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

1. pre-processed data

• 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)

2. the original data

• 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.

Method

Setup environments

Necessary libraries were put in file requirements.txt

Training & Testing

python train.py

Pretrained weights and test results

See onedrive

• 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:

Testing on 301 urban centers

• 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

Predict other regions

• Download sentinel-1 & sentinel-2 from the GEE
• Download WSF

Other files

• Download sentinel-1 & sentinel-2 from the GEE platform coming soon