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.gitattributes

@@ -619,3 +619,4 @@ dataset/Zmutt/20231028_Zmutt_hillshade.tif filter=lfs diff=lfs merge=lfs -text
 dataset/Zmutt/20231028_Zmutt_s1.tif filter=lfs diff=lfs merge=lfs -text
 dataset/Zmutt/20231028_Zmutt_s2.tif filter=lfs diff=lfs merge=lfs -text
 dataset/Zmutt/Zmutt_dem.tif filter=lfs diff=lfs merge=lfs -text
+figures/VV-3dB-25.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -21,6 +21,11 @@ backscatter with a 3 dB threshold and a 25\% wet snow percentage threshold.
 This method achieved high accuracy, identifying the start of snowmelt within an average
 of 6.5 days compared to in-situ physical simulations.
 
+Results Example:
+
+![Melting Season Assessment](./figures/VV-3dB-25.png)
+
+
 Second, Glacial Lake Segmentation was performed using deep
 learning-based semantic segmentation models. These models integrated multi-source data,
 including Sentinel-1 (radar), Sentinel-2 (optical), and Digital Elevation Models
@@ -33,6 +38,10 @@ accurate segmentation of early-stage lakes.
 Overall, the work demonstrates that advanced deep learning models
 outperform traditional machine learning methods for glacial lake mapping.
 
+The best performing model (SwinV2-CNN U-Net) architecture:
+
+![Lake Segmentation Example](./figures/swinv2cnn.png)
+
 
 ## Repository Structure
 - `dataset/`: Contains the data for the Deep Learning models, including satellite images and corresponding labels.