气候变化领域集成服务门户(CCPortal): Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution

CCPortal

DOI	10.1016/j.rse.2020.112096
	Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution
	Chen T.-H.K.; Qiu C.; Schmitt M.; Zhu X.X.; Sabel C.E.; Prishchepov A.V.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	Landsat imagery is an unparalleled freely available data source that allows reconstructing land-cover and land-use change, including urban form. This paper addresses the challenge of using Landsat data, particularly its 30 m spatial resolution, for monitoring three-dimensional urban densification. Unlike conventional convolutional neural networks (CNNs) for scene recognition resulting in resolution loss, the proposed semantic segmentation framework provides a pixel-wise classification and improves the accuracy of urban form mapping. We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network (FCN) and a texture-based random forest (RF) model to map urban density in the two morphological dimensions: horizontal (compact, open, sparse) and vertical (high rise, low rise). We test whether a model trained on the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether we could use the model trained on the Danish data to accurately map ten other European cities. Our results show that an implementation of deep networks and the inclusion of multi-scale contextual information greatly improve the classification and the model's ability to generalize across space and time. Between the two semantic segmentation models, DeepLab provides more accurate horizontal and vertical classifications than FCN when sufficient training data is available. By using DeepLab, the F1 score can be increased by 4 and 10 percentage points for detecting vertical urban growth compared to FCN and RF for Denmark. For mapping the ten other European cities with training data from Denmark, DeepLab also shows an advantage of 6 percentage points over RF for both horizontal and vertical dimensions. The resulting maps across the years 1985 to 2018 reveal different patterns of urban growth between Copenhagen and Aarhus, the two largest cities in Denmark, illustrating that those cities have used various planning policies in addressing population growth and housing supply challenges. In summary, we propose a transferable deep learning approach for automated, long-term mapping of urban form from Landsat images that is effective in areas experiencing a slow pace of urban growth or with small-scale changes. © 2020 Elsevier Inc.
英文关键词	Deep learning; Landsat; Multi-temporal classification; Semantic segmentation; Spatial and temporal transferability; Urban form; Urban growth; Urbanization
语种	英语
scopus关键词	Classification (of information); Convolution; Convolutional neural networks; Decision trees; Deep learning; Housing; Land use; Mapping; Population statistics; Semantics; Textures; Contextual information; Convolutional networks; Landsat time series; Planning policies; Semantic segmentation; Spatial resolution; Temporal and spatial; Vertical dimensions; Urban growth; accuracy assessment; classification; Landsat; mapping; pixel; population growth; satellite data; satellite imagery; segmentation; spatiotemporal analysis; three-dimensional modeling; time series analysis; urbanization; Aarhus [Denmark]; Copenhagen [(CTY) Hovedstaden]; Denmark; Hovedstaden
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179118
作者单位	Department of Environmental Science, Aarhus University, Frederiksborgvej 399, Roskilde, DK-4000, Denmark; Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, Roskilde, DK-4000, Denmark; Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen, Øster Voldgade 10, København K, DK-1350, Denmark; Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), Arcisstr. 21, Munich, 80333, Germany; Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Münchener Str. 20, Wessling, 82234, Germany; Department of Geoinformatics, Munich University of Applied Sciences, Karlstraße 6, Munich, 80333, Germany
推荐引用方式 GB/T 7714	Chen T.-H.K.,Qiu C.,Schmitt M.,et al. Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution[J],2020,251.
APA	Chen T.-H.K.,Qiu C.,Schmitt M.,Zhu X.X.,Sabel C.E.,&Prishchepov A.V..(2020).Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution.Remote Sensing of Environment,251.
MLA	Chen T.-H.K.,et al."Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution".Remote Sensing of Environment 251(2020).