Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction

• URL: http://arxiv.org/abs/2407.00748v1
• Date: Sun, 30 Jun 2024 16:13:13 GMT
• Title: Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction
• Authors: Dazhou Yu, Xiaoyun Gong, Yun Li, Meikang Qiu, Liang Zhao,
• Abstract summary: Multi-source spatial point data prediction is crucial in fields like environmental monitoring and natural resource management. Existing models in this area often fall short due to their domain-specific nature and lack a strategy for integrating information from various sources. We introduce an innovative multi-source spatial point data prediction framework that adeptly aligns information from varied sources without relying on ground truth labels.
• Score: 10.646376827353551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-source spatial point data prediction is crucial in fields like environmental monitoring and natural resource management, where integrating data from various sensors is the key to achieving a holistic environmental understanding. Existing models in this area often fall short due to their domain-specific nature and lack a strategy for integrating information from various sources in the absence of ground truth labels. Key challenges include evaluating the quality of different data sources and modeling spatial relationships among them effectively. Addressing these issues, we introduce an innovative multi-source spatial point data prediction framework that adeptly aligns information from varied sources without relying on ground truth labels. A unique aspect of our method is the 'fidelity score,' a quantitative measure for evaluating the reliability of each data source. Furthermore, we develop a geo-location-aware graph neural network tailored to accurately depict spatial relationships between data points. Our framework has been rigorously tested on two real-world datasets and one synthetic dataset. The results consistently demonstrate its superior performance over existing state-of-the-art methods.

Related papers

• Tabular Data Synthesis with Differential Privacy: A Survey [24.500349285858597]
  Data sharing is a prerequisite for collaborative innovation, enabling organizations to leverage diverse datasets for deeper insights. Data synthesis tackles this by generating artificial datasets that preserve the statistical characteristics of real data. Differentially private data synthesis has emerged as a promising approach to privacy-aware data sharing.
  arXiv  Detail & Related papers  (2024-11-04T06:32:48Z)
• Enhancing Ecological Monitoring with Multi-Objective Optimization: A Novel Dataset and Methodology for Segmentation Algorithms [17.802456388479616]
  We introduce a unique semantic segmentation dataset of 6,096 high-resolution aerial images capturing indigenous and invasive grass species in Bega Valley, New South Wales, Australia. This dataset presents a challenging task due to the overlap and distribution of grass species. The dataset and code will be made publicly available, aiming to drive research in computer vision, machine learning, and ecological studies.
  arXiv  Detail & Related papers  (2024-07-25T18:27:27Z)
• CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT [16.821900475733102]
  Foundation models (FMs) can harness distributed and diverse environmental data by leveraging prior knowledge. We introduce CoRAST, a novel learning framework that utilizes FMs for enhanced analysis of distributed, correlated heterogeneous data. Our evaluation on real-world weather dataset demonstrates CoRAST's ability to exploit correlated heterogeneous data.
  arXiv  Detail & Related papers  (2024-03-27T11:11:06Z)
• SpaCE: The Spatial Confounding Environment [2.572906392867547]
  SpaCE provides realistic benchmark datasets and tools for evaluating causal inference methods. Each dataset includes training data, true counterfactuals, a spatial graph with coordinates, and smoothness and confounding scores. SpaCE facilitates an automated end-to-end pipeline, simplifying data loading, experimental setup, and evaluating machine learning and causal inference models.
  arXiv  Detail & Related papers  (2023-12-01T16:42:57Z)
• UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation [59.77275587857252]
  A holistic human dataset inevitably has insufficient and low-resolution information on local parts. We propose to use multi-source datasets with various resolution images to jointly learn a high-resolution human generative model.
  arXiv  Detail & Related papers  (2023-09-25T17:58:46Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources [21.32471030724983]
  Individual-level data (microdata) that characterizes a population is essential for studying many real-world problems. In this study, we examine synthetic data generation as a tool to extrapolate difficult-to-obtain high-resolution data.
  arXiv  Detail & Related papers  (2022-12-08T01:22:12Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [78.31180235269035]
  We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM) EERM resorts to multiple context explorers that are adversarially trained to maximize the variance of risks from multiple virtual environments. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• Federated Causal Discovery [74.37739054932733]
  This paper develops a gradient-based learning framework named DAG-Shared Federated Causal Discovery (DS-FCD) It can learn the causal graph without directly touching local data and naturally handle the data heterogeneity. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method.
  arXiv  Detail & Related papers  (2021-12-07T08:04:12Z)
• Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation [63.24594955429465]
  Multi-source entity linkage is critical in high-impact applications such as data cleaning and user stitching. AdaMEL is a deep transfer learning framework that learns generic high-level knowledge to perform multi-source entity linkage. Our framework achieves state-of-the-art results with 8.21% improvement on average over methods based on supervised learning.
  arXiv  Detail & Related papers  (2021-10-27T15:20:41Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.