Papers and Presentations

This page does not provide a comprehensive overview of all publications covering WUDAPT or Local Climate Zones. Please use your database/platform of preference (Google scholar, web of science, scopus, …) to find more publications.

Feel free to reach out to in case you want to see your work listed below.


The fundamental idea behind WUDAPT and the state of the project are discussed here:

  • Ching, J., Mills, G., Bechtel, B., See, L., Feddema, J., Wang, X., Ren, C., Brousse, O., Martilli, A., Neophytou, M., Mouzourides, P., Stewart, I., Hanna, A., Ng, E., Foley, M., Alexander, P., Aliaga, D., Niyogi, D., Shreevastava, A., Bhalachandran, P., Masson, V., Hidalgo, J., Fung, J., Andrade, M., Baklanov, A., Dai, W., Milcinski, G., Demuzere, M., Brunsell, N., Pesaresi, M., Miao, S., Mu, Q., Chen, F., Theeuwes, N., 2018. WUDAPT: An Urban Weather, Climate, and Environmental Modeling Infrastructure for the Anthropocene. Bull. Amer. Meteor. Soc. 99, 1907–1924.

In addition:

  • We have compiled a briefing document on WUDAPT that you can download from here.
  • We have also written a small piece in Nature, which is accompanied by an IIASA blog on WUDAPT.
  • The need for WUDAPT for weather, climate and air quality applications is outlined here.
  • Jason Ching gave a presentation on WUDAPT at the American Meteorological Society meeting (10-14 January 2016) in New Orleans entitled ‘The World Urban Database and Access Portal Tools, WUDAPT, an international collaborative project for climate relevant physical geography data for the world’s cities’.  The extended abstract can be accessed here.

LCZ mapping (level 0)

The LCZ Generator is explained here (open access):

  • Demuzere M, Kittner J, Bechtel B. LCZ Generator: A Web Application to Create Local Climate Zone Maps. Front Environ Sci. 2021;9. doi:10.3389/fenvs.2021.637455 .

The LCZ mapping workflow is presented here (open access):

  • Bechtel, B., Alexander, P., Böhner, J., Ching, J., Conrad, O., Feddema, J., Mills, G., See, L. and Stewart, I. 2015. Mapping local climate zones for a worldwide database of form and function of cities. International Journal of Geographic Information, 4(1), 199-219. doi:10.3390/ijgi4010199 .

The standard quality assessment and the state of the database are discussed here:

  • Bechtel, B., Alexander, P.J., Beck, C., Böhner, J., Brousse, O., Ching, J., Demuzere, M., Fonte, C., Gál, T., Hidalgo, J., Hoffmann, P., Middel, A., Mills, G., Ren, C., See, L., Sismanidis, P., Verdonck, M.-L., Xu, G., Xu, Y., 2019. Generating WUDAPT Level 0 data – Current status of production and evaluation. Urban Climate 27, 24–45.

Further studies applied the LCZ mapping methodology in different environments and presented alternative methods:

  • Lipson, M., Nazarian, N., Hart, M., Nice, K., Conroy, B. (2022). Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets. Sydney morphology and land surface dataset.
  • Lehnert M, Savi S, Geletiˇ J. Mapping Local Climate Zones and Their Applications in European Urban Environments : A Systematic Literature Review and Future Development Trends. 2021. doi:10.3390/ijgi10040260
  • Yokoya, N., Ghamisi, P., Xia, J., Sukhanov, S., Heremans, R., Tankoyeu, I., Bechtel, B., Saux, B.L., Moser, G. & Tuia, D. (2018). Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 1363–1377, 10.1109/JSTARS.2018.2799698 .
  • Xu, Y., Ren, C., Cai, M., Edward, N. Y. Y., & Wu, T. (2017). Classification of Local Climate Zones Using ASTER and Landsat Data for High-Density Cities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
  • Verdonck, M.-L., Okujeni, A., Van Der Linden, S., Demuzere, M., De Wulf, R. & Van Coillie, F. 2017. Influence of Neighbourhood Information on ‘ Local Climate Zone ’ Mapping in Heterogeneous Cities. Int J Appl Earth Obs Geoinformation 62, 102–13. doi:10.1016/j.jag.2017.05.017
  • Bechtel, B., Demuzere, M., Sismanidis, P., Fenner, D., Brousse, O., Beck, C.,Van Coillie, F., Conrad, O., Keramitsoglou, I., Middel, A., Mills, G., Niyogi, D., Otto, M., See, L., Verdonck, M.-L., 2017. Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX). Urban Science, 1(2).  
  • Zhongli, L., & Hanqiu, X. (2016). A study of Urban heat island intensity based on “local climate zones”: A case study in Fuzhou, China. In Earth Observation and Remote Sensing Applications (EORSA), 2016 4th International Workshop on (pp. 250–254). IEEE. Retrieved from
  • Bechtel, B., See, L., Mills, G., & Foley, M. (2016). Classification of Local Climate Zones Using SAR and Multispectral Data in an Arid Environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99), 1–9.
  • Danylo, O., See, L., Bechtel, B., Schepaschenko, D., & Fritz, S. (2016). Contributing to WUDAPT: A Local Climate Zone Classification of Two Cities in Ukraine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99), 1–13.
  • Mitraka, Z., Del Frate, F., Chrysoulakis, N., & Gastellu-Etchegorry, J.-P. (2015). Exploiting Earth Observation data products for mapping Local Climate Zones. In Urban Remote Sensing Event (JURSE), 2015 Joint (pp. 1–4). IEEE. Retrieved from
  • Geletič, J., & Lehnert, M. (2016). GIS-based delineation of local climate zones: The case of medium-sized Central European cities. Moravian Geographical Reports, 24(3), 2–12.
  • Kaloustian, N., & Bechtel, B. (2016). Local Climatic Zoning and Urban Heat Island in Beirut. Procedia Engineering, 169, 216–223.
  • erera, N. G. R., & Emmanuel, R. (2016). A “Local Climate Zone” based approach to urban planning in Colombo, Sri Lanka. Urban Climate. Retrieved from
  • Qiu, C., Schmitt, M., Mou, L., Ghamisi, P., Zhu, X., Qiu, C., Schmitt, M., Mou, L., Ghamisi, P. & Zhu, X.X. (2018). Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets. Remote Sensing, 10, 1572, 10.3390/rs10101572 .
  • REN, C., WANG, R., CAI, M., XU, Y., Zheng, Y., & Ng, E. (2016). The Accuracy of LCZ maps Generated by the World Urban Database and Access Portal Tools (WUDAPT) Method: A Case Study of Hong Kong. Retrieved from [RG]

Model applications

Several studies have applied WUDAPT data in different models:

  • Lipson, M., Nazarian, N., Hart, M., Nice, K., Conroy, B. (2022). A Transformation in City-Descriptive Input Data for Urban Climate Models. Environmental Informatics and Remote Sensing. Frontiers.
  • Varentsov, M., Samsonov, T., Demuzere, M., Impact of Urban Canopy Parameters on a Megacity’s Modelled Thermal Environment. Atmosphere (Basel). 2020;11(12):1349. doi:10.3390/atmos11121349
  • Geletič, J., Lehnert, M., Dobrovolný, P., Žuvela-Aloise, M., (2019). Spatial modelling of summer climate indices based on local climate zones: expected changes in the future climate of Brno, Czech Republic. Climatic Change.
  • Verdonck M-L, Demuzere M, Hooyberghs H, Priem F, Van Coillie F. Heat risk assessment for the Brussels capital region under different urban planning and greenhouse gas emission scenarios. J Environ Manage. 2019;249:109210. doi:10.1016/j.jenvman.2019.06.111
  • Hammerberg, K., Brousse, O., Martilli, A. and Mahdav, A. (2018). Implications of employing detailed urban canopy parameters for mesoscale climate modelling: a comparison between WUDAPT and GIS databases over Vienna, Austria. International Journal of Climatology. doi: 10.1002/joc.5447
  • Brousse, O., Martilli, A., Foley, M., Mills, G., & Bechtel, B. (2016). WUDAPT, an efficient land use producing data tool for mesoscale models? Integration of urban LCZ in WRF over Madrid. Urban Climate, 17, 116–134.
  • Alexander, P. J., Bechtel, B., Chow, W. T. L., Fealy, R., & Mills, G. (2016). Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation. Urban Climate, 17, 196–215.
  • Wouters, H., Demuzere, M., Blahak, U., Fortuniak, K., Maiheu, B., Camps, J., … van Lipzig, N. P. M. (2016). The efficient urban canopy dependency parametrization (SURY) v1.0 for atmospheric modelling: description and application with the COSMO-CLM model for a Belgian summer. Geosci. Model Dev., 9(9), 3027–3054.
  • Alexander, P. J., Mills, G., & Fealy, R. (2015). Using LCZ data to run an urban energy balance model. Urban Climate, 13, 14–37.

Level 1 and 2 data

Methodologies to derive more detailed information on urban morphology:

  • Ching, J., Aliaga, D., Mills, G., Masson, V., See, L., Neophytou, M., et al. (2019). Pathway using WUDAPT’s Digital Synthetic City tool towards generating urban canopy parameters for multi-scale urban atmospheric modeling. Urban Climate, 28, 100459.
  • Middel A, Lukasczyk J, Zakrzewski S, Arnold M, Maciejewski R. Urban form and composition of street canyons: A human-centric big data and deep learning approach. Landsc Urban Plan. 2019, 183:122-132. doi:10.1016/j.landurbplan.2018.12.001
  • Middel A, Lukasczyk J, Maciejewski R, Demuzere M, Roth M. Sky View Factor footprints for urban climate modeling. Urban Clim. 2018; 25:120-134. doi:10.1016/j.uclim.2018.05.004
  • Xu, Y., Ren, C., Ma, P., Ho, J., Wang, W., et al. (2017). Urban morphology detection and computation for urban climate research. Landscape and Urban Planning, 167, 212-224.