Local Climate Zone Mapping as Remote Sensing Scene Classification Using Deep Learning: A Case Study of Metropolitan China
Shengjie Liu, Qian Shi
Email: [email protected], [email protected]
This paper is accepted by ISPRS Journal P&RS. [Paper]
Free access via this link before July 4.
Preprint via this link
We've generated a new product of the whole Pearl River Delta region (9+2 cities), available here
- Used the Sentinel-2 composite images from 2018-2019 to generate the LCZ map, so the mosaic effect is reduced to a minimum.
- Included the whole administrative area of the following cities: Guangzhou, Foshan, Zhaoqing, Shenzhen, Dongguan, Huizhou, Zhongshan, Jiangmen, Zhuhai, Macau, and Hong Kong (广州，佛山，肇庆，深圳，东莞，惠州，中山，江门，珠海，澳门，香港)
Tiff file is available here (only core urban areas included)
Download Training and Testing Data from Google Drive or email me
| Region | City |
|---|---|
| The Greater Bay Area | Guangzhou, Foshan, Shenzhen, Dongguan, Huizhou, Zhuhai, Zhongshan, Jiangmen, Macau, Hong Kong 广州，佛山，深圳，东莞，惠州，珠海，中山，江门，澳门，香港 |
| The Shanghai Metropolis | Shanghai, Hangzhou, Shaoxing 上海，杭州，绍兴 |
| The Beijing Metropolis | Beijing, Tianjin, Tangshan 北京，天津，及部分唐山 |
Download Pretrained Model from Google Drive.
The size of input is with 10 channels
The channel order is (channel & Sentinel-2 band):
[0,1,2,3,4,5,6,7,8,9] = [2,3,4,5,6,7,8,11,12,8A]
Please use the following normalization when applying the model.
The subimage should be
import keras
modelfile = 'modelpath'
model = keras.models.load_model(modelfile)
data = np.float32(data)
data = data/5000.0
for subimage in data:
model.predict(subimage)