paeslemesa/matchgeodem

MatchGeo v1.3

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

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🎯 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

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🗺️ 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)

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📊 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

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📁 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

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📥 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

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🎓 Citation

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

@dataset{correa_2026_matchgeo_tdml,
  author       = {Correa, Sabrina Paes Leme P. and Panzini, D. and Oliveira, H. N. and Belton, D. and Iv'{a}nov'{a}, I. and Santos, A. de Paula},
  title        = {{MatchGeo: Digital Elevation Model Dataset for Local Feature Matching (TrainingDML-AI Compliant)}},
  year         = {2026},
  publisher    = {Zenodo},
  version      = {1.3},
  doi          = {10.5281/zenodo.21229785},
  url          = {https://doi.org/10.5281/zenodo.21229785},
  note         = {OGC TrainingDML-AI (23-008r3 / 24-006r1) compliant metadata. 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 1.3) [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).

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📜 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

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🏗️ 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 TDML compliant per-region metadata

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

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🐛 Issues & Support

• Bug reports: GitHub Issues
• Questions: GitHub Discussions
• Email: sabrina.correa@ufv.br

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📅 Changelog

v1.3 (2026-07-24)

• Updated annotation structure according to OGC TDML
• Modified metadata to be in accordance to OGC TDML

v1.2 (2026-07-22)

• Adjusted metadata according to FAIR principles
• Added finalised annotation for BRA_SP
• Re-cropped images to 256x256 pixels

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

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🙏 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)

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

Last Updated: 2026-07-24