Abstract
The local climate zone (LCZ) scheme was originally proposed to provide an interdisciplinary taxonomy for urban heat island (UHI) studies. In recent years, the scheme has also become a starting point for the development of higher-level products, as the LCZ classes can help provide a generalized understanding of urban structures and land uses. LCZ mapping can therefore theoretically aid in fostering a better understanding of spatio-temporal dynamics of cities on a global scale. However, reliable LCZ maps are not yet available globally. As a first step toward automatic LCZ mapping, this work focuses on LCZ-derived land cover classification, using multi-seasonal Sentinel-2 images. We propose a recurrent residual network (Re-ResNet) architecture that is capable of learning a joint spectral-spatial-temporal feature representation within a unitized framework. To this end, a residual convolutional neural network (ResNet) and a recurrent neural network (RNN) are combined into one end-to-end architecture. The ResNet is able to learn rich spectral-spatial feature representations from single-seasonal imagery, while the RNN can effectively analyze temporal dependencies of multi-seasonal imagery. Cross validations were carried out on a diverse dataset covering seven distinct European cities, and a quantitative analysis of the experimental results revealed that the combined use of the multi-temporal information and Re-ResNet results in an improvement of approximately 7 percent points in overall accuracy. The proposed framework has the potential to produce consistent-quality urban land cover and LCZ maps on a large scale, to support scientific progress in fields such as urban geography and urban climatology.
Highlights
The local climate zone (LCZ) scheme has been developed primarily for the communication of meta-data produced by observational urban heat island (UHI) studies and has a broad range of applications, including classifying weather stations and assessing social inequality (Stewart, 2011)
Cross validations were carried out on a diverse dataset covering seven distinct European cities, and a quantitative analysis of the experimental results revealed that the combined use of the multi-temporal information and Re-residual convolutional neural network (ResNet) results in an improvement of approximately 7 percent points in overall accuracy
Using the simplified land cover classification results as basis, a complete LCZ classification can be achieved by adding multisensor and multi-temporal information, such as that provided by LiDAR and satellite images acquired by other sensors (Xu et al, 2018)
Summary
The local climate zone (LCZ) scheme has been developed primarily for the communication of meta-data produced by observational urban heat island (UHI) studies and has a broad range of applications, including classifying weather stations and assessing social inequality (Stewart, 2011). In addition to its strong usefulness in urban climate studies (Stewart and Oke, 2012; Stewart et al, 2014; Fenner et al, 2017; Quan et al, 2017; Quanz et al, 2018; Kotharkar and Bagade, 2018), the potential of LCZ for classifying the internal urban structure of human settlements, to provide auxiliary data for applications such as disaster mitigation, urban planning, and population assessment (Bechtel et al, 2016; Wicki and Parlow, 2017) in a rapidly urbanizing world (Taubenböck et al, 2012) has recently been explored. Accurate LCZ maps can be used to extract and analyze reliable and detailed information on the extent of human settlement to provide assistance in fulfilling the evaluation and monitoring requirements of the 2030 Agenda for Sustainable Development and provide reference information for achieving the Sustainable Development Goal 11 (United and Nations, 2015), “Make cities and human settlements inclusive, safe, resilient, and sustainable.” As an example of such applications, the LCZ framework was exploited to monitor sustainable urbanization in terms of access to safe housing using data from an exemplary study in Pretoria and Johannesburg, South Africa (Danylo et al, 2017)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS Journal of Photogrammetry and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
