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

This PR significantly improves the dataset card for GTPBD by:
- Adding `task_categories: ['image-segmentation', 'object-detection']` to the metadata for better discoverability.
- Incorporating direct links to the official paper ([https://huggingface.co/papers/2507.14697](https://huggingface.co/papers/2507.14697)) and the GitHub repository ([https://github.com/Z-ZW-WXQ/GTPBD/](https://github.com/Z-ZW-WXQ/GTPBD/)) after the main dataset title.
- Integrating the detailed "Statistics" section from the GitHub README to provide more context on geographic coverage, area, and label diversity.
- Expanding the "Multi-Task Benchmarks" information by including the detailed "Tasks & Benchmarks" section from the GitHub README, which lists models and metrics for each task. The brief task list within "What's Included in `GTPBD`" has been removed to avoid redundancy.
- Adding a "Sample Usage" section with instructions on how to download the dataset using `git lfs clone`, as found in the GitHub README.

These updates aim to make the dataset more accessible, understandable, and aligned with Hugging Face Hub best practices, without altering the crucial initial announcements and licensing information provided by the original authors.

README.md

@@ -1,6 +1,10 @@
 ---
 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
@@ -46,6 +50,9 @@ You may **not** use the dataset or any part of it for commercial purposes. Pleas
 ---
 # 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:
@@ -55,6 +62,14 @@ We are pleased to introduce **GTPBD**, the updated and enhanced version of our g
 - 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:
@@ -73,16 +88,45 @@ These metadata files allow users to reference or re-acquire the corresponding sa
   - 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:
@@ -105,9 +149,4 @@ Only **five countries** currently do **not** include the imagery due to licensin
 - 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**