Improve dataset card: Add paper/code links, task categories, statistics, and sample usage

README.md

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 ---
 license: cc-by-nc-4.0
+task_categories:
+- image-segmentation
+- object-detection
 ---
+
 # ❗ Latest Announcement: GTPBD Dataset & Paper (Updated on October 9th, 2025)
 
 ## 🎉 NeurIPS 2025 Acceptance
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 # GTPBD: Global Terraced Parcel and Boundary Dataset (Updated Version)
 
+Paper: [GTPBD: A Fine-Grained Global Terraced Parcel and Boundary Dataset](https://huggingface.co/papers/2507.14697)
+Code: [https://github.com/Z-ZW-WXQ/GTPBD/](https://github.com/Z-ZW-WXQ/GTPBD/)
+
 We are pleased to introduce **GTPBD**, the updated and enhanced version of our global terraced agricultural parcel dataset. This release supports fine-grained segmentation, edge detection, and geospatial learning tasks across diverse, often mountainous terrains where terrace farming is practiced.
 
 **GTPBD** (Global Terraced Parcel and Boundary Dataset) is the first fine-grained benchmark dataset tailored for agricultural parcel extraction in **global terraced regions**. Unlike prior datasets focused on flat or regular farmlands, GTPBD specifically captures **complex terrain types**, providing **multi-level annotations** and supporting **multiple remote sensing tasks**, including:
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 - Parcel Extraction  
 - Unsupervised Domain Adaptation (UDA)  
 
+# Statistics
+
+*   **Geographic Coverage**: 7 major zones in China + global transcontinental regions
+*   **Area Covered**: 885 km² of annotated terraces
+*   **Label Diversity**:
+    *   3-level labels (mask, boundary, parcel)
+    *   Complex topologies (shared vs. non-shared field ridges)
+
 ## 📦 What's Included in `GTPBD`
 
 The current release provides a refined subset of the dataset in `.png` format, named `GTPBD_enhenced_png`. It includes:
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   - Parcel Mask Labels  
   - Pixel-Level Boundary Labels  
   - Instance-Level Parcel Labels  
-- **🔧 Multi-Task Benchmarks**:
-  - Semantic Segmentation  
-  - Edge Detection  
-  - Agricultural Parcel Extraction  
-  - Unsupervised Domain Adaptation (UDA)
-
 - **📑 Data augmentation for improved generalization in terraced parcel learning**:  
   - Images with **`rot`** in their filenames are **rotated versions** of the originals  
   - Images with **`flip`** in their filenames are **flipped versions** of the originals
 
+# Tasks & Benchmarks
+
+### 1. Semantic Segmentation
+
+*   **Models**: U-Net, DeepLabV3, PSPNet, SegFormer, Mask2Former, etc.
+*   **Metrics**: IoU, Pixel Accuracy, F1-score, Recall, Precision
+
+### 2. Edge Detection
+
+*   **Models**: UEAD, MuGE, PiDiNet, REAUNet-Sober
+*   **Metrics**: ODS, OIS, AP
+
+### 3. Agricultural Parcel Extraction
+
+*   **Models**: HBGNet, SEANet, REAUNet
+*   **Metrics**: OA, IoU, F1, plus object-level:
+    *   GOC (Over-Classification Error)
+    *   GUC (Under-Classification Error)
+    *   GTC (Total Classification Error)
+
+### 4. Unsupervised Domain Adaptation (UDA)
+
+*   **Domains**: South (S), North (N), Global (G)
+*   **Transfers**: S → N, G → S, etc.
+*   **Methods**: Source Only, FDA, DAFormer, HRDA, PiPa
+
+## Sample Usage
+
+You can download the dataset directly from Hugging Face:
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/wxqzzw/GTD
+```
+
 ## 📑 Explanation of Directories
 
 The **GTPBD dataset** directory structure is illustrated below:
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 - If you **require original GeoTIFF (`.tif`)** for research use, please contact us directly.
 
 For questions, feedback, or `.tif` access support, contact:  
-📧 **zhangzhw65@mail2.sysu.edu.cn**
-
-
-
-
-
+📧 **zhangzhw65@mail2.sysu.edu.cn**