Spatial Data and Intelligence

5th China Conference, SpatialDI 2024, Nanjing, China, April 25¿27, 2024, Proceedings
Herausgegeben:Meng, Xiaofeng; Zhang, Xueying; Guo, Danhuai; Hu, Di; Zheng, Bolong; Zhang, Chunju

• Broschiertes Buch

Produktbeschreibung

This book constitutes the refereed post proceedings of the 5th China Conference on Spatial Data and Intelligence, SpatialDI 2024, held in Nanjing, China, during April 25-27, 2024.

The 25 full papers included in this book were carefully reviewed and selected from 95 submissions. They were organized in topical sections as follows: Spatiotemporal Data Analysis, Spatiotemporal Data Mining, Spatiotemporal Data Prediction, Remote Sensing Data Classification and Applications of Spatiotemporal Data Mining.

Produktdetails

• Lecture Notes in Computer Science 14619
• Verlag: Springer / Springer Nature Singapore / Springer, Berlin
• Artikelnr. des Verlages: 978-981-97-2965-4
• 2024
• Seitenzahl: 372
• Erscheinungstermin: 1. Mai 2024
• Englisch
• Abmessung: 235mm x 155mm x 21mm
• Gewicht: 564g
• ISBN-13: 9789819729654
• ISBN-10: 9819729653
• Artikelnr.: 70288761

Inhaltsangabe

.- 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.