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matchgeodem

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metadata 
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 License: CC BY 4.0 Python 3.10+

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 DOI-backed, permanent archive
Hugging Face Datasets https://huggingface.co/datasets/paeslemesa/matchgeo Streaming loader available

Quick Download 

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

🎓 Citation

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

@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 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

📅 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