Generating WUDAPT Level 0 data – Current status of production and evaluation

Bechtel, Benjamin; Alexander, Paul J.; Beck, Christoph; Böhner, Jürgen; Brousse, Oscar; Ching, Jason; Demuzere, Matthias; Fonte, Cidália C.; Gál, Tamás; Hidalgo, Julia; Hoffmann, Peter; Middel, Ariane; Mills, Gerald; Ren, Chao; See, Linda; Sismanidis, Panagiotis; Verdonck, Marie-Leen; Xu, Gaochao; Xu, Yong

doi:10.1016/j.uclim.2018.10.001

Cited by 208 publications

(145 citation statements)

References 53 publications

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“…The more iterations (at least three) the better the accuracy; • Submit your LCZ map and training areas to the WUDAPT portal [29], even if your city is already present: combining training areas typically results in an overall better classification.…”

Section: Discussionmentioning

confidence: 99%

The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification

et al. 2019

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Since 2012, Local Climate Zones (LCZ) have been used for numerous studies related to urban environment. In 2015, this use amplified because a method to map urban areas in LCZs was introduced by the World Urban Database and Access Portal Tools (WUDAPT). However in 2017, the first HUMan INfluence EXperiment showed that these maps often have poor or low quality. Since the maps are used in different applications such as urban modelling and land use/land cover change studies, it is of the utmost importance to improve mapping accuracies and a second experiment was launched. In HUMINEX 2.0, the focus lies on providing guidelines on the use of the mapping protocol based on the results of both HUMINEX 1.0 and 2.0. The results showed that: (1) it is important to follow the mapping protocol as strictly as possible, (2) a reasonable amount of time should be spent on the mapping procedure, (3) all users should perform a driving test, and (4) training area sets should be stored in the WUDAPT database for other users.

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Section: Discussionmentioning

confidence: 99%

The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification

et al. 2019

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“…Under this context, LULC is categorized into 17 LCZs based on surface cover, structure, material, and human activity [2]. An ongoing project, the world urban database and access portal tools (WUDAPT) [54], is aimed to gather such climate relevant surface information using freely remotely sensed data (i.e., Landsat and Sentinel).…”

Section: The Optical-sar Zhuhai-macau Lcz Datasetmentioning

confidence: 99%

Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data

et al. 2019

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Object-based image analysis (OBIA) has been widely used for land use and land cover (LULC) mapping using optical and synthetic aperture radar (SAR) images because it can utilize spatial information, reduce the effect of salt and pepper, and delineate LULC boundaries. With recent advances in machine learning, convolutional neural networks (CNNs) have become state-of-the-art algorithms. However, CNNs cannot be easily integrated with OBIA because the processing unit of CNNs is a rectangular image, whereas that of OBIA is an irregular image object. To obtain object-based thematic maps, this study developed a new method that integrates object-based post-classification refinement (OBPR) and CNNs for LULC mapping using Sentinel optical and SAR data. After producing the classification map by CNN, each image object was labeled with the most frequent land cover category of its pixels. The proposed method was tested on the optical-SAR Sentinel Guangzhou dataset with 10 m spatial resolution, the optical-SAR Zhuhai-Macau local climate zones (LCZ) dataset with 100 m spatial resolution, and a hyperspectral benchmark the University of Pavia with 1.3 m spatial resolution. It outperformed OBIA support vector machine (SVM) and random forest (RF). SVM and RF could benefit more from the combined use of optical and SAR data compared with CNN, whereas spatial information learned by CNN was very effective for classification. With the ability to extract spatial features and maintain object boundaries, the proposed method considerably improved the classification accuracy of urban ground targets. It achieved overall accuracy (OA) of 95.33% for the Sentinel Guangzhou dataset, OA of 77.64% for the Zhuhai-Macau LCZ dataset, and OA of 95.70% for the University of Pavia dataset with only 10 labeled samples per class.

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“…To perform an accuracy assessment, a total of 415 validation points was randomly selected based on the classified LCZ map to compare with Google Earth imagery at each validation point. The overall accuracy, calculated based on the confusion matrix (Middel et al, ; C. Wang et al, ), is 70% in the urban areas, which is reliable (Bechtel et al, ). The final map should be treated as a general rather than precise description of the city and its surrounding environment.…”

Section: Model Configurationsmentioning

confidence: 99%

High‐Resolution, Multilayer Modeling of Singapore's Urban Climate Incorporating Local Climate Zones

Mughal

Yin

et al. 2019

JGR Atmospheres

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We applied Weather Research and Forecasting (WRF) model's Multilayer Urban Canopy Model (MLUCM) to simulate the urban climate of Singapore during a hot period in April 2016. The high‐resolution local climate zone (LCZ) map was used as urban land use/land cover data in order to study the intraurban variability in different LCZ classes. The LCZ map for Singapore was developed by adopting the World Urban Database and Access Portal Tools (WUDAPT) methodology based on satellite remote sensing imageries and building height data. The coupled WRF/MLUCM model was validated using meteorological data from stations across Singapore. Higher index of agreement compared to observations and lower root‐mean‐squared error of 2‐m temperature, relative humidity, and global horizontal irradiance showed satisfactory model performance. A sensitivity analysis of initial and boundary conditions helped in determining the model configuration with the least error for quantifying the urban heat island (UHI) effect. The diurnal cycle and the spatial pattern of UHI were investigated, and it was found that the mean UHI intensity peaked in the early morning at 2.2 °C, reaching 3.6 °C in the Compact High Rise (LCZ1) areas. The anthropogenic heat due to indoor air conditioning was found to play a major role in all the processes studied, while the effect of the different land use types was most pronounced during nighttime and least visible near noon. The UHI circulation developed near the central catchment is found to prevent sea breezes from further propagating inland.

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Generating WUDAPT Level 0 data – Current status of production and evaluation

Cited by 208 publications

References 53 publications

The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification

The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification

Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data

High‐Resolution, Multilayer Modeling of Singapore's Urban Climate Incorporating Local Climate Zones

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