The document discusses the classification of local climate zones using remote sensing images and convolutional neural networks (CNN) to improve accuracy. It presents various accuracy statistics for different schemes and methods, showing that the implementation of neighborhood information significantly enhances classification performance. The results indicate that CNN can achieve higher overall accuracy compared to random forests (RF) in urban settings across multiple cities.

1. Local Climate Zone classification
using remote sensing Images with
convolutional neural networks
Cheolhee Yoo, Daehyeon Han, Jungho Im, benjamin bechtel

2. OA: Overall accuracy
OAurb : Overall accuracy of built series
Scheme
Rome Chicago Madrid
OA (%) OAurb (%) Kappa OA (%) OAurb (%) Kappa OA (%) OAurb (%) Kappa
(a) Not considering neighborhood information
S1 (RF) 72.42 69.01 0.67 85.18 81.71 0.82 75.49 78.51 0.71
S2 (RF) 73.15 69.58 0.68 87.73 85.54 0.85 76.49 81.19 0.72
S3 (CNN) 76.67 75.80 0.72 87.83 85.93 0.85 77.50 81.82 0.74
(b) Considering neighborhood information
S4 (RF) 75.38 73.90 0.70 89.63 88.73 0.87 77.02 81.72 0.73
S5 (CNN) 83.54 85.88 0.80 91.42 90.97 0.90 81.56 86.11 0.78
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4. Bechtel, et al (2015). Mapping local climate zones for a worldwide database of the form and
function of cities. ISPRS International Journal of Geo-Information, 4(1), 199-219.
Danylo, et al(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, 9(5), 1841-1853.
Verdonck, et al(2017). Influence of neighbourhood information on ‘Local Climate Zone’mapping in
heterogeneous cities. International Journal of Applied Earth Observation and Geoinformation, 62, 102-113.
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5. * Indicates the stratified cross validated Polygon

6. Producer’saccuracy
CNN

7. Producer’saccuracy
CNN

8. Producer’saccuracy
CNN

9. User’saccuracy
CNN

10. Rome

12. Madrid