A CNN-Based Method for Computational Intensity Prediction and Geospatial Domain Decomposition

GAO Fan; LU Wei; GAN Linlu

doi:10.13203/j.whugis20240119

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A CNN-Based Method for Computational Intensity Prediction and Geospatial Domain Decomposition

Cartography and GIS | 更新时间：2026-11-05

- A CNN-Based Method for Computational Intensity Prediction and Geospatial Domain Decomposition
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 5, Pages: 1048-1058(2026)
- 作者机构：
  
  陆军工程大学通信工程学院，江苏南京，210007
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20240119
  CLC： P237
- Received：25 February 2025，
  
  Published：05 May 2026
- 稿件说明：
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高凡,路威,甘麟露.基于卷积神经网络的地理空间域计算强度预测与分解方法[J].武汉大学学报（信息科学版）,2026,51(5):1048-1058.

GAO Fan,LU Wei,GAN Linlu.A CNN-Based Method for Computational Intensity Prediction and Geospatial Domain Decomposition[J].Geomatics and Information Science of Wuhan University,2026,51(5):1048-1058.
高凡,路威,甘麟露.基于卷积神经网络的地理空间域计算强度预测与分解方法[J].武汉大学学报（信息科学版）,2026,51(5):1048-1058. DOI：10.13203/j.whugis20240119

GAO Fan,LU Wei,GAN Linlu.A CNN-Based Method for Computational Intensity Prediction and Geospatial Domain Decomposition[J].Geomatics and Information Science of Wuhan University,2026,51(5):1048-1058. DOI：10.13203/j.whugis20240119

摘要

地理空间域分解是并行地理计算中实现负载均衡的核心问题，而如何准确提取地理空间域上特征并预测域上计算强度是地理空间域均衡分解的关键。然而，在地理空间域特征提取和计算强度模型建模方面，现有方法过于依赖专家知识，存在特征适用性差、建模过程复杂、模型精度低的问题。为摆脱专家知识依赖，实现高精度、自动化地理空间域计算强度预测，提出一种基于人工智能深度学习的地理空间域计算强度预测和分解方法。所提方法从数据驱动角度出发，将地理空间域对齐卷积神经网络输入结构，利用卷积神经网络自动化捕捉地理空间域特征，预测域上计算强度，并将计算强度预测模型嵌入地理空间域分解，实现任务的均衡划分。以矢量空间相交为例，证明了所提方法在计算性能和自动化程度两个方面的优势。

Abstract

Objectives

The evaluation of heterogeneity is essential for domain decomposition in parallel geocomputation. According to the theory of geospatial domain

the assessment of heterogeneity can be transformed to the computational intensity (CI) modelling

thus the key of domain decomposition is feature extraction and computational intensity prediction. However

the existing methods rely on expert knowledge for feature extraction and CI modelling of geospatial domain

suffering from poor feature applicability

complex modelling process and low model accuracy. In order to relieve the dependence on expert knowledge and achieve accurate computational intensity prediction

a computational intensity prediction and decomposition method for geospatial domain based on artificial intelligence (AI) deep learning is proposed.

Methods

We use convolutional neural network (CNN) to capture the features of geospatial domain automatically

and a fully connected layer is used to predict the computational intensity. An added component is developed to match the geospatial domain and the input of CNN.

Results

Spatial intersection on vector data is implemented to compare the proposed method and traditional methods. The results demonstrate the advantages of the proposed method in terms of the usability and parallel performance.

Conclusions

The proposed method optimizes computational intensity prediction and domain decomposition from data science perspective

which provides a reference on how AI deep learning can be used in high-performance geocomputation.

关键词

Keywords

references

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