Estimating Small River Widths in Complex Terrain Using Remote Sensing

Files and Descriptions:

1. TP_Lake.csv: This CSV file contains identified lakes in the Tibetan Plateau.

2. TP_River_Monthly_Statistics.csv: Monthly statistics for river data on the Tibetan Plateau, including estimations like active channel percentage and width for different river orders.

3. S2RiverWidth.py: The Python script that contains the main code for river width estimation model. This script includes the functions for preprocessing Sentinel-2 images and predicting river widths.

4. best_model_vCloud10.pth: The pre-trained deep learning model weights used for river width estimation. This model is a ResNeXt model fine-tuned on our dataset. It accepts Sentinel-2 TOA image with cloud percentage <10% (SCL).

5. Example.tif: A Sentinel-2 TOA image in .tif format, used for testing the river width estimation model.

Model Input Requirements:The model requires a Sentinel-2 TOA image in .tif format as input. The image should be scaled by a factor of 10,000 (with reflectance values range from 0 to 1). The `get_model_input` function automatically crops the image to 224x224 pixels around the center to fit the model's input requirements.