Progress in urban climate science is severely restricted by the lack of useful information that describes aspects of the form and function of cities at a detailed spatial resolution. To overcome this shortcoming we are initiating an international effort to develop the World Urban Database and Access Portal Tools (WUDAPT) to gather and disseminate this information in a consistent manner for urban areas worldwide. The first step in developing WUDAPT is a description of cities based on the Local Climate Zone (LCZ) scheme, which classifies natural and urban landscapes into categories based on climate-relevant surface properties. This methodology provides a culturally-neutral framework for collecting information about the internal physical structure of cities. Moreover, studies have shown that remote sensing data can be used for supervised LCZ mapping. Mapping of LCZs is complicated because similar LCZs in different regions have dissimilar spectral properties due to differences in vegetation, building materials and other variations in cultural and physical environmental factors. The WUDAPT protocol developed here provides an easy to understand workflow; uses freely available data and software; and can be applied by someone without specialist knowledge in spatial analysis or urban climate science. The paper also provides an example use of the WUDAPT project results.

The World Urban Database and Access Portal Tools (WUDAPT) is an international community-based initiative to acquire and disseminate climate relevant data on the physical geographies of cities for modeling and analysis purposes. The current lacuna of globally consistent information on cities is a major impediment to urban climate science toward informing and developing climate mitigation and adaptation strategies at urban scales. WUDAPT consists of a database and a portal system; its database is structured into a hierarchy representing different levels of detail, and the data are acquired using innovative protocols that utilize crowdsourcing approaches, Geowiki tools, freely accessible data, and building typology archetypes. The base level of information (L0) consists of local climate zone (LCZ) maps of cities; each LCZ category is associated with a range of values for model-relevant surface descriptors (roughness, impervious surface cover, roof area, building heights, etc.). Levels 1 (L1) and 2 (L2) will provide specific intra-urban values for other relevant descriptors at greater precision, such as data morphological forms, material composition data, and energy usage. This article describes the status of the WUDAPT project and demonstrates its potential value using observations and models. As a community-based project, other researchers are encouraged to participate to help create a global urban database of value to urban climate scientists.

Although more than half of the Earth’s population live in urban areas, we know remarkably little about most cities and what we do know is incomplete (lack of coverage) and inconsistent (varying definitions and scale). While there have been considerable advances in the derivation of a global urban mask using satellite information, the complexity of urban structures, the heterogeneity of materials, and the multiplicity of spectral properties have impeded the derivation of universal urban structural types (UST). Further, the variety of UST typologies severely limits the comparability of such studies and although a common and generic description of urban structures is an essential requirement for the universal mapping of urban structures, such a standard scheme is still lacking. More recently, there have been two developments in urban mapping that have the potential for providing a standard approach: the Local Climate Zone (LCZ) scheme (used by the World Urban Database and Access Portal Tools project) and the Global Human Settlement Layer (GHSL) methodology by JRC. In this paper the LCZ scheme and the GHSL LABEL product were compared for selected cities. The comparison between both datasets revealed a good agreement at city and coarse scale, while the contingency at pixel scale was limited due to the mismatch in grid resolution and typology. At a 1 km scale, built-up as well as open and compact classes showed very good agreement in terms of correlation coefficient and mean absolute distance, spatial pattern, and radial distribution as a function of distance from town, which indicates that a decomposition relevant for modelling applications could be derived from both. On the other hand, specific problems were found for both datasets, which are discussed along with their general advantages and disadvantages as a standard for UST classification in urban remote sensing.