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README.md

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+---
+language: en
+license: mit
+tags:
+- change-detection
+- remote-sensing
+- eo-sar
+- pytorch
+- segmentation
+datasets:
+- doron333/change-detection-dataset
+metrics:
+- iou
+- precision
+- recall
+- f1
+---
+
+# Binary Change Detection (EO-SAR Fusion)
+
+This model is a **Siamese UNet** designed for binary change detection using fused Electro-Optical (EO) and Synthetic Aperture Radar (SAR) satellite imagery. 
+
+## Model Description
+The architecture uses dual weight-shared **ResNet-34** encoders to extract multi-modal features from pre-event RGB (EO) and post-event grayscale (SAR) images. Feature differences are fused via skip connections into a UNet decoder.
+
+- **Encoder:** ResNet-34 (Pre-trained on ImageNet)
+- **Loss Function:** Combined Focal Loss + Dice Loss (Optimized for 58:1 class imbalance)
+- **Input Resolution:** 256x256
+
+## Intended Uses
+This model is intended for rapid disaster damage assessment where pre-event optical data and post-event SAR data are available.
+
+## Training Results (Validation Set)
+- **IoU:** ~27.73%
+- **F1 Score:** ~43.42%
+- **Precision:** ~35.79%
+- **Recall:** ~55.18%
+
+## How to Use
+The model expects a 3-channel EO image (Pre-event) and a 1-channel SAR image (Post-event) as inputs.