The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation"

A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation<br>This is the implementation for the paper "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation".The framework is Learning-based Computing Framework for Geospatial data(LCF-G).Prediction, ParallelComputation and SampleGeneration.This paper includes three case studies, each corresponding to a folder. Each folder contains four subfolders: data, CIThe <b>data</b> folder contains geospatail data.The <b>CIPrediction</b> folder contains model training code.The <b>ParallelComputation</b> folder contains geographic computation code.The <b>SampleGeneration </b>folder contains code for sample generation.case: Generation of DEM from point cloud datastep 1: Data downloadDataset 1 has been uploaded to the directory <i>1point2dem/data</i>. The other two datasets, <b>Dataset 2</b> and <b>Dataset 3</b>, can be downloaded from the following website:OpenTopography: https://opentopography.org/Below are the steps for downloading <b>Dataset 2</b> and <b>Dataset 3</b>, along with the query parameters:Dataset 2:<b>Visit OpenTopography Website</b>:Go to Dataset 2 Download Link.https://portal.opentopography.org/lidarDataset?opentopoID=OTLAS.112018.2193.1<b>Coordinates &amp; Classification</b>:In the section "1. Coordinates &amp; Classification", select the option <b>"Manually enter selection coordinates"</b>.<b>Set the coordinates as follows</b>: <b>Xmin</b> = 1372495.692761,<b>Ymin</b> = 5076006.86821,<b>Xmax</b> = 1378779.529766,<b>Ymax</b> = 5085586.39531<b>Point Cloud Data Download</b>: