Research on urban land use extraction at the metropolitan scale

doi:10.13474/j.cnki.11-2246.2022.0044

Abstract

Abstract: With the improvement of the high resolution of domestic satellites, the detailed description of urban surface elements can be described from different aspects such as geometric form, texture structure, and spectral information. Compared with object-oriented classification method, the rapid development of deep learning technology has continuously improved the accuracy of urban building extraction. However, due to the high buildings and trees on both sides of the road, the extraction accuracy of urban roads is still limited. Based on the use of convolutional neural networks to extract buildings, this paper uses OSM polygon road data and urban boundary data, combined with normalized vegetation index and water index, and with the help of spatial layer overlay and error-tolerant processing, high-precision extraction of urban buildings, roads, vegetation and water bodies within urban built-up areas has been achieved. This study provides an effective approach in supporting for domestic high-resolution image-assisted urban fine management and application.

Key words: earth surface elements, GF-2, OSM, deep learning

CLC Number:

SUN Qinqin, CAI Guoyin, YANG Liuzhong, ZHANG Ning, DU Mingyi. Research on urban land use extraction at the metropolitan scale[J]. Bulletin of Surveying and Mapping, 2022, 0(2): 62-66.

References

[1] MURAYAMA Y,KAMUSOKO C, YAMASHITA A, et al. Importance of remote sensing and land change modeling for urbanization studies[C]//Proceedings of 2017 Hrban Development in Asia and Africa.[S.l.]:Springer,2017.
[2] LIU X P, MA L, LI X, et al. Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata[J]. International Journal of Geographical Information Science, 2014, 28:148-163.
[3] DEWAN A M, YAMAGUCHI Y. Land use and land cover change in Greater Dhaka, Bangladesh:using remote sensing to promote sustainable urbanization[J]. Applied Geography, 2009,29(3):390-401.
[4] ZHANG C, SARGENT I, PAN X, et al. An object-based convolutional neural network (OCNN) for urban land use classification[J]. Remote Sensing of Environment,2018,216:57-70.
[5] GRIPPA T, GEORGANOS S, ZAROUGUI S, et al.Mapping urban land use at street block level using OpenStreetMap, remote sensing data, and spatial metrics[J]. ISPRS International Journal of Geo-Information,2018,7(7),57-70.
[6] SOLIMAN A, SOLTANI K, YIN J, et al. Social sensing of urban land use based on analysis of Twitter users'mobility patterns[J]. Plos One, 2017, 12(7):e0181657.
[7] SUN J, WANG H, SONG Z L, et al.Mapping essential urban land use categories in Nanjing by integrating multi-source big data[J]. Remote Sensing,2020,12(15):2386.
[8] ARSANJANI J, HELBICH M, BAKILLAH M, et al. Toward mapping land-use patterns from volunteered geographic information[J]. International Journal of Geographical Information Science,2013,27(12):2264-2278.
[9] GONG P, CHEN B, LI X C, et al. Mapping essential urban land use categories in China (EULUC-China):preliminary results for 2018[J]. Science Bulletin,2020,65(3):182-187.
[10] 黄煜. SCSG-OSM数据模型研究及在城市三维建模中的应用[D].桂林:桂林理工大学,2019.
[11] 康磊,张付兵.一种基于OSM数据的道路更新方法[J].测绘与空间地理信息,2021,44(2):19-23.
[12] CAI G Y, REN H Q, YANG L Z, et al. Detailed urban land use land cover classification at the metropolitan scale using a three-layer classification scheme[J]. Sensors,2019,19(14):3120.
[13] 厦门市统计局,国家统计局厦门调查队.厦门经济特区年鉴[M].北京:中国统计出版社, 2019.
[14] 潘腾.高分二号卫星的技术特点[J].中国航天, 2015(1):3-9.
[15] 韩利冬.高分2号遥感影像典型自然要素的自动识别方法研究[D].泰安:山东农业大学,2020.
[16] 胡光庭,彭彦彦,宋子辉.基于高分遥感的风景名胜区资源环境评价[J].建设科技,2016(4):48-50.
[17] 董仲奎,温俊丽.高分遥感数据在城市园林绿化规划实施监测中的应用[J].测绘与空间地理信息,2016,39(8):75-76.
[18] 张宁,于静,杨柳忠,等.利用遥感技术辅助保障房巡查[J].建设科技,2015(15):145-147.
[19] NEIS P, ZIPF A. Analyzing the contributor activity of a volunteered geographic information project-the case of OpenStreetMap[J]. IJGI,2012,1(2):146-165.
[20] ZHANG Y, LI Q Z, HUANG H P, et al. The combined use of remote sensing and social sensing data in fine-grained urban land use mapping:a case study in Beijing, China[J]. Remote Sensing,2017,9(9):865.
[21] SHELHAMER E, LONG J, DARRELL T. Fully convolu-tional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence, 2017,39,640-651.
[22] SUN Z C,XU R, DU W J, et al. High-resolution urban land mapping in China from Sentinel 1A/2 imagery based on Google Earth Engine[J]. Remote Sensing, 2019, 11(7):752.