Estimating Household Green Space in Composite Residential Community Solely Using Drone Oblique Photography
文献类型：期刊论文
| 作者 | Kang, Meiqi2; Song, Kaiyi2; Liao, Xiaohan1; Lin, Jiayuan2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-08-03 |
| 卷号 | 17期号:15页码:2691 |
| 关键词 | average green space per household high-rise building drone RGB vegetation index facade image instance segmentation |
| DOI | 10.3390/rs17152691 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Residential green space is an important component of urban green space and one of the major indicators for evaluating the quality of a residential community. Traditional indicators such as the green space ratio only consider the relationship between green space area and total area of the residential community while ignoring the difference in the amount of green space enjoyed by household residents in high-rise and low-rise buildings. Therefore, it is meaningful to estimate household green space and its spatial distribution in residential communities. However, there are frequent difficulties in obtaining specific green space area and household number through ground surveys or consulting with property management units. In this study, taking a composite residential community in Chongqing, China, as the study site, we first employed a five-lens drone to capture its oblique RGB images and generated the DOM (Digital Orthophoto Map). Subsequently, the green space area and distribution in the entire residential community were extracted from the DOM using VDVI (Visible Difference Vegetation Index). The YOLACT (You Only Look At Coefficients) instance segmentation model was used to recognize balconies from the facade images of high-rise buildings to determine their household numbers. Finally, the average green space per household in the entire residential community was calculated to be 67.82 m2, and those in the high-rise and low-rise building zones were 51.28 m2 and 300 m2, respectively. Compared with the green space ratios of 65.5% and 50%, household green space more truly reflected the actual green space occupation in high- and low-rise building zones. |
| URL标识 | 查看原文 |
| WOS关键词 | VEGETATION INDEXES ; AUTOMATED CROP |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001549682500001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215516]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Lin, Jiayuan |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst, Natl Observat & Res Stn, Chongqing 400715, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Kang, Meiqi,Song, Kaiyi,Liao, Xiaohan,et al. Estimating Household Green Space in Composite Residential Community Solely Using Drone Oblique Photography[J]. REMOTE SENSING,2025,17(15):2691. |
| APA | Kang, Meiqi,Song, Kaiyi,Liao, Xiaohan,&Lin, Jiayuan.(2025).Estimating Household Green Space in Composite Residential Community Solely Using Drone Oblique Photography.REMOTE SENSING,17(15),2691. |
| MLA | Kang, Meiqi,et al."Estimating Household Green Space in Composite Residential Community Solely Using Drone Oblique Photography".REMOTE SENSING 17.15(2025):2691. |
入库方式： OAI收割
来源：地理科学与资源研究所
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