---
language:
  - en
license: cc-by-4.0
library_name: datasets
tags:
  - remote-sensing
  - geospatial
  - dem
  - digital-elevation-model
  - lidar
  - photogrammetry
  - computer-vision
  - feature-matching
  - cross-domain
  - geotiff
  - structure-from-motion
  - satellite-stereophotogrammetry
task_categories:
  - image-feature-matching
task_ids:
  - local-feature-detection
  - local-feature-matching
  - cross-domain-matching
pretty_name: MatchGeo
size_categories:
  - 10K<n<100K
  - 1G<n<10G
---

# MatchGeo

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.21229785.svg)](https://doi.org/10.5281/zenodo.21229785)
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)

**A curated multi-region Digital Elevation Model (DEM) dataset for training and benchmarking local feature matching algorithms in urban and natural terrain analysis.**

---

## 🎯 Overview

MatchGeo aggregates high-resolution elevation data from **13 distinct environments across 6 continents** to support research in cross-domain local feature detection and matching. The dataset provides standardised 256x256-pixel patches with handcrafted and automated ground-truth annotations for training computer vision models on geospatial data.

### Key Features

- **Multi-source fusion**: LiDAR, photogrammetry, Structure-from-Motion (SfM), and satellite stereophotogrammetry
- **Global coverage**: 13 regions across 6 continents — from Antarctica to the Sahara
- **Standardised format**: All cities processed to 256x256 pixel Cloud Optimized GeoTIFF (COG) tiles
- **Rich annotations**: 40,287 verified patch pairs (GER_BN and BRA_SP)
- **Cross-area evaluation**: Explicit intra-region and inter-region test splits
- **FAIR compliant**: ISO 19115-2 metadata, DOI registration, open access

---

## 🗺️ Dataset Coverage

| Region | Country | Acquisition | Resolution | Year | Terrain | Labelled | N Tiles | N Labelled Tiles |
|------|---------|-------------|------------|------|---------|----------|---------|------------------|
| Antarctic Peninsula (ATA_MV) | Antarctica | REMA (Satellite) | 2.0 m | 2009–2024 | Polar, ice | ❌ | 2,760 | 0 |
| São Paulo (BRA_SP) | Brazil | Airborne LiDAR | 0.5 m | 2020 | Urban | ✅ | 895 | 821 |
| Wutai Shan (CHN_WS) | China | UAV SfM | 0.67 m | 2021 | Mountainous | ❌ | 1,784 | 0 |
| El Hierro (ESP_EH) | Canary Islands (Spain) | Airborne LiDAR | 0.5 m | 2022–2025 | Volcanic, coastal | ❌ | 4,074 | 0 |
| Lahti Lake (FIN_LM) | Finland | LiDAR + Photogrammetry | 2.0 m | 2020–2026 | Temperate, country | ❌ | 400 | 0 |
| Bonn (GER_BN) | Germany | Airborne LiDAR | 1.0 m | 2016–2018 | Temperate, country | ✅ | 1,857 | 369 |
| Sinabung Volcano (IDN_SV) | Indonesia | UAS SfM | 0.87 m | 2018 | Volcanic, tropical | ❌ | 301 | 0 |
| Almaty City (KAZ_AC) | Kazakhstan | Pleiades Tristereo | 1.5 m | 2017 | Semi-arid, urban | ❌ | 1,698 | 0 |
| Wadi Al-Akhdar (KSA_WA) | Saudi Arabia | SPOT 6 Stereo | 1.6 m | 2016 | Desert, graben | ❌ | 6,521 | 0 |
| Hebron Fault (NAM_HF) | Namibia | WorldView-3 Stereo | 0.53 m | 2017 | Arid, fault zone | ❌ | 2,405 | 0 |
| Kapiti Coast (NZL_KP) | New Zealand | Airborne LiDAR | 1.0 m | 2010–2025 | Coastal, temperate, country | ❌ | 3,087 | 0 |
| Tarlac (PHL_TA) | Philippines | Airborne LiDAR | 1.0 m | 2014–2017 | Tropical | ❌ | 462 | 0 |
| Grand Canyon (USA_GC) | United States | LiDAR | 0.5 m | 2020–2026 | Desert, canyon | ❌ | 1,024 | 0 |

**Total Size**: ~12.5 GB  
**Total Tiles**: 27,268 (256x256 px patches)  
**Labelled Tiles**: 1,190 (369 Bonn + 821 São Paulo)  
**Ground Truth Annotations**: 40,287 handcrafted point annotations (Bonn + São Paulo)

---

## 📊 Per-Region Statistics

| Key ID | Area name | Source | EPSG | Area (km²) | Min Height (m) | Max Height (m) | Height range (m) |
|--------|-----------|--------|------|------------|----------------|----------------|------------------|
| ATA_MV | Antarctica | REMA | EPSG:3031 | 711.37 | –55.00 | 375.32 | 430.49 |
| BRA_SP | Brazil | GeoSampa | EPSG:31983 | 15.49 | 708.46 | 995.03 | 286.57 |
| CHN_WS | China | OpenTopography | EPSG:32649 | 105.00 | 1,338.90 | 2,181.95 | 843.05 |
| ESP_EH | Canary Islands | PNOA-LiDAR | EPSG:3040 | 156.00 | 2.00 | 1,191.39 | 1,189.39 |
| FIN_LM | Finland | NLS Finland | EPSG:3067 | 98.00 | 64.38 | 403.79 | 342.42 |
| GER_BN | Germany | Geobasis NRW | EPSG:25832 | 135.58 | 31.73 | 390.35 | 358.62 |
| IDN_SV | Indonesia | OpenTopography | EPSG:32647 | 17.67 | 1,100.06 | 2,385.18 | 1,185.11 |
| KAZ_AC | Kazakhstan | OpenTopography | EPSG:32643 | 247.47 | 594.71 | 1,660.38 | 1,065.57 |
| KSA_WA | Saudi Arabia | OpenTopography | EPSG:32637 | 1,260.57 | 856.20 | 1,457.76 | 601.56 |
| NAM_HF | Namibia | OpenTopography | EPSG:32733 | 77.48 | 861.89 | 1,251.90 | 390.01 |
| NZL_KP | New Zealand | LINZ | EPSG:2193 | 195.52 | 95.16 | 1,609.97 | 1,514.81 |
| PHL_TA | Philippines | LiPAD | EPSG:32651 | 30.04 | 22.09 | 401.14 | 379.05 |
| USA_GC | United States | USGS 3DEP | EPSG:6341 | 16.20 | 461.76 | 1,384.25 | 922.49 |

---

## 📁 Repository Structure

```
MatchGeo-DEM-v1/
├── README.md                          # This file
├── DATASET_DESCRIPTION.md             # FAIR-compliant formal description
├── LICENSE                            # CC BY 4.0 full legal text
├── CITATION.cff                       # Machine-readable citation
├── manifest.json                      # Central catalog (JSON-LD)
├── checksums.sha256                   # File integrity verification
│
├── data/
│   ├── ATA_MV/
│   │   ├── ATA_MV.tif                 # Merged DEM (BigTIFF, tiled, DEFLATE)
│   │   ├── ATA_MV_extent.geojson      # Bounding polygon
│   │   ├── ATA_MV_tiles.geojson       # Tile index
│   │   ├── ATA_MV_metadata.json       # ISO 19115-2 + OGC 23-008r3 metadata
│   │   ├── ATA_MV.qmd                 # QGIS layer metadata
│   │   └── tiles/                     # 256x256 pixel patches
│   ├── BRA_SP/
│   │   ├── BRA_SP.tif
│   │   ├── BRA_SP_extent.geojson
│   │   ├── BRA_SP_tiles.geojson
│   │   ├── BRA_SP_metadata.json
│   │   ├── BRA_SP.qmd
│   │   ├── annotations/               # JSON keypoint files (labelled)
│   │   │   └── BRA_SP_###_###.json    # Handcrafted annotations
│   │   └── tiles/
│   ├── ... (11 more cities)
│   └── GER_BN/
│       ├── GER_BN.tif
│       ├── GER_BN_extent.geojson
│       ├── GER_BN_tiles.geojson
│       ├── GER_BN_metadata.json
│       ├── GER_BN.qmd
│       ├── annotations/               # Handcrafted annotations
│       │   └── GER_BN_###_###.json
│       └── tiles/
│
├── splits/
│   ├── train.csv                      # Tile IDs for training
│   ├── validation.csv                 # Tile IDs for validation
│   └── test.csv                       # Tile IDs for testing
│
└── scripts/
    ├── process_las.py                 # PDAL pipeline for LAS→DEM
    ├── crop_tiles.py                  # 256x256 patch extraction
    ├── fix_nodata.py                  # NoData standardization
    ├── write_qgis_metadata.py         # QGIS .qmd generator
    └── cleanup_aux_xml.py             # Remove QGIS temp files
```

---

## 📥 Download

| Repository | Link | Notes |
|------------|------|-------|
| **Zenodo (Primary)** | [https://doi.org/10.5281/zenodo.21229785](https://doi.org/10.5281/zenodo.21229785) | DOI-backed, permanent archive |
| Hugging Face Datasets | [https://huggingface.co/datasets/paeslemesa/matchgeo](https://huggingface.co/datasets/paeslemesa/matchgeo) | Streaming loader available |

### Quick Download

```bash
# Using zenodo_get (pip install zenodo_get)
zenodo_get 10.5281/zenodo.21229785
```

---

## 🎓 Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{correa_2026_matchgeo,
  author       = {Correa, S. P. L. P. and Pazini Pedro, D. F. and Oliveira, H. N. and Belton, D. and Ivánová, I and Santos, A. de Paula},
  title        = {{MatchGeo: Digital Elevation Model Dataset for Local Feature Matching}},
  year         = 2026,
  publisher    = {Zenodo},
  version      = {1.1},
  doi          = {10.5281/zenodo.21229785},
  url          = {https://doi.org/10.5281/zenodo.21229785},
  note         = {Contains data derived from REMA, GeoSampa, OpenTopography, CNIG, Maanmittauslaitos, Geobasis NRW, LINZ, LiPAD, and USGS 3DEP}
}
```

**Plain text citation:**  
Correa, S. P. L. P., Pazini Pedro, D. F., Oliveira, H. N., Belton, D., Ivánová, I., & Santos, A. de Paula. (2026). *MatchGeo: Digital Elevation Model Dataset for Local Feature Matching* (Version 1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.21229785

### Source Dataset Citations

When using specific cities, also cite the original sources (see `DATASET_DESCRIPTION.md` Section 8 for full BibTeX).

---

## 📜 License & Attribution

This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.

You are free to:
- **Share**: Copy and redistribute the material in any medium or format
- **Adapt**: Remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:
- **Attribution**: You must give appropriate credit, provide a link to the license, and indicate if changes were made

### Required Attribution Statements

When using this dataset, your publication or product must include:

1. **Dataset citation** (see [Citation](#-citation) section)
2. **Original source acknowledgments** (per region):
   - *ATA_MV: Data derived from REMA © Polar Geospatial Center / University of Minnesota*
   - *BRA_SP: Data derived from GeoSampa © Prefeitura de São Paulo*
   - *CHN_WS: Data derived from OpenTopography dataset by Zhou, C. (DOI: 10.5069/G98C9TGT)*
   - *ESP_EH: Data derived from PNOA-LiDAR © CNIG / Instituto Geográfico Nacional*
   - *FIN_LM: Data derived from Maanmittauslaitos © National Land Survey of Finland*
   - *GER_BN: Data derived from Geobasis NRW © Bezirksregierung Köln*
   - *IDN_SV: Data derived from OpenTopography dataset by Carr, B. (DOI: 10.5069/G8988568)*
   - *KAZ_AC: Data derived from OpenTopography dataset by Amey et al. (DOI: 10.5069/G9H41PMP)*
   - *KSA_WA: Data derived from OpenTopography dataset by Matthieu et al. (DOI: 10.5069/G9V40SDZ)*
   - *NAM_HF: Data derived from OpenTopography dataset by Salomon et al. (DOI: 10.5069/G9W957BC)*
   - *NZL_KP: Data derived from LINZ © Land Information New Zealand*
   - *PHL_TA: Data derived from LiPAD © UP Diliman TCAGP / DREAM Program*
   - *USA_GC: Data derived from USGS 3DEP © U.S. Geological Survey*

---

## 🏗️ Processing Pipeline

All cities were processed through a standardized PDAL pipeline:

1. **Acquisition** — Raw data from source portals (LAZ, DEM, point clouds)
2. **Preprocessing** — Region-specific filtering (ground classification, outlier removal, noise filtering)
3. **Rasterization** — PDAL `writers.gdal` with `output_type=max` (DSM)
4. **Standardization** — Float32, NoData=-9999, BigTIFF, Tiled, DEFLATE compression
5. **Patch Extraction** — Non-overlapping 256x256 pixel grid
6. **Annotation** — Handcrafted keypoints (Bonn, São Paulo)
7. **Metadata** — ISO 19115-2 + OGC 23-008r3 compliant per-region metadata

See `scripts/process_las.py` for the full PDAL pipeline.

---

## 🐛 Issues & Support

- **Bug reports**: [GitHub Issues](https://github.com/paeslemesa/matchgeo/issues)
- **Questions**: [GitHub Discussions](https://github.com/paeslemesa/matchgeo/discussions)
- **Email**: sabrina.correa@ufv.br

---

## 📅 Changelog

### v1.1 (2026-05-11)
- Expanded to 13 cities across 6 continents
- Reorganized into `data/` folder with per-region metadata
- Added QGIS `.qmd` metadata files
- Standardized NoData values to -9999.0
- Added BigTIFF, tiled, DEFLATE compression to addapt to COG files
- Updated to MatchGeo branding

### v1.0 (2026-03-30)
- Initial release
- Bonn: 20,000+ handcrafted annotations
- Sao Paulo: 10,000+ handcrafted annotations

---

## 🙏 Acknowledgments

- **Data providers**: REMA (Polar Geospatial Center / University of Minnesota), Prefeitura de São Paulo (GeoSampa), OpenTopography, CNIG (Spain), Maanmittauslaitos (Finland), Geobasis NRW (Germany), LINZ (New Zealand), UP Diliman TCAGP / DREAM (Philippines), USGS (United States)
- **Imagery providers**: CNES / Airbus DS (Pleiades), Maxar (WorldView-3), SPOT Image (SPOT 6)
- **Funding**: This project is currently funded by **CNPq (Brazil)**
- **Institutional support**: Universidade Federal de Viçosa (UFV)

---

**Maintainer**: Sabrina Correa | Universidade Federal de Viçosa | sabrina.correa@ufv.br

**Last Updated**: 2026-07-23