Metadata

City (ID)	London (London)
Training data	London_NialBuckly_TK_MF_20170612 Contains training data from: Buckley N. (2017). London WUDAPT Level 0 training data is licensed under CC-BY-NC-SA 4.0. available at www.wudapt.org/cities/raw-training-area-download/
Size and number	N (size) of traing areas: LCZ1:1(0.18 km²), LCZ2:6(2.8 km²), LCZ4:4(0.62 km²), LCZ5:19(4 km²), LCZ6:35(16 km²), LCZ8:16(6.4 km²), LCZ10:10(2.2 km²), LCZA:34(17 km²), LCZB:9(3.4 km²), LCZD:43(36 km²), LCZG:23(1.5e+02 km²),
Features	LANDSAT: LC82010242016047LGN00 , LC82010242016335LGN00 ,
Author, Co-authors	Nial Buckley, n.a.
Acknowledgements, funding	n.a.
Review	Micheal Foley Torben Kraft
Class occurrence (%)	URBAN: 17 NATURAL[E]: 83 LCZ: <1:0.0031, 2:0.1, 4:0.012, 5:0.69, 6:15, 8:0.93, 10:0.18, A:8.6, B:2.6, D:69, G:3,>
Time	years: 2016-2016 months:
Extend	Lat: 51.53, Lon: 0.06567, UTM zone 31N X: 204570-358470 Y: 5666280-5802280
Grid cell size (m)	100
Reference	n.a.
Status	in_review, Review: in_prep, Summary: [BOOTSTRAP]passed[OA_urb]0.68[WA]0.96[review]in_prep
Evaluation Version	dblv00, docversion=0.33

Summary

Consistency (urban)

Certainty (urban)

Correctness (urban)

Score

0.679

0.775

n.a.

4.28

LCZ Map

The final map (=classification result) can slightly vary (~ 5 %) according to the random factor of the classifier. This is the post-filtered classification (Majority filter, 3 pixels radius).

Consistency

Since independent validation data is typically missing, consistency and robustness are evaluated by a bootstrapping cross-validation approach. Therefore, the classification is conducted 25 times using 50 % of the training areas (TA) and subsequently evaluated using the other 50 % (testing data).

The boxplots show the distribution of different accuracy measures based on the testing data in the 25 runs, i.e.:

· OA: overall accuracy on all Polygons

· Kappa: Standard measure accounting for different classes

· OA on urban polygons only

· Builtup: OA of classification in urban and natural (without E) only

· Weighted by class similarity metric (shown below), accounts for (dis-similarity) of classes

The measures also reflect the robustness vs. the random choice of training data. However, they cannot proof, that the TA are semantically correct, but only that they are consistent.

Parameter (mean of 25 runs)	Accuracy
Overall accuracy of all testing polygons	0.883
Kappa of all testing polygons	0.807
Overall accuracy of urban classes	0.679
Accuracy built-up (natural vs urban classes, without E)	0.956
Accuracy weighted with LCZ metric	0.96

Certainty

Correctness

Here the comparison with the reference files is displayed if available.

Parameter (mean of 25 runs)	Accuracy
Overall accuracy of all testing polygons	n.a.
Kappa of all testing polygons	n.a.
Overall accuracy of urban classes	n.a.
Accuracy built-up (natural vs urban classes, without E)	n.a.
Accuracy weighted with LCZ metric	n.a.

Attribution

The data (i.e. the map, the original training data, and the revised training data) are distributed under the CC-BY-NC-SA license. In particular, permission is hereby granted, free of charge, to any person obtaining a copy of this data and associated documentation files, to use these data for non commercial use, subject to the following:

- Understand that these data are created by crowd sourcing and machine learning and thus will naturally contain some errors. It comes as it is without any warranty. We do not guarantee the quality and reliability of this dataset and assume no responsibility whatsoever for any direct or indirect damage and loss caused by use of this dataset or damages of users due to changing, deleting or terminating the provision of this dataset.

- This above copyright and permission notice shall be included in all copies or substantial portions of the data. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

- The authors are acknowledged appropriately and changes must be indicated. The persons referred to as authors contributed the original training data, which has been modified and complemented during the WUDAPT Level 0 quality assessment process (Bechtel et al. in review) by the person called reviewer and the WUDAPT team in general. This resulted in the revised training data, which was used together with USGS Landsat data and SAGA GIS to create the map using the method by Bechtel et al. (2015).

- For scientific use the following papers are cited:

o Bechtel B, Daneke C (2012) Classification of Local Climate Zones based on multiple Earth Observation data. IEEE Journal of elected Topics in Applied Earth Observations and Remote Sensing 5:11911202

o Bechtel B, Alexander PJ, Böhner J, Ching J, Conrad O, Feddema J, Mills G, See L, Stewart I (2015) Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities. ISPRS International Journal of Geo-Information 4:199219

o Bechtel B, Alexander PJ, Beck C, Brousse O, Ching J, Demuzere M, Gal T, Hidalgo J, Hoffmann P, Middel A, Mills G, Ren C, See L, Sismanidis P, Verdonck ML, Xu G, XU Y (in review) Generating WUDAPT Level 0 data current status of production and evaluation. Urban Climate

o 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, Bhalachandram S, Masson V, Hidalgo J, Fung J, Fatima-Andrade M, Baklanov A, Wei Dai D, Milcinski G, Demuzere M, Brunsell N, Pesaresi M, Miao S, Mu Q, Chen F, Theeuwes N (2018) World Urban Database and Access Portal Tools (WUDAPT), an urban weather, climate and environmental modeling infrastructure for the Anthropocene. Bulletin of the American Meteorological Society accepted.

o Stewart ID, Oke TR (2012) Local Climate Zones for Urban Temperature Studies. Bulletin of the American Meteorological Society 93:18791900