气候变化领域集成服务门户(CCPortal): Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks

CCPortal

DOI	10.1016/j.rse.2019.111472
	Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks
	Rosentreter J.; Hagensieker R.; Waske B.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	In recent years, the concept of Local Climate Zones (LCZs) has become a new standard in the research of urban landscapes. LCZs outline a classification scheme, which is designed to categorize urban and rural surfaces according to their climate-relevant properties, irrespective of local building materials or cultural background. We present a novel workflow for a high-resolution derivation of LCZs using multi-temporal Sentinel 2 (S2) composites and supervised Convolutional Neural Networks (CNNs). We assume that CNNs, due to their potential invariance to size and illumination of objects, are best suited to predict the highly context-based LCZs on a large scale. As a first step, the proposed workflow includes a fully automated generation of cloud-free S2 composites. These composites serve as training data basis for the LCZ classifications carried out over eight German cities. Results show that by using a CNN, overall accuracies can be increased by an average of 16.5 and 4.8 percentage points when compared to a pixel-based and a texture-based Random Forest approach, respectively. If sufficient training data is available, CNN models proved to be robust in classifying unknown cities and achieved overall accuracies of up to 86.5%. The proposed method constitutes a feasible approach for automated, large scale mapping of LCZs, and could be the preferred alternative for LCZ classifications in upcoming studies. © 2019 Elsevier Inc.
英文关键词	Convolutional neural network; Local climate zones; Sentinel 2
语种	英语
scopus关键词	Convolution; Decision trees; Mapping; Neural networks; Textures; Classification scheme; Convolutional neural network; Cultural backgrounds; Fully automated; Local climate; Overall accuracies; Percentage points; Sentinel 2; Classification (of information); artificial neural network; automation; classification; climate change; pixel; satellite imagery; Sentinel
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/179555
作者单位	Remote Sensing Osnabrück, Institute of Computer Science, University of Osnabrück, Wachsbleiche 27, Osnabrück, 49090, Germany; osir.io, c/o the Drivery, Mariendorfer Damm 1, Berlin, 12099, Germany
推荐引用方式 GB/T 7714	Rosentreter J.,Hagensieker R.,Waske B.. Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks[J],2020,237.
APA	Rosentreter J.,Hagensieker R.,&Waske B..(2020).Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks.Remote Sensing of Environment,237.
MLA	Rosentreter J.,et al."Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks".Remote Sensing of Environment 237(2020).