DATASET_DESCRIPTION.md

# MatchGeo Dataset — Version 1.1

## 1. Dataset Overview

| Attribute | Details |
|---|---|
| **Title** | MatchGeo: Multi-region Digital Elevation Model Dataset for Local Feature Matching |
| **Version** | 1.1 |
| **Release Date** | 2026-05-11 |
| **Authors / Creators** | Correa, S. P. L. P.; Pazini Pedro, D. F.; Oliveira, H. N.; Belton, D.; Ivánová, I.; Santos, A. de Paula |
| **Contact** | sabrina.correa@ufv.br |
| **Persistent Identifier (DOI)** | [10.5281/zenodo.21229785](https://doi.org/10.5281/zenodo.21229785) |
| **Keywords** | digital elevation model, DEM, DSM, local feature matching, geospatial dataset, urban terrain, cross-domain matching, computer vision, GeoTIFF, LiDAR, photogrammetry, Structure-from-Motion, satellite stereophotogrammetry |
| **Related Publication** | In preparation |
| **Repository** | [Zenodo](https://doi.org/10.5281/zenodo.21229785) · [Hugging Face](https://huggingface.co/datasets/paeslemesa/matchgeo) |

---

## 2. Description

MatchGeo is a curated, multi-region Digital Elevation Model (DEM) dataset designed for training and benchmarking local feature matching algorithms in urban and natural terrain analysis. It aggregates high-resolution elevation data from **13 distinct environments** across 6 continents to support research in cross-domain local feature detection and matching under varying acquisition methods, climates, terrain types, and urban morphologies.

The dataset provides standardized **256x256 pixel GeoTIFF patches** with handcrafted ground truth annotations for two cities, enabling systematic evaluation of both intra-region and inter-region model generalization.

### Geographic & Temporal Scope

| Region | Country | Temporal Coverage | Spatial Coverage | Terrain Type |
|---|---|---|---|---|
| Antarctic Peninsula (ATA_MV) | Antarctica | 2009–2024 | Peninsula | Polar, ice |
| São Paulo (BRA_SP) | Brazil | 2020 | Municipal (1,544 km²) | Tropical, urban |
| Wutai Shan (CHN_WS) | China | 2021 | Regional | Mountainous |
| El Hierro (ESP_EH) | Canary Islands (Spain) | 2022–2025 | Regional | Volcanic, coastal |
| Lahti Lake (FIN_LM) | Finland | 2020–2026 | Regional | Temperate, country |
| Bonn (GER_BN) | Germany | 2016–2018 | City limits | Temperate, country |
| Sinabung Volcano (IDN_SV) | Indonesia | 2018 | 16.07 km² | Volcanic, tropical |
| Almaty City (KAZ_AC) | Kazakhstan | 2017 | 304.28 km² | Semi-arid, urban |
| Wadi Al-Akhdar (KSA_WA) | Saudi Arabia | 2016 | 1,200 km² | Desert, graben |
| Hebron Fault (NAM_HF) | Namibia | 2017 | Regional | Arid, fault zone |
| Kapiti Coast (NZL_KP) | New Zealand | 2010–2025 | Regional | Coastal, temperate, country |
| Tarlac (PHL_TA) | Philippines | 2014–2017 | Regional | Tropical, flat |
| Grand Canyon (USA_GC) | United States | 2020–2026 | Regional | Desert, canyon |

### Purpose & Use Cases
- Training and benchmarking local feature detectors and descriptors on geospatial elevation data
- Cross-domain generalization studies (LiDAR vs. photogrammetry vs. satellite, temperate vs. tropical vs. desert)
- Urban terrain analysis and change detection
- Evaluation of matching robustness across sensor modalities and terrain types
- Benchmarking for computer vision models on non-RGB data

---

## 3. Content & Schema

### 3.1 Files Included

| Filename / Folder | Description | Approx. Size |
|---|---|---|
| `data/ATA_MV/` | Antarctic Peninsula tiles + metadata | 138.3 MB |
| `data/BRA_SP/` | São Paulo tiles + annotations + metadata | 105.34 MB |
| `data/CHN_WS/` | Wutai Shan tiles + metadata | 206.08 MB |
| `data/ESP_EH/` | El Hierro tiles + metadata | 422.49 MB |
| `data/FIN_LM/` | Lahti Lake tiles + metadata | 45.97 MB |
| `data/GER_BN/` | Bonn tiles + annotations + metadata | 333.24 MB |
| `data/IDN_SV/` | Sinabung Volcano tiles + metadata | 34.98 MB |
| `data/KAZ_AC/` | Almaty City tiles + metadata | 128.00 MB |
| `data/KSA_WA/` | Wadi Al-Akhdar tiles + metadata | 556.85 MB |
| `data/NAM_HF/` | Hebron Fault tiles + metadata | 115.24 MB |
| `data/NZL_KP/` | Kapiti Coast tiles + metadata | 362.23 MB |
| `data/PHL_TA/` | Tarlac tiles + metadata | 64.10 MB |
| `data/USA_GC/` | Grand Canyon tiles + metadata | 138.27 MB |
| `splits/` | Train / validation / test split manifests (CSV) | 1.6 MB |
| `manifest.json` | Central machine-readable catalog (JSON-LD) | 7.2 KB |
| `README.md` | High-level project documentation | 12.5 KB |
| `DATASET_DESCRIPTION.md` | This file | 126.0 KB |
| `LICENSE` | Full CC BY 4.0 legal text | 14.2 KB |

### 3.2 Per-region File Structure

Each region folder follows this structure:

```
<REGION_ID>/
├── <REGION_ID>.tif                  # Merged DEM (BigTIFF, tiled, DEFLATE)
├── <REGION_ID>_extent.geojson       # Bounding polygon of full coverage
├── <REGION_ID>_tiles.geojson        # Tile index (grid of all 256x256 patches)
├── metadata/
│   └── <REGION_ID>_metadata.json    # ISO 19115-2 + OGC 23-008r3 metadata
├── annotations/                     # Only for labelled cities (BRA_SP, GER_BN)
│   └── <REGION_ID>_###_###.json     # Keypoint annotations per tile
└── tiles/
    └── <REGION_ID>_###_###.tif      # 256x256 pixel patches
```

### 3.3 Data Dictionary

| Variable / Field | Data Type | Description | Units / Format | Nullable | Example |
|---|---|---|---|---|---|
| `tile_id` | string | Unique identifier for each 256x256 patch | — | No | `GER_BN_001_010` |
| `region` | string | Source region code | — | No | `GER_BN` |
| `geometry` | GeoTIFF raster | Elevation patch | 256x256 px, float32 | No | — |
| `crs` | string | Coordinate reference system | EPSG code | No | `EPSG:25832` |
| `resolution` | float | Ground sample distance | meters | No | `1.0` |
| `acquisition_method` | string | Data acquisition technique | — | No | `airborne_lidar` |
| `acquisition_year` | integer | Year of data capture | year | No | `2017` |
| `elevation_min` | float | Minimum elevation in patch | meters | Yes | `42.3` |
| `elevation_max` | float | Maximum elevation in patch | meters | Yes | `156.8` |
| `elevation_mean` | float | Mean elevation in patch | meters | Yes | `98.4` |
| `elevation_std` | float | Standard deviation of elevation | meters | Yes | `12.1` |
| `annotated` | boolean | Whether patch has handcrafted ground truth | — | No | `true` |
| `n_annotations` | integer | Number of verified point annotations in patch | count | Yes | `127` |
| `split` | string | Dataset split assignment | — | No | `train` |
| `difficulty` | string | Difficulty tag for stratification | — | Yes | `easy` |

---

## 4. Methodology & Provenance

### 4.1 Data Sources

All raw data were obtained from open municipal, national, or research portals and are redistributed under terms compatible with CC BY 4.0.

| City | Source Dataset | Provider | Method | Resolution | Year | CRS | License / Terms |
|---|---|---|---|---|---|---|---|
| **ATA_MV** | REMA | Polar Geospatial Center / University of Minnesota | Satellite stereophotogrammetry | 1.0 m | 2009–2024 | EPSG:3031 | CC BY 4.0 |
| **BRA_SP** | GeoSampa MDS 2020 | Prefeitura de São Paulo (PMSP) / SMUL / GEOINFO | Airborne LiDAR (Optech ORION H300) | **0.5 m** | 2020 | EPSG:31983 | Open municipal data |
| **CHN_WS** | Wutai Shan / Yingwang Shan 2021 | Zhou, C. / OpenTopography | UAV SfM (DJI Phantom 4) | 1.0 m | 2021 | EPSG:32649 | OpenTopography terms |
| **ESP_EH** | PNOA-LiDAR 3ª Cobertura | CNIG / Instituto Geográfico Nacional | Airborne LiDAR | 0.5 m | 2022–2025 | EPSG:3040 | CC BY 4.0 |
| **FIN_LM** | Elevation Model 2 m | Maanmittauslaitos (NLS Finland) | Airborne LiDAR + photogrammetry | 2.0 m | 2020–2026 | EPSG:3067 | CC BY 4.0 |
| **GER_BN** | Digitales Oberflächenmodell (DOM) | Geobasis NRW / Bezirksregierung Köln | Airborne LiDAR | 1.0 m | 2016–2018 | EPSG:25832 | Open data |
| **IDN_SV** | Sinabung Volcano 2018 | Carr, B. / OpenTopography | UAS SfM (DJI Matrice 210) | 0.87 m | 2018 | EPSG:32647 | OpenTopography terms |
| **KAZ_AC** | Almaty City 2017 | Amey et al. / OpenTopography | Pleiades Tristereo | 1.0 m | 2017 | EPSG:32643 | OpenTopography terms |
| **KSA_WA** | Wadi-al-Akhdar 2016 | Matthieu et al. / OpenTopography | SPOT 6 Stereo | 1.6 m | 2016 | EPSG:32637 | OpenTopography terms |
| **NAM_HF** | Hebron Fault 2017 | Salomon et al. / UCT / OpenTopography | WorldView-3 Stereo | 0.53 m | 2017 | EPSG:32733 | OpenTopography terms |
| **NZL_KP** | NZ LiDAR 1m DEM | LINZ | Airborne LiDAR | 1.0 m | 2010–2025 | EPSG:2193 | CC BY 4.0 |
| **PHL_TA** | LiPAD | UP Diliman TCAGP / DREAM / DOST | Airborne LiDAR | 1.0 m | 2014–2017 | EPSG:32651 | Open data |
| **USA_GC** | USGS 3DEP DEM | U.S. Geological Survey | LiDAR | 0.5 m | 2020–2026 | EPSG:6341 | Public Domain |

### 4.2 Processing Pipeline

All cities were processed through a standardized PDAL pipeline with region-specific adaptations:

1. **Acquisition & Download** — Raw data retrieved from source portals in native CRS and resolution.
2. **Preprocessing (region-specific)**:
   - **BRA_SP**: Ground classification via `filters.smf` (scalar=1.25, slope=0.15, threshold=0.5, window=16.0)
   - **CHN_WS**: Noise removal (`filters.range` Classification![7:7]) + statistical outlier removal (mean_k=6, multiplier=2.0)
   - **IDN_SV, KSA_WA, NAM_HF**: Statistical outlier removal (mean_k=6, multiplier=2.0)
   - **Others**: Direct rasterization
3. **Rasterization** — PDAL `writers.gdal` with:
   - `output_type`: `max` (DSM: highest point per cell)
   - `data_type`: `float32`
   - `nodata`: `-9999`
   - `gdalopts`: `COMPRESS=DEFLATE|TILED=YES|BIGTIFF=YES`
   - `resolution`: City-specific (see table above)
4. **Patch Extraction** — Non-overlapping 256x256 pixel patches extracted using a regular grid (no resampling).
5. **Quality Control** — Outlier removal based on elevation range checks, CRS validation, and visual inspection.
6. **Annotation (BRA_SP, GER_BN)** — Handcrafted point-level annotations in normalized coordinates.
7. **Stratified Splitting** — 80% train / 10% validation / 10% test (planned; to be stratified by region and difficulty).
8. **Packaging & Metadata** — Per-region ISO 19115-2 metadata; central JSON-LD manifest; archived on Zenodo with DOI.

### 4.3 Software & Tools

- Python 3.10.20
- PDAL 2.6.0 (point cloud processing)
- GDAL / rasterio (geospatial I/O and reprojection)
- NumPy / pandas (tabular metadata management)
- Custom annotation and patch-generation scripts

---

## 5. Access & Licensing

### 5.1 Availability

| Repository | URL | Access Type |
|---|---|---|
| **Zenodo (Primary)** | [https://doi.org/10.5281/zenodo.21229785](https://doi.org/10.5281/zenodo.21229785) | Open, DOI-backed, permanent |
| **Hugging Face Datasets** | [https://huggingface.co/datasets/paeslemesa/matchgeo](https://huggingface.co/datasets/paeslemesa/matchgeo) | Open, streaming loader available |

- **Total Size**: ~12.5 GB
- **File Formats**: Cloud Optimized GeoTIFF - COG (`.tif`), GeoJSON (`.geojson`), metadata JSON, split manifests CSV
- **Access Type**: Open access, no registration required

### 5.2 License

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

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.

The full legal text is provided in the [`LICENSE`](LICENSE) file and at [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode).

### 5.3 Terms of Use & Source Attribution

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

1. **Dataset citation** (see [Section 8: Citation](#8-citation))
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*

---

## 6. Interoperability & Technical Details

### 6.1 Standards & Formats

| Aspect | Standard / Value |
|---|---|
| **Raster format** | Cloud Optimized GeoTIFF (BigTIFF variant, OGC 23-008r3 compliant) |
| **Internal tiling** | 256 × 256 pixels |
| **Pixel depth** | Float32 |
| **Patch dimensions** | 256 × 256 pixels |
| **Coordinate systems** | Region-specific UTM (see Section 4.1) |
| **Metadata standard** | ISO 19115-2 + OGC 23-008r3 |
| **Encoding** | UTF-8 for all text and tabular files |
| **Compression** | DEFLATE |
| **NoData value** | -9999 |

### 6.2 OGC GeoTIFF Compliance (23-008r3)

All GeoTIFFs comply with OGC 23-008r3 and contain the following required keys:

| Key | Status | Source |
|---|---|---|
| GTModelTypeGeoKey | ✅ | PDAL `override_srs` |
| GTRasterTypeGeoKey | ✅ | GDAL default (PixelIsArea) |
| ProjectedCSTypeGeoKey | ✅ | EPSG code embedded |
| GeogGeodeticDatumGeoKey | ✅ | Derived from EPSG |
| GeogAngularUnitsGeoKey | ✅ | Degree (default) |
| ProjLinearUnitsGeoKey | ✅ | Meter (default) |
| PixelScale | ✅ | GDAL writer |
| TiePoint | ✅ | Upper-left corner coordinate |
| BigTIFF | ✅ | `BIGTIFF=YES` |
| Tiled | ✅ | `TILED=YES` |
| Compression | ✅ | `COMPRESS=DEFLATE` |
| NoData | ✅ | `nodata=-9999` |

> **Note on vertical CRS**: The GeoTIFFs encode 2D projected CRS (EPSG codes) with elevation as pixel values. A vertical CRS is not explicitly embedded in the GeoTIFF GeoKeys; vertical datum information is documented in per-region `metadata.json` files.

### 6.3 Controlled Vocabularies

- **GCMD Keywords**: `EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION > DIGITAL ELEVATION/TERRAIN MODEL (DEM)`
- **ISO Topic Category**: `elevation`
- **INSPIRE Theme**: `Elevation`

### 6.4 Data Splits

| Split | Proportion | Stratification |
|---|---|---|
| Train | 80% | By region and difficulty |
| Validation | 10% | By region and difficulty |
| Test | 10% | By region and difficulty |

Explicit **intra-region** and **inter-region** test subsets are planned for future releases.

---

## 7. Quality Assurance & Known Limitations

### 7.1 Validation

- Bonn annotations were verified by multiple annotators.
- Elevation ranges were cross-checked against known region topographies.
- CRS consistency was validated using GDALinfo and automated assertions.

### 7.2 Known Biases & Limitations

| Issue | Description | Mitigation / Status |
|---|---|---|
| **Geographic bias** | Dense annotation only in GER_BN and BRA_SP; others unlabelled | v1.1 separates labelled/unlabelled; future releases will add annotations |
| **Temporal mismatch** | Data spans 2009–2026 across cities | Documented; users should account for temporal drift |
| **Sensor heterogeneity** | LiDAR, photogrammetry, SfM, satellite stereo — different noise characteristics | Explicitly treated as cross-domain challenge |
| **Resolution heterogeneity** | Native resolutions range from 0.5 m to 2.0 m | |
| **Missing data** | Water bodies, ocean excluded; NoData=-9999 | Documented in per-tile metadata |
| **ATA_MV uncertainty** | Antarctica data from REMA satellite stereophotogrammetry | Marked for verification |
| **CHN_WS CRS discrepancy** | BibTeX says EPSG:32649; user previously mentioned EPSG:32648 | To be verified from actual file |
| **Vertical CRS** | No explicit vertical GeoKey in GeoTIFFs; elevations are source-dependent (orthometric or ellipsoidal) | Documented in per-region `metadata.json` |

---

## 8. 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:

```bibtex
% BRA_SP
@misc{datasetgeosampa,
  author = {{Prefeitura do Munic{'i}pio de S{\~a}o Paulo (PMSP)}},
  title = {{Nuvem de Pontos {MDS} 2020}},
  year = {2020},
  publisher = {GeoSampa},
  url = {https://geosampa.prefeitura.sp.gov.br/}
}

% CHN_WS
@misc{datasetchinayingwang2021,
  author = {Zhou, C.},
  title = {High Resolution Topography of {Wutai Shan} and {Yingwang Shan}, China, 2021},
  year = {2023},
  publisher = {OpenTopography},
  doi = {10.5069/G98C9TGT}
}

% ESP_EH
@misc{datasetspain,
  author = {{Centro Nacional de Informaci{'o}n Geogr{'a}fica (CNIG)}},
  title = {{PNOA-LiDAR --- 3{\textordfeminine} Cobertura (2022--2025)}},
  year = {2022--2025},
  url = {https://centrodedescargas.cnig.es/CentroDescargas/lidar-tercera-cobertura}
}

% FIN_LM
@misc{datasetfinland,
  author = {{Maanmittauslaitos --- National Land Survey of Finland}},
  title = {{Korkeusmalli 2 m / Elevation Model 2 m}},
  year = {2026},
  url = {https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/elevation-model-2-m}
}

% GER_BN
@misc{datasetgermany,
  author = {{Geobasis NRW --- Bezirksregierung K{\"o}ln}},
  title = {{Digitales Oberfl{\"a}chenmodell ({DOM})}},
  year = {2024},
  url = {https://www.bezreg-koeln.nrw.de/geobasis-nrw/produkte-und-dienste/hoehenmodelle/digitale-oberflaechenmodelle/digitales}
}

% IDN_SV
@misc{datasetindonesiasinabung2018,
  author = {Carr, B.},
  title = {{Sinabung Volcano} (Indonesia), {June 20, 2018}},
  year = {2021},
  publisher = {OpenTopography},
  doi = {10.5069/G8988568}
}

% KAZ_AC
@misc{datasetkazakhstanalmaty2017,
  author = {Amey, R. and Watson, C. S. and Elliott, J. and Walker, R.},
  title = {{Almaty City, Kazakhstan, 2017, Derived from {Pleiades} Tristereo Imagery}},
  year = {2021},
  publisher = {OpenTopography},
  doi = {10.5069/G9H41PMP}
}

% KSA_WA
@misc{dataset_saudiarabia_wadi_al_akhdar_2016,
  author = {Matthieu, R. and Moulin, A. and J{'o}nsson, S.},
  title = {{Digital Surface Model of {Wadi-al-Akhdar} Graben, Saudi Arabia, 2016}},
  year = {2024},
  publisher = {OpenTopography},
  doi = {10.5069/G9V40SDZ}
}

% NAM_HF
@misc{datasetnamibiahebron_2017,
  author = {Salomon, G. and Smit, J. and Muir, R. and Stevens, V. and Sloan, R. A.},
  title = {{Hebron Fault, Namibia 2017 {WorldView-3} Stereophotogrammetric {DEM}}},
  year = {2021},
  publisher = {OpenTopography},
  doi = {10.5069/G9W957BC}
}

% NZL_KP
@misc{datasetnewzealand,
  author = {{Toit{\=u} Te Whenua Land Information New Zealand ({LINZ})}},
  title = {New Zealand {LiDAR} 1m {DEM}},
  year = {2025},
  url = {https://data.linz.govt.nz/layer/121859-new-zealand-lidar-1m-dem/services/csw/}
}

% PHL_TA
@misc{datasetphilippines,
  author = {{Disaster Risk and Exposure Assessment for Mitigation (DREAM) Program}},
  title = {{LiPAD --- {LiDAR} Portal for Archiving and Distribution}},
  year = {2016},
  url = {https://lipad.dream.upd.edu.ph/}
}

% USA_GC
@misc{datasetusa,
  author = {{U.S. Geological Survey}},
  title = {{3D Elevation Program (3DEP) Digital Elevation Models}},
  year = {2026},
  url = {https://apps.nationalmap.gov/downloader/}
}
```

---

## 9. Version History

| Version | Date | Changes | Author |
|---|---|---|---|
| 1.0 | 2026-03-30 | Initial release; GER_BN 20,000+ handcrafted annotations | Correa et al. |
| 1.1 | 2026-05-11 | Expanded to 13 cities; reorganized into labelled/unlabelled; added per-region metadata; synchronized with FAIR + OGC 23-008r3 | Correa et al. |

---

## 10. Acknowledgments & Funding

- **Data providers**: European Commission / ESA (Copernicus), 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)
- **Institutional support**: Universidade Federal de Viçosa (UFV)

---

## 11. Contact & Support

- **General inquiries**: sabrina.correa@ufv.br
- **Dataset maintainer**: Sabrina Correa, Universidade Federal de Viçosa
- **Issues / bug reports**: [GitHub Issues](https://github.com/paeslemesa/matchgeo/issues)
- **Discussions**: [GitHub Discussions](https://github.com/paeslemesa/matchgeo/discussions)

---

**Last Updated**: 2026-07-09