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.gitignore

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+# Byte-compiled / optimized / DLL files
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+*$py.class
+
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+
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+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Abstra
+# Abstra is an AI-powered process automation framework.
+# Ignore directories containing user credentials, local state, and settings.
+# Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#  Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#  that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#  and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#  you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
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+
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+#  Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to
+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
+#  refer to https://docs.cursor.com/context/ignore-files
+.cursorignore
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+
+# Marimo
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+
+
+# LLM
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+
+# Ignore all JSON files for github repo due to size constraints
+*.json

.hfignore

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+# Local/dev files
+.git/
+.claude/
+.gitignore
+.gitkeep
+CLAUDE.md
+firebase-debug.log
+
+# Empty placeholder files
+data/raw/
+data/images/

LICENSE

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+Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
+
+Copyright (c) 2025
+
+This work is licensed under the Creative Commons Attribution-NonCommercial 4.0
+International License.
+
+You are free to:
+
+  Share - copy and redistribute the material in any medium or format
+  Adapt - remix, transform, and build upon the material
+
+Under the following terms:
+
+  Attribution - You must give appropriate credit, provide a link to the license,
+  and indicate if changes were made. You may do so in any reasonable manner, but
+  not in any way that suggests the licensor endorses you or your use.
+
+  NonCommercial - You may not use the material for commercial purposes.
+
+  No additional restrictions - You may not apply legal terms or technological
+  measures that legally restrict others from doing anything the license permits.
+
+Notices:
+
+  You do not have to comply with the license for elements of the material in the
+  public domain or where your use is permitted by an applicable exception or
+  limitation.
+
+  No warranties are given. The license may not give you all of the permissions
+  necessary for your intended use. For example, other rights such as publicity,
+  privacy, or moral rights may limit how you use the material.
+
+For the full license text, see:
+https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+---
+
+Note: Original Chang'E-4 imagery is owned by China's Lunar and Planetary Data
+System (CLPDS) / National Astronomical Observatories of China (NAOC) and is
+subject to their usage terms. This license applies only to the segmentation
+masks and code in this repository.

README.md

@@ -1,3 +1,129 @@
----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - lunar
+  - chang-e-4
+  - terrain-classification
+  - segmentation
+  - planetary-science
+pretty_name: Chang'E-4 Terrain Classification Dataset
+---
+
+# Chang'E-4 TCM Dataset
+
+Tools and data for terrain classification using Chang'E-4 Yutu-2 rover imagery.
+
+> **Note:** This repository contains only segmentation mask annotations. Original Chang'E-4 images are not included due to copyright restrictions. You must download the source images directly from CLPDS (see instructions below).
+
+## Quick Start
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Convert PDS files to images
+python scripts/convert_pds.py data/raw data/images
+```
+
+## Downloading Chang'E-4 Data
+
+Data is available from China's Lunar and Planetary Data System (CLPDS).
+
+### 1. Register an Account
+
+1. Go to https://clpds.bao.ac.cn
+2. Create an account (check spam folder for confirmation email from NAOC)
+
+### 2. Download Data
+
+1. Navigate to Chang'E-4 data search: https://clpds.bao.ac.cn/ce5web/searchOrder-ce4En.do
+2. Select an instrument:
+   - **PCAM** - Panoramic Camera (rover, stereo pairs)
+   - **TCAM** - Terrain Camera (lander)
+   - **LPR** - Lunar Penetrating Radar
+   - **VNIS** - Visible/Near-IR Spectrometer
+3. Choose data level (L2A or higher for calibrated data)
+4. Add files to cart and process order
+5. Download and extract to `data/raw/`
+
+### Data Format
+
+Chang'E-4 uses PDS4 format:
+- XML label file (`.xml`) - metadata
+- Data file (`.img`, `*L`) - image data
+
+## Converting to Images
+
+The conversion script applies debayering and contrast stretching:
+
+```bash
+# Convert all PDS files in data/raw to PNG
+python scripts/convert_pds.py data/raw data/images
+
+# Preview files without converting
+python scripts/convert_pds.py data/raw data/images --dry-run
+
+# Flatten output to single directory
+python scripts/convert_pds.py data/raw data/images --flat
+```
+
+### Processing Steps
+
+1. Read PDS4 file using `pds4_tools`
+2. Debayer raw Bayer images (PCAM full-resolution)
+3. Apply 2% linear contrast stretch
+4. Save as PNG
+
+## Project Structure
+
+```
+├── data/
+│   ├── masks/        # Segmentation mask annotations (JSON)
+│   ├── raw/          # Downloaded PDS4 files (you provide)
+│   └── images/       # Converted PNG images (generated)
+├── docs/
+│   └── chinese-moon-data-access.md
+├── scripts/
+│   └── convert_pds.py
+└── requirements.txt
+```
+
+## References
+
+- [CLPDS Data Portal](https://clpds.bao.ac.cn)
+- [Chang'E-4 Data Releases](https://moon.bao.ac.cn/pubMsg/detail-CE4EN.jsp)
+- [CLPDS Overview Paper](https://link.springer.com/article/10.1007/s11214-021-00862-3)
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@misc{chang4tcm2025,
+  author       = {Yu, Sam and Lastname, Firstname and },
+  title        = {Chang'E-4 Terrain Classification Dataset},
+  year         = {2026},
+  organization = {Lothan Space, },
+  publisher    = {GitHub},
+  url          = {https://github.com/siyu6974/change4-tcm-dataset}
+}
+```
+
+Please also cite the original data source:
+
+```bibtex
+@misc{clpds2025,
+  author       = {{Ground Research and Application System of China's Lunar and Planetary Exploration Program}},
+  title        = {Chang'E-4 Scientific Data},
+  publisher    = {China National Space Administration},
+  url          = {https://moon.bao.ac.cn}
+}
+```
+
+## License
+
+The code and segmentation masks in this repository are licensed under [CC BY-NC 4.0](LICENSE) (Creative Commons Attribution-NonCommercial 4.0). You are free to use, share, and adapt for non-commercial purposes with attribution.
+
+Original Chang'E-4 imagery is owned by CLPDS/NAOC and must be downloaded directly from their portal. See [docs/chinese-moon-data-access.md](docs/chinese-moon-data-access.md) for their citation requirements.

data/raw/placeholder.txt

@@ -0,0 +1 @@
+Original files here

docs/chinese-moon-data-access.md

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+# Accessing Chang'E-4 Rover Data
+
+This guide explains how to access and download images and scientific data from China's Chang'E-4 mission and Yutu-2 rover.
+
+## Chang'E-4 Mission Overview
+
+Chang'E-4 achieved humanity's first soft landing on the lunar far side on January 3, 2019, landing in the Von Kármán crater. The mission includes a lander and the Yutu-2 rover, which continues to operate and explore the lunar surface.
+
+**Key Facts:**
+- **Launch**: December 8, 2018
+- **Landing**: January 3, 2019
+- **Location**: Von Kármán crater, lunar far side
+- **Rover**: Yutu-2 (玉兔二号)
+- **Data Format**: PDS4
+
+## Chang'E-4 Instruments
+
+| Instrument | Abbreviation | Description |
+|------------|--------------|-------------|
+| Landing Camera | LCAM | Descent imaging during landing |
+| Terrain Camera | TCAM | Surface imaging from lander |
+| Panoramic Camera | PCAM | 360° surface imaging from rover |
+| Lunar Penetrating Radar | LPR | Subsurface structure detection |
+| Visible and Near-Infrared Imaging Spectrometer | VNIS | Mineral composition analysis |
+| Low Frequency Spectrometer | LFS | Radio astronomy observations |
+
+## Data Portal
+
+**Primary access point for Chang'E-4 data:**
+
+- **Chang'E-4 Data Search**: https://clpds.bao.ac.cn/ce5web/searchOrder-ce4En.do
+- **Main Portal**: https://moon.bao.ac.cn/web/enmanager/home
+- **Contact**: lpdc@nao.cas.cn
+
+The data is hosted by **China's Lunar and Planetary Data System (CLPDS)**, operated by the National Astronomical Observatories of China (NAOC).
+
+## Registration Process
+
+1. **Navigate to the portal**: Go to https://clpds.bao.ac.cn
+2. **Create an account**: Click on the registration/login link
+3. **Fill in the registration form**: Provide required information
+4. **Download and submit registration form**: Some datasets require an additional form
+5. **Check your spam folder**: Confirmation emails from NAOC may be incorrectly encoded
+
+## Downloading Chang'E-4 Data
+
+The download process works like online shopping:
+
+1. **Log in** to your account
+2. **Go to Chang'E-4 search**: https://clpds.bao.ac.cn/ce5web/searchOrder-ce4En.do
+3. **Select an instrument**: PCAM, TCAM, LPR, VNIS, etc.
+4. **Choose data level**: Typically Level 2A or higher for calibrated data
+5. **Set time range** (optional): Filter by lunar day or date range
+6. **Add to cart**: Select the data products you need
+7. **Process order**: Submit to initiate download
+
+## Data Processing Levels
+
+| Level | Description | Availability |
+|-------|-------------|--------------|
+| L0 | Raw telemetry data | Not public |
+| L1 | Depacketized, time-tagged | Not public |
+| L2A | Calibrated, geolocated | Public |
+| L2B | Mosaics and derived products | Public |
+| L2C | Higher-level products | Public |
+
+**Note:** Only calibrated L2+ data is publicly available. Raw L0/L1 telemetry remains proprietary.
+
+## PDS4 Data Format
+
+Chang'E-4 data uses PDS4 format. Each data product consists of:
+
+- **XML label file** (`.xml`): Metadata describing the observation
+- **Data file**: Image or measurement data (format varies by instrument)
+
+Common image formats include `.img` or `.fits` files paired with their XML labels.
+
+## Data Releases
+
+Chang'E-4 data is released periodically in batches organized by lunar day. Check the data release announcements for the latest available data:
+
+- [Chang'E-4 Data Release Announcements](https://moon.bao.ac.cn/pubMsg/detail-CE4EN.jsp)
+
+## Usage Requirements
+
+1. **Non-commercial use**: Data is for research and educational purposes
+2. **Citation required**: Acknowledge the data source in publications
+3. **Submit results**: Send publications to lpdc@nao.cas.cn
+
+### Suggested Citation
+
+```
+Ground Research and Application System of China's Lunar and Planetary
+Exploration Program. Chang'E-4 [Instrument] Data. China National Space
+Administration, [Year]. https://moon.bao.ac.cn
+```
+
+## Troubleshooting
+
+- **Service instability**: The portal may be slow for overseas users. Retry if needed.
+- **Language**: Some pages are Chinese-only. Use browser translation.
+- **JavaScript required**: The data portal requires JavaScript enabled.
+
+## Other Chang'E Missions
+
+For context, other missions in the Chinese Lunar Exploration Program:
+
+| Mission | Year | Description |
+|---------|------|-------------|
+| Chang'E-1 | 2007 | Lunar orbiter |
+| Chang'E-2 | 2010 | Lunar orbiter, higher resolution |
+| Chang'E-3 | 2013 | Near-side lander + Yutu rover |
+| Chang'E-5 | 2020 | Sample return (1,731g) |
+| Chang'E-6 | 2024 | Far-side sample return (1,935g) |
+
+## References
+
+- [CLPDS Overview Paper (Space Science Reviews)](https://link.springer.com/article/10.1007/s11214-021-00862-3)
+- [Planetary Society Guide to Downloading Chang'e Data](http://www.planetary.org/blogs/guest-blogs/2016/01221450-china-invites-public-on-board.html)
+- [Chang'E-4 Data Release Announcement](https://moon.bao.ac.cn/pubMsg/detail-CE4EN.jsp)

requirements.txt

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+pds4-tools>=1.3
+colour-demosaicing>=0.2
+colour-science>=0.4
+scikit-image>=0.21
+Pillow>=10.0
+numpy>=1.24

scripts/convert_pds.py

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+#!/usr/bin/env python3
+"""
+Convert Chang'E-4 PDS4 image files to PNG format.
+
+Usage:
+    python scripts/convert_pds.py data/raw data/images
+
+This script reads PDS4 files from the input directory, applies debayering
+and contrast stretching, then saves the results as PNG images.
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+from pds4_tools import pds4_read
+from skimage import exposure
+from skimage.util import img_as_float
+from colour_demosaicing import demosaicing_CFA_Bayer_Menon2007
+import colour
+
+
+def read_pds(path: Path) -> np.ndarray:
+    """Read a PDS4 image file and return as float array."""
+    data = pds4_read(str(path), quiet=True)
+    img = np.array(data[0].data)
+    return img_as_float(img)
+
+
+def debayer(img: np.ndarray, pattern: str = "RGGB") -> np.ndarray:
+    """Convert Bayer pattern image to RGB."""
+    debayered = demosaicing_CFA_Bayer_Menon2007(img, pattern)
+    return colour.cctf_encoding(debayered)
+
+
+def stretch(img: np.ndarray, percentile: float = 2.0) -> np.ndarray:
+    """Apply linear contrast stretch using percentile clipping."""
+    p_low, p_high = np.percentile(img, (percentile, 100 - percentile))
+    return exposure.rescale_intensity(img, in_range=(p_low, p_high))
+
+
+def save_image(img: np.ndarray, path: Path) -> None:
+    """Save float image array as PNG."""
+    img_uint8 = np.uint8(np.clip(img, 0, 1) * 255)
+    Image.fromarray(img_uint8).save(path)
+
+
+def convert_pds_file(input_path: Path, output_path: Path) -> bool:
+    """
+    Convert a single PDS file to PNG.
+
+    Returns True on success, False on failure.
+    """
+    try:
+        img = read_pds(input_path)
+
+        # PCAM full resolution images need debayering
+        # Dimensions: 1728x2352 for raw Bayer, 864x1176 for already processed
+        if img.shape == (1728, 2352):
+            img = debayer(img)
+        elif len(img.shape) == 2 and img.shape not in [(864, 1176)]:
+            # Unknown grayscale format, try debayering if dimensions suggest Bayer
+            if img.shape[0] > 500 and img.shape[1] > 500:
+                img = debayer(img)
+
+        img = stretch(img)
+        save_image(img, output_path)
+        return True
+
+    except Exception as e:
+        print(f"Error converting {input_path}: {e}", file=sys.stderr)
+        return False
+
+
+def find_pds_files(directory: Path) -> list[Path]:
+    """Find all PDS4 image files in directory (recursively)."""
+    pds_files = []
+
+    # PDS4 data files typically end with these extensions
+    patterns = ["*.*L", "*.img", "*.IMG", "*.dat", "*.DAT"]
+
+    for pattern in patterns:
+        pds_files.extend(directory.rglob(pattern))
+
+    # Filter out XML label files
+    pds_files = [f for f in pds_files if not f.suffix.lower() == ".xml"]
+
+    return sorted(set(pds_files))
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Convert Chang'E-4 PDS4 files to PNG images",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+    python scripts/convert_pds.py data/raw data/images
+    python scripts/convert_pds.py ./PCAM ./output --flat
+        """,
+    )
+    parser.add_argument(
+        "input_dir",
+        type=Path,
+        help="Directory containing PDS4 files",
+    )
+    parser.add_argument(
+        "output_dir",
+        type=Path,
+        help="Directory for output PNG images",
+    )
+    parser.add_argument(
+        "--flat",
+        action="store_true",
+        help="Output all files to single directory (ignore subdirectory structure)",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="List files that would be converted without converting",
+    )
+
+    args = parser.parse_args()
+
+    if not args.input_dir.exists():
+        print(f"Error: Input directory does not exist: {args.input_dir}", file=sys.stderr)
+        sys.exit(1)
+
+    pds_files = find_pds_files(args.input_dir)
+
+    if not pds_files:
+        print(f"No PDS files found in {args.input_dir}")
+        sys.exit(0)
+
+    print(f"Found {len(pds_files)} PDS files")
+
+    if args.dry_run:
+        for f in pds_files:
+            print(f"  {f}")
+        sys.exit(0)
+
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    success_count = 0
+    fail_count = 0
+
+    for pds_file in pds_files:
+        # Determine output path
+        if args.flat:
+            output_path = args.output_dir / f"{pds_file.stem}.png"
+        else:
+            # Preserve subdirectory structure
+            rel_path = pds_file.relative_to(args.input_dir)
+            output_path = args.output_dir / rel_path.with_suffix(".png")
+            output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        print(f"Converting: {pds_file.name} -> {output_path.name}")
+
+        if convert_pds_file(pds_file, output_path):
+            success_count += 1
+        else:
+            fail_count += 1
+
+    print(f"\nDone: {success_count} converted, {fail_count} failed")
+
+
+if __name__ == "__main__":
+    main()