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

• Record Nr.: UNINA9910855382803321
• Autore: Meng Xiaofeng
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
• ISBN: 981-9729-66-1
• Edizione: [1st ed. 2024.]
• Descrizione fisica: 1 online resource (364 pages)
• Collana: Lecture Notes in Computer Science, , 1611-3349 ; ; 14619
• Altri autori (Persone): ZhangXueying; GuoDanhuai; HuDi; ZhengBolong; ZhangChunju
• Disciplina: 005.3
• Soggetti: 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
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• 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.
• Sommario/riassunto: 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.