrieval systems
Computer and Information Systems Applications
Computer Communication Networks
System Performance and Evaluation
Computer System Implementation
Information Storage and Retrieval
ISBN 9789819729661
9819729661
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- Spatiotemporal Data Analysis. -- Multi-view Contrastive Clustering with Clustering Guidance and Adaptive Auto-en-coders. -- Cloud-Edge Collaborative Continual Adaptation for ITS Object Detection. -- Understanding Spatial Dependency among Spatial Interactions. -- An Improved DBSCAN Clustering Method for AIS Trajectories Incorporating DP Compression and Discrete Fréchet Distance. -- Structure and Semantic Contrastive Learning for Nodes Clustering in Heterogeneous Information Networks. -- Accuracy Evaluation Method for Vector Data Based on Hexagonal Discrete Global Grid. -- Applying Segment Anything Model to Ground-Based Video Surveillance for Identify-ing Aquatic Plant. -- Spatiotemporal Data Mining. -- Mining Regional High Utility Co-location Pattern. -- Local Co-location Pattern Mining Based on Regional Embedding. -- RCPM_RLM: A Regional Co-location Pattern Mining Method Based on Representa-tion Learning Model. -- Construction of a Large-Scale Maritime Elements Semantic Schema Based on Hetero-geneous Graph Models. -- OCGATL: One-Class Graph Attention Networks with Transformation Learning for Anomaly Detection For Argo Data. -- RGCNdist2vec: Using Graph Convolutional Networks and Distance2Vector to Esti-mate Shortest Path Distance along Road Networks. -- Self-supervised Graph Neural Network based Community Search over Heterogeneous Information Networks. -- Measurement and Research on the Conflict between Residential Space and Tourism Space in Pianyan Ancient Township. -- Spatiotemporal Data Prediction. -- Spatio-Temporal Sequence Prediction Of Diversion Tunnel Based On Machine Learn-ing Multivariate Data Fusion. -- DyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction. -- Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model. -- Remote Sensing Data Classification. -- MADB-RemdNet for Few-Shot Learning in Remote Sensing Classification. -- Convolutional Neural Network Based on Multiple Attention Mechanisms for Hyper-spectral and LiDAR Classification. -- Few-shot Learning Remote Scene Classification Based On DC-2DEC. -- Applications of Spatiotemporal Data Mining. -- Neural HD Map Generation from Multiple Vectorized Tiles Locally Produced by Au-tonomous Vehicles. -- Trajectory Data Semi-fragile Watermarking Algorithm Considering Spatiotemporal Features. -- HPO-LGBM-DRI: Dynamic Recognition Interval Estimation for Imbalanced Fraud Call via HPO-LGBM. -- A Review on Urban Modelling for Future Smart Cities.
Record Nr. UNINA-9910855382803321
Meng Xiaofeng  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui