2017-2020年中国农村地区建筑物样本及标注无人机影像数据集

本数据在陕西渭南、江苏盐城、四川庐山、广东梅州、新疆阿图什、西藏那曲等中国多个农村地区，基于无人机航拍图像，制作并开源数据集：《中国农村地区建筑物样本及标注无人机影像数据集》；在此基础上，应用深度学习卷积神经网络，进行研究区农村建筑物自动提取及建筑面积估算应用。本数据集具有广阔的应用前景，既可用于国土部门统筹城乡发展，又为推动国家生态文明建设和美丽乡村战略规划提供技术数据支撑和辅助决策。

This dataset was developed and open-sourced as the "UAV Imagery Dataset with Building Samples and Annotations in Rural China" based on UAV aerial imagery collected from multiple rural regions across China, including Weinan (Shaanxi Province), Yancheng (Jiangsu Province), Lushan (Sichuan Province), Meizhou (Guangdong Province), Artux (Xinjiang Uygur Autonomous Region), and Naqu (Tibet Autonomous Region). Based on this dataset, deep learning convolutional neural networks were employed to automatically extract rural buildings and estimate their floor areas in the corresponding study areas. This dataset has broad application prospects: it can be utilized by land management departments to coordinate urban-rural development, and provides technical data support and auxiliary decision-making for advancing national ecological civilization construction and the strategic planning of beautiful rural areas.

搜集汇总

背景与挑战

背景概述

该数据集是一个基于2017-2020年中国多个农村地区（如陕西渭南、江苏盐城等）无人机影像的建筑物样本及标注数据集，分辨率高达1.02cm至7.76cm，主要用于深度学习卷积神经网络，以支持农村建筑物的自动提取和面积估算。它具有高精度处理特点，应用前景广泛，可为城乡协调发展、生态文明建设和美丽乡村战略规划提供技术数据支持和辅助决策。