Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks

Alexander, 2016, Linking urban climate classification with an urban energy and water budget model: multi-site and multi-seasonal evaluation, Urban Clim., 17, 196, 10.1016/j.uclim.2016.08.003 Arnfield, 2003, Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island, Int. J. Climatol., 23, 1, 10.1002/joc.859 Auer, 1978, Correlation of land use and cover with meteorological anomalies, J. Appl. Meteorol., 17, 636, 10.1175/1520-0450(1978)017<0636:COLUAC>2.0.CO;2 Bechtel, 2015, Mapping local climate zones for a worldwide database of the form and function of cities, ISPRS Int. J. Geo-Inf., 4, 199, 10.3390/ijgi4010199 Bechtel, 2016, Classification of local climate zones using SAR and multispectral data in an arid environment, IEEE J. Select. Topics. Appl. Earth Observ. Remote Sens., 9, 3097, 10.1109/JSTARS.2016.2531420 Bechtel, 2019, Generating WUDAPT level 0 data – current status of production and evaluation, Urban Clim., 27 Brousse, 2016, WUDAPT, an efficient land use producing data tool for mesoscale models? integration of urban LCZ in WRF over madrid, Urban Clim., 17, 116, 10.1016/j.uclim.2016.04.001 Danylo, 2016, Contributing to WUDAPT: a local climate zone classification of two cities in Ukraine, IEEE J. Select. Topics. Appl. Earth Observ. Remote Sens., 9, 1841, 10.1109/JSTARS.2016.2539977 Demuzere, 2019, Global transferability of local climate zone models, Urban Clim., 27, 46, 10.1016/j.uclim.2018.11.001 Diaz-Pacheco, 2013, Exploring the limitations of CORINE land cover for monitoring urban land-use dynamics in metropolitan areas, J. Land Use Sci., 9, 243, 10.1080/1747423X.2012.761736 EEA (European Environment Agency), 2012 Ellefsen, 1991, Mapping and measuring buildings in the canopy boundary layer in ten u.s. cities, Energy Build., 16, 1025, 10.1016/0378-7788(91)90097-M Esch, 2013, Urban footprint processor—fully automated processing chain generating settlement masks from global data of the TanDEM-x mission, IEEE Geosci. Remote Sens. Lett., 10, 1617, 10.1109/LGRS.2013.2272953 Fenner, 2017, Intra and inter ‘local climate zone’ variability of air temperature as observed by crowdsourced citizen weather stations in berlin, Germany, Meteorol. Z., 26, 525, 10.1127/metz/2017/0861 Frantz, 2018 Frantz, 2016, An operational radiometric landsat preprocessing framework for large-area time series applications, IEEE Trans. Geosci. Remote Sens., 54, 3928, 10.1109/TGRS.2016.2530856 Frantz, 2018, Improvement of the fmask algorithm for sentinel-2 images: separating clouds from bright surfaces based on parallax effects, Remote Sens. Environ., 215, 471, 10.1016/j.rse.2018.04.046 Gál, 2015, Comparison of two different local climate zone mapping methods 2012, Urban climate zone detection and discrim ination using object - based analysis of VHR scenes Geiss, 2015, Normalization of TanDEM-x DSM data in urban environments with morphological filters, IEEE Trans. Geosci. Remote Sens., 53, 4348, 10.1109/TGRS.2015.2396195 Geletič, 2016, GIS-based delineation of local climate zones: the case of medium-sized central european cities, Morav. Geogr. Rep., 24, 2 Geletič, 2019, Spatial modelling of summer climate indices based on local climate zones: expected changes in the future climate of brno, Czech republic, Clim. Change, 10.1007/s10584-018-2353-5 Geletič, 2019, Inter-/intra-zonal seasonal variability of the surface urban heat island based on local climate zones in three central european cities, Build. Environ., 156, 21, 10.1016/j.buildenv.2019.04.011 Hammerberg, 2018, Implications of employing detailed urban canopy parameters for mesoscale climate modelling: a comparison between WUDAPT and GIS databases over vienna, Austria, Int. J. Climatol., 38, e1241, 10.1002/joc.5447 Hermosilla, 2012, Assessing contextual descriptive features for plot-based classification of urban areas, Landsc. Urban Plan., 106, 124, 10.1016/j.landurbplan.2012.02.008 Hidalgo, 2019, Comparison between local climate zones maps derived from administrative datasets and satellite observations, Urban Clim., 27, 64, 10.1016/j.uclim.2018.10.004 Hinton Homer, 2012 Hu, 2018, Feature extraction and selection of sentinel-1 dual-pol data for global-scale local climate zone classification, ISPRS Int. J. Geo-Inf., 7, 379, 10.3390/ijgi7090379 Huang, 2017, Densely connected convolutional networks Ioffe, 2015, Batch normalization: accelerating deep network training by reducing internal covariate shift, 448 Kaloustian, 2016, Local climatic zoning and urban heat island in beirut, Procedia Eng., 169, 216, 10.1016/j.proeng.2016.10.026 Kingma, 2015 Koc, 2017, Mapping local climate zones for urban morphology classification based on airborne remote sensing data LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Lelovics, 2014, Design of an urban monitoring network based on local climate zone mapping and temperature pattern modelling, Clim. Res., 60, 51, 10.3354/cr01220 Middel, 2014, Impact of urban form and design on mid-afternoon microclimate in phoenix local climate zones, Landsc. Urban Plan., 122, 16, 10.1016/j.landurbplan.2013.11.004 Mitraka, 2015, Exploiting earth observation data products for mapping local climate zones Müller, 2015, Mining dense landsat time series for separating cropland and pasture in a heterogeneous brazilian savanna landscape, Remote Sens. Environ., 156, 490, 10.1016/j.rse.2014.10.014 Olofsson, 2014, Good practices for estimating area and assessing accuracy of land change, Remote Sens. Environ., 148, 42, 10.1016/j.rse.2014.02.015 Perera, 2018, A “local climate zone” based approach to urban planning in colombo, Sri Lanka, Urban Clim., 23, 188, 10.1016/j.uclim.2016.11.006 Qiu, 2018, Feature importance analysis for local climate zone classification using a residual convolutional neural network with multi-source datasets, Remote Sens., 10, 1572, 10.3390/rs10101572 Qiu, 2019, Local climate zone-based urban land cover classification from multi-seasonal sentinel-2 images with a recurrent residual network, ISPRS J. Photogrammetry Remote Sens., 154, 151, 10.1016/j.isprsjprs.2019.05.004 Ren, 2017, Implementing wudapt product into urban development impact analysis by using wrf simulation result - a case study of the pearl river delta region (1980-2010), 22 Rizwan, 2008, A review on the generation, determination and mitigation of urban heat island, J. Environ. Sci., 20, 120, 10.1016/S1001-0742(08)60019-4 See, 2015, Developing a community-based worldwide urban morphology and materials database (WUDAPT) using remote sensing and crowdsourcing for improved urban climate modelling Simonyan, 2014, Very deep convolutional networks for large-scale image recognition, vol. 1556 Stewart, 2011 Stewart, 2012, Local climate zones for urban temperature studies, Bull. Am. Meteorol. Soc., 93, 1879, 10.1175/BAMS-D-11-00019.1 Stewart, 2013, Evaluation of the ‘local climate zone’ scheme using temperature observations and model simulations, Int. J. Climatol., 34, 1062, 10.1002/joc.3746 UN DESA (United Nations, Department of Economic and Social Affairs, Population Division), 2018 Verdonck, 2017, Influence of neighbourhood information on ‘local climate zone’ mapping in heterogeneous cities, Int. J. Appl. Earth Obs. Geoinf., 62, 102, 10.1016/j.jag.2017.05.017 Xu, 2019, Urban climate zone classification using convolutional neural network and ground-level images, Prog. Phys. Geogr.: Earth Environ., 43, 410, 10.1177/0309133319837711 Yokoya, 2018, Open data for global multimodal land use classification: outcome of the 2017 IEEE GRSS data fusion contest, IEEE J. Select. Topics. Appl. Earth Observ. Remote Sens., 11, 1363, 10.1109/JSTARS.2018.2799698 Yoo, 2019, Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using landsat images, ISPRS J. Photogrammetry Remote Sens., 157, 155, 10.1016/j.isprsjprs.2019.09.009 Yu, 2017, Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework, GIScience Remote Sens., 54, 741, 10.1080/15481603.2017.1323377 Zheng, 2018, GIS-based mapping of local climate zone in the high-density city of Hong Kong, Urban Clim., 24, 419, 10.1016/j.uclim.2017.05.008