DOI: 10.57760/sciencedb.j00001.00226
title: Building sampling and labeling dataset of UAV images in rural China
title: 中国农村地区建筑物样本及标注无人机影像数据集

description: Rural buildings are one of the most important means to observe rural land changes and economic development. As an agricultural country like China, timely and accurate extraction of rural buildings from high-resolution remote sensing images is crucial to rural development and rural planning. With the recent advancements of computer vision and computing capabilities, deep learning has achieved considerable achievements in many applications such as building extraction due to its automatic learning features and strong applicability. Deep learning usually requires large amounts of training data. At present, the datasets commonly used in deep learning to identify buildings are mainly international open-source building datasets, including Massachusetts, INRIA, WHU, etc. These datasets are based on foreign buildings, lacking sampling data of buildings that are open-source, high-precision, wide-covered, and suitable for the architectural style of rural areas in China. Here, we propose an open-resource dataset named “Building sampling and labeling dataset of UAV images in rural China”. This dataset is based on the unmanned aerial images (UAV) collected in Weinan, Shaanxi, Huai’an, Jiangsu, Kangding, Sichuan, Shanwei, Guangdong, Huizhou, Guangdong, Atushi, Xinjiang, Songyuan, Jilin, and other rural areas in China from 2017 to 2020. This dataset has high spatial resolution and can represent the characteristics of buildings in rural China. It can be applied for building extraction using deep learning methods as well as be combined with further research for spatial analysis. Furthermore, it is of great significance for rural development and the Beautiful Countryside Construction in China.
description: Rural buildings are one of the most important means to observe rural land changes and economic development. As an agricultural country like China, timely and accurate extraction of rural buildings from high-resolution remote sensing images is crucial to rural development and rural planning. With the recent advancements of computer vision and computing capabilities, deep learning has achieved considerable achievements in many applications such as building extraction due to its automatic learning features and strong applicability. Deep learning usually requires large amounts of training data. At present, the datasets commonly used in deep learning to identify buildings are mainly international open-source building datasets, including Massachusetts, INRIA, WHU, etc. These datasets are based on foreign buildings, lacking sampling data of buildings that are open-source, high-precision, wide-covered, and suitable for the architectural style of rural areas in China. Here, we propose an open-resource dataset named “Building sampling and labeling dataset of UAV images in rural China”. This dataset is based on the unmanned aerial images (UAV) collected in Weinan, Shaanxi, Huai’an, Jiangsu, Kangding, Sichuan, Shanwei, Guangdong, Huizhou, Guangdong, Atushi, Xinjiang, Songyuan, Jilin, and other rural areas in China from 2017 to 2020. This dataset has high spatial resolution and can represent the characteristics of buildings in rural China. It can be applied for building extraction using deep learning methods as well as be combined with further research for spatial analysis. Furthermore, it is of great significance for rural development and the Beautiful Countryside Construction in China.