Duplicate from wxqzzw/GTD

Co-authored-by: Zhang Zhiwei <wxqzzw@users.noreply.huggingface.co>

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GTPBD_enhenced_png.zip

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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
+Our paper, **"GTPBD: A Fine-Grained Global Terraced Parcel and Boundary Dataset"**, has been officially accepted by **NeurIPS 2025**!  
+The camera-ready version and the arXiv version will be released by **October 23, 2025**.  
+
+## 📌 Before You Use GTPBD
+If you plan to use the **GTPBD dataset**, please make sure to **carefully read the following notes and announcements**.  
+They include important information about dataset versions, imagery sources, licensing compliance, and access instructions.
+
+👉 This will help you correctly use the dataset, avoid common misunderstandings, and ensure compliance with data usage policies.
+
+## ❗ Important Notice on Imagery and Google Earth TOS
+To ensure full compliance with Google Earth’s Terms of Service:  
+- **No Google Earth imagery** is distributed in this dataset.  
+- For the small subset where GE was used, only **metadata (`.xml`) files** are provided.  
+- These `.xml` files include geospatial info (coordinates, projection, extent), enabling users to reproduce the dataset legally.  
+
+This approach guarantees **reproducibility** while respecting **data licensing policies**.  
+
+## 📦 Dataset Update
+- The dataset has been reconstructed for **higher quality and usability**.  
+- The new release is **`GTPBD_enhenced_png`**, where most Google Earth imagery has been replaced with **GF-2 satellite imagery**.  
+- Compared with the earlier `GTPBD_PNG` version, this update ensures improved consistency and reliability.  
+
+### 🌍 Current Coverage
+- **Replaced with GF-2 imagery**: majority of regions.  
+- **Still using Google Earth (GE) imagery**: *South Africa, Brazil, Morocco, Nigeria, Tunisia*.  
+  - For these five countries, we only provide **`.xml` metadata files** under the `Rest_of_world/` directory.  
+  - Users can retrieve the corresponding imagery themselves via **Google Earth Pro** or other licensed high-resolution providers.  
+
+## 📌 License and Usage
+
+This dataset is released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.
+
+You are free to:
+
+- Use and modify the annotation data for **non-commercial research and education**
+- Share adapted versions of the data **with attribution**
+
+You may **not** use the dataset or any part of it for commercial purposes. Please contact us if you are unsure whether your intended use qualifies.
+
+---
+# 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:
+
+- Semantic Segmentation  
+- Edge Detection  
+- 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:
+
+- ✅ Expanded annotation coverage, supporting more diverse terrain types and regions  
+- ✅ Three-level manual labels: **parcel_labels**, **boundary_labels**, and **mask_labels**  
+- ✅ `.xml` geospatial metadata files for each image, including:
+  - Geographic coordinates (bounding box)
+  - Spatial resolution
+  - Projection information and image footprint  
+These metadata files allow users to reference or re-acquire the corresponding satellite images for further geospatial analysis.
+
+- **🌍 Global Scope**: 47,537 high-resolution samples from China, Vietnam, Tunisia, Ethiopia, Mexico, Peru, and more  
+- **🌾 Terraced Focus**: 200,000+ annotated parcels with irregular, stepped, and shadowed structures  
+- **🧩 Multi-Level Labels**:
+  - Parcel Mask Labels  
+  - Pixel-Level Boundary Labels  
+  - Instance-Level Parcel Labels  
+- **📑 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:
+
+![GTPBD Directory Structure](dataset_structure.png)
+
+- **Regional Subfolders**  
+  - *Central China, East China, North China, Northeast China, Northwest China, South China, Southwest China*: Seven major Chinese agricultural regions.  
+  - *Rest of the world*: Includes other international samples (e.g., South Africa, Brazil, Morocco, Nigeria, Tunisia).
+---
+
+
+## 📥 Need the Full `.tif` Satellite Imagery?
+
+Most regions in **`GTPBD_enhenced_png`** already include the bundled **PNG** imagery.  
+Only **five countries** currently do **not** include the imagery due to licensing constraints:
+**South Africa, Brazil, Morocco, Nigeria, Tunisia**.
+
+- For these countries, we provide **`.xml` metadata** under `Rest_of_world/` so you can legally retrieve the corresponding images from **Google Earth Pro** or other **licensed high-resolution providers**.
+- 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**