lcz
Local Climate Zone Mapping as Remote Sensing Scene Classification Using Deep Learning: A Case Study of Metropolitan China
Shengjie Liu, Qian Shi
Email: sjliu.me@gmail.com, shixi5@mail.sysu.edu.cn
This paper is accepted by ISPRS Journal P&RS. [Paper]
Free access via this link before July 4.
Preprint via this link
New Data Released (2020-08-18)!
We’ve generated a new product of the whole Pearl River Delta region (9+2 cities), available here
New Features
- 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)
Pearl River Delta (The Greater Bay Area, old version)
Tiff file is available here (only core urban areas included)
Training and Testing Data
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 |
Load a pretrained model
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)