<b>From street view imagery to the countryside: large-scale perception of rural China using deep learning</b>

This project includes the deep learning model code about the article "From street view imagery to the countryside: large-scale perception of rural China using deep learning," which introduces a Pair-CNN model. This model is applied to rural street view data to evaluate the subjective perceptions of rural China on five dimensions: Wealthy, Lively, Habitable, Tidy, and Terroir. The project includes both the data and code that support the Pair-CNN model.1. "data" folderPicture.zip—Rural street view imagery data, containing 100 randomly selected rural imagery in JPG format.label_fy.csv—Label data, recording volunteers' comparisons of two rural images based on the wealth metric (only includes images from Picture.zip). It has three fields: lefturl, righturl, and label. The label field is a binary classification result: 0 means the left image is better than the right image, and 1 means the opposite.2. "model" folderResNet.py—The basic structure of the model, using ResNet50ComparaNet.py—The basic structure of the Pair-CNN model3."other" folderGet_Image.py—Script for loading image dataGridMask.py—Data augmentation4."run" folderTrain_Compare.ipynb—Executable file for model trainingTrueSkill.ipynb—Executable file for the TrueSkill algorithm5.requirements.txtEnvironment configuration and version numbers, including Python 3.9 and Pytorch 2.2<br>本项目包含论文《从街景影像到乡村：基于深度学习的中国农村大规模感知研究》（From street view imagery to the countryside: large-scale perception of rural China using deep learning）对应的深度学习模型代码，该论文介绍了一种Pair-CNN模型（Pair-CNN）。该模型被应用于乡村街景数据，以从五个维度对中国农村的主观感知进行评估：富裕度（Wealthy）、活力度（Lively）、宜居性（Habitable）、整洁度（Tidy）以及风土特色（Terroir）。本项目同时涵盖支撑该Pair-CNN模型的数据集与代码。 1. "data" 文件夹 Picture.zip：乡村街景影像数据集，包含100张随机选取的JPG格式乡村影像。 label_fy.csv：标注数据集，记录了志愿者基于富裕度指标对两张乡村影像进行的对比评级，该数据集仅包含Picture.zip中的影像。该文件包含三个字段：lefturl、righturl与label。其中label字段为二分类结果：0代表左侧影像优于右侧影像，1则代表相反情况。 2. "model" 文件夹 ResNet.py：模型基础结构代码，采用ResNet50架构。 ComparaNet.py：Pair-CNN模型的基础结构代码。 3. "other" 文件夹 Get_Image.py：影像数据加载脚本。 GridMask.py：数据增强相关代码。 4. "run" 文件夹 Train_Compare.ipynb：模型训练可执行文件。 TrueSkill.ipynb：TrueSkill算法可执行文件。 5. requirements.txt：环境配置文件及依赖版本清单，包含Python 3.9与PyTorch 2.2。

搜集汇总

背景与挑战

背景概述

该数据集包含用于评估中国农村主观感知的Pair-CNN深度学习模型代码和相关数据，涵盖街景图像和志愿者标注的财富指标比较标签。数据集支持模型训练和评估，适用于农村环境感知研究。