A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere

Author: Yinxia Cao, Qihao Weng* | Paper link | Date: August 2024 | Journal: Remote Sensing of Environment

Dataset

Download link in google drive, only with samples or Onedrive, full data. The total size is 2.72G
Unzip them to a path (e.g., data)
Split dataset into train/val/test set, see the directory data The specific spliting file is put in BH_dataset.py
Obtain the statistics of the dataset, see the directory datasteglobe The specifi file is stats_dataset_globe.py
Distribution of the dataset (45,000 samples)

original building height data: onedrive
original sentinel-1/2 data for sampling construction:
They were downloaded from GEE for each image patch (640 x 640 m), and therefore there is no orginal data.Just see 1. the pre-processed data.

Necessary libraries were put in file requirements.txt

python train.py

weights of the super-resolution module: weights/realesrgan
weights of the proposed method for height estimation: weights/realesrgan_feature_aggre_weight_globe
Results on testing set:

Basic information
For large metropolitan areas, the file name is xx_large, while for metropolitan areas, the file name is xx_metro.
Data and predicted results: see onedrive
The mean and std of each urban center is put in datasetglobe/urbanarea_meanstd.xls and the results are put in:
Predicting by yourself
- Download and put the origin data (S1&S2) in the current directory data/urban/input_data
- Prepare xx_grid.shp as the prediction unit
  - Download WSF (world settlement footprint) 2019 at 10-m resolution as the valid built-up areas, see the official website
  - Clip WSF to the extent of each urban center
  - Convert the shapefile of urban centers into rasters
  - Split each urban center into grid with size of 640 x 640 m
  - Select the grid with wsf > 20 pixels
- Predict all the urban centers

python predict_realesanet_feature_globe.py

Results:
- Distribution
- The mean and std of building height in each urban center