update README

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+  - en
+task_categories:
+  - image-segmentation
+tags:
+  - remote-sensing
+  - aerial-imagery
+  - change-detection
+  - building-change
+  - high-resolution
+size_categories:
+  - 1K<n<10K
+dataset_name: ValaisCD
+---
+# ValaisCD Dataset
+High-Resolution Aerial Change Detection (Switzerland, 2017–2023) 
+
+**Project page:** https://manonbechaz.github.io/2Player/
+
+---
+
+## 🗺️ Overview
+
+**ValaisCD** is a high-resolution change detection dataset built from **SwissTopo SWISSIMAGE 10 cm aerial imagery**, covering several urban and peri-urban regions of the canton of *Valais*, Switzerland.  
+It provides pairs of aerial images captured in **2017** and **2023**, along with **automatically generated building-change labels** derived from the *SwissTLM3D* topographic vector database.
+
+---
+
+## 📦 Data Summary
+
+- **Source imagery:**  
+  - SWISSIMAGE 10 cm aerial photographs  
+  - Original resolution: **0.1 m/pixel**  
+  - Downsampled to **0.5 m/pixel** for dataset construction  
+- **Geographic coverage:**  
+  - Martigny  
+  - Sion  
+  - Sierre  
+  - Brig  
+  (all located in the canton of Valais, Switzerland)
+- **Temporal coverage:**  
+  - 2017 and 2023
+- **Patch extraction:**  
+  - Original tiles: 10,000 × 10,000 pixels  
+  - Converted into **non-overlapping 256 × 256 pixel patches**  
+- **Label source:**  
+  - Building-change annotations derived from the **SwissTLM3D** landscape model  
+  - Labels correspond to differences between 2017 and 2023 building footprints  
+- **Task:** Binary change detection (building changes)
+
+---
+
+## 🧹 Dataset Pruning & Quality Filtering
+
+Despite the detailed SwissTLM3D vector labels, some discrepancies remain due to temporal misalignment between image capture and map updates.
+
+To address this, ValaisCD introduces an **automatic pruning pipeline** that filters out mislabeled or noisy samples:
+
+1. Train a supervised CD model (**FC-Siam-Diff**) on a **development subset**.
+2. Run the model on the rest of the dataset.
+3. Identify samples with significant false positives or false negatives.
+4. Remove these samples, under the assumption that they contain annotation errors.
+5. Preserve a fixed ratio of **5% changed samples** to ensure distributional balance.
+6. Apply the filtering independently to changed and unchanged samples.
+
+### 📍 Development region  
+- **Brig** is used as the development set for training the filtering model.
+
+### 📍 Final geographic split (after pruning)  
+- **Train:** Sion (70%)  
+- **Validation:** Sierre (20%)  
+- **Test:** Martigny (10%)
+
+This split ensures **geographical independence** and avoids spatial leakage.
+
+---
+
+## 🔍 How Pruning Quality Was Evaluated
+
+Dataset quality was evaluated indirectly:
+
+- Train FC-Siam-Diff models on datasets with progressively more aggressive pruning.  
+- Evaluate all models on a fixed test set.  
+- Observe performance trends as noisy samples are removed.
+
+### Findings:
+
+- Moderate pruning **improves model performance**, confirming removal of mislabeled or ambiguous samples.  
+- Excessive pruning **hurts performance**, due to loss of informative examples.  
+- Optimal size for ValaisCD is **5,000 samples**, balancing label quality and dataset sufficiency.
+
+This curated version (5000 samples) is the official version used in the associated paper.
+
+---
+
+## 📁 Dataset Structure
+
+ValaisCD/
+│
+├── train/
+│   ├── 2017/                 # images at t0
+│   ├── 2023/                 # images at t1
+│   ├── labels/            # binary change masks
+│   │
+│   ├── test_imgs_filtered_1000_2.pkl          # List of images kept in different pruned versions
+│   ├── test_imgs_filtered_2000_2.pkl             
+│   ├── test_imgs_filtered_5000_2.pkl             # This is the version used in the paper
+│   └── test_imgs_filtered_10000_2.pkl            
+│   
+│
+├── val/
+│   ├── 2017/
+│   ├── 2023/
+│   ├── labels/
+│   │
+│   ├── test_imgs_filtered_1000_2.pkl
+│   ├── test_imgs_filtered_2000_2.pkl             
+│   ├── test_imgs_filtered_5000_2.pkl             # This is the version used in the paper
+│   └── test_imgs_filtered_10000_2.pkl             
+│ 
+├── val/
+│   ├── 2017/
+│   ├── 2023/
+│   ├── labels/
+│   │
+│   ├── test_imgs_filtered_1000_2.pkl
+│   ├── test_imgs_filtered_2000_2.pkl             
+│   ├── test_imgs_filtered_5000_2.pkl             # This is the version used in the paper
+│   └── test_imgs_filtered_10000_2.pkl             
+
+│
+└── dev/
+    ├── 2017/
+    ├── 2023/
+    └── labels/
+
+