Titlebook: Spatial Data and Intelligence; 5th China Conference Xiaofeng Meng,Xueying Zhang,Chunju Zhang Conference proceedings 2024 The Editor(s) (if

MUTE

Structure and Semantic Contrastive Learning for Nodes Clustering in Heterogeneous Information Networetapaths. Some scholars have adopted contrastive learning methods on the basis of deep clustering, which has achieved promising clustering performance. Despite this, few of them pay attention to redundant information in features, while also not considering both the semantics and structure of the nod

ZEST

An Accuracy Evaluation Method for Multi-source Data Based on Hexagonal Global Discrete Gridshich can be used for efficient storage and application of large-scale global spatial data, and it is a digital multi-resolution geo-reference model, which helps to establish a new data model and is expected to make up for the deficiencies of the existing spatial data in the aspects of organization,

眼界

Applying Segment Anything Model to Ground-Based Video Surveillance for Identifying Aquatic Plantlite remote sensing, while effective for large-scale monitoring, incurs high costs and limited applicability for localized surveillance. Unmanned aerial vehicle (UAV) offers higher spatial resolution but is hampered by operational complexity, deployment costs, and weather-dependent limitations, prev

MARS

Mining Regional High Utility Co-location Patternve different distributions and different values. However, existing methods for mining pattern ignore these differences. In this paper, we propose a novel method for mining regional high utility co-location pattern by considering both instance distribution and value. First, local regions are obtained

强有力

Local Co-location Pattern Mining Based on Regional Embeddingin LCP mining. Existing regional partitioning methods may ignore potential LCPs due to subjective elements. Additionally, with the diversity of geographic data increases, previous mining techniques disregarded the semantic information within the data, and limited the interpretability of local region

注入

RCPM_RLM: A Regional Co-location Pattern Mining Method Based on Representation Learning Modelat can only appear in specific local areas. Regional co-location pattern mining (RCPM) is designed to discover co-location patterns like these. The regional co-location patterns can reveal the association relationships among spatial features in the local regions. However, most studies only divide th

HEAVY

Construction of a Large-Scale Maritime Elements Semantic Schema Based on Heterogeneous Graph Modelstterns and deep behavioral characteristics of vessels from vast amounts of shipping statistics. Additionally, aligning these characteristics with infrastructure such as berths for effective association and recommendation to vessels is a critical requirement for the evolution of intelligent maritime

antipsychotic

OCGATL: One-Class Graph Attention Networks with Transformation Learning for Anomaly Detection for Artant step in ocean science big data. However, in classical anomaly algorithms, Argo anomaly detection mostly has low accuracy, poor efficiency, and neglects the spatial continuity of Argo data. In the research on anomaly detection of spatial and regional data, graph anomaly detection has achieved ex

发炎

RGCNdist2vec: Using Graph Convolutional Networks and Distance2Vector to Estimate Shortest Path Distance estimation methods either have a long training time or the model training time is reduced by sacrificing the estimation accuracy. To address the above problems, this paper proposes a Road Graph Convolutional Networks and Distance2Vector (RGCNdist2vec), which is suitable for road network scenario

戏服