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E-raamat: Spatial Data and Intelligence: 6th International Conference, SpatialDI 2025, Xiamen, China, April 17, 2025, Proceedings

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Spatial Data and Intelligence: 6th International Conference, SpatialDI 2025, Xiamen, China, April 17, 2025, ProceedingsSuurem pilt
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This book constitutes the refereed post proceedings of the 6th International Conference on Spatial Data and Intelligence, SpatialDI 2025, held in Xiamen, China, during April 17–19, 2025.

The 17 full papers were carefully reviewed and selected from 100 submissions. The conference focuses on generative AI and spatial data intelligence, spatiotemporal knowledge graphs and large geographic models, digital twins and smart cities, government spatiotemporal big data and data governance, emergency disaster reduction and sustainable development, spatial humanities and social geography computing, spatiotemporal data management and analysis, and intelligent processing of remote sensing images.

.- BuildingView: Constructing Urban Building Exteriors Databases with
Street View Imagery and Multimodal Large Language Model.


.- Urban fire risk prediction and spatiotemporal analysis based on
machine learning.


.- Spatio-Temporal Diffusion Attention Networks for Vessel Flow Prediction.


.- Study on pollutants and greenhouse gases emission inventory making
and emission prediction of Tianjin Port.


.- Automatic Landslide Identifification Based on High- Resolution
Remote Sensing Images Using LightweightDeep Learning Network.


.- LCformer: Enhancing Multivariate Time Series Forecasting with
Transformer Based on Lagged Correlations.


.- SignalingTraj: A Signaling Data based Trajectory Generation with
Diffusion Model.


.- Research on Estimation Time of Arrival in Marine Traffic based on
Large Language Model.


.- HTDiff: Self-Guiding Diffusion Models for Hand Trajectory Prediction.


.- A Method for Ship Trajectory Repair Based on Feature Correlation and SHAP
Model Interpretability.


.- A Maritime Route Prediction Method for Large Oil Tankers Based on IMO-MMSI
Matching and EncoderLSTM Model.


.- Learning Sequential Features of Check-ins for User Relationship
Inference.


.- Spatial Optimization of Fire Stations in Beijing Based on
Multi-factor Fire Risk Analysis and Covering Problem Model.


.- A Location Label Optimization Method for Crowdsourcing Trajectory Data.


.- Leveraging Data Augmentation through Contrastive Self-Supervised
Learning for Next Point-of-Interest Recommendation.


.- Deductive Inference of How Urbanization Shaped by Governmental Policy in
Beijing from 2005 to
2022.


.- LERI Evaluation and Driving Mechanism Analysis via GWRF Model.
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