Datasets:

paeslemesa
/

matchgeodem

@@ -9,46 +9,128 @@ tags:
 - dem
 - depth
 - cv
-pretty_name: GeoMatch-DEM
 size_categories:
 - 1K<n<10K
 ---
-# GeoMatch-DEM: Multi-City Digital Elevation Model Dataset for Local Feature Matching
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.XXXXXXX.svg)](https://doi.org/10.5281/zenodo.XXXXXXX)
 [![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 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
-**A curated multi-city Digital Elevation Model (DEM) dataset for training and benchmarking local feature matching algorithms in urban terrain analysis.**
 ---
 ## 🎯 Overview
-GeoMatch-DEM aggregates high-resolution elevation data from three distinct urban environments—Bonn (Germany), São Paulo (Brazil), and Fortaleza (Brazil)—to support research in cross-domain local feature detection and matching. The dataset provides standardized 333×333 pixel patches with handcrafted and automated ground truth annotations for training computer vision models on geospatial data.
 ### Key Features
-- **Multi-source fusion**: LiDAR (Bonn, São Paulo) and photogrammetric (Fortaleza) elevation data
 - **Standardized format**: All cities processed to 333×333 pixel GeoTIFF tiles
-- **Rich annotations**: 27,000+ verified patch pairs from Bonn alone
 - **Cross-city evaluation**: Explicit intra-city and inter-city test splits
 - **FAIR compliant**: ISO 19115-2 metadata, DOI registration, open access
 ---
-## 📊 Dataset Statistics
-| City | Source | Method | Resolution | Tiles | CRS | Year | Area |
-|------|--------|--------|------------|-------|-----|------|------|
-| **Bonn** | IEEE GeoNRW | Airborne LiDAR | 1.0 m | [X] | EPSG:25832 | 2016-2018 | City limits |
-| **São Paulo** | GeoSampa | Airborne LiDAR | **0.5 m** | 700+ | EPSG:31983 | [Year] | Municipal |
-| **Fortaleza** | Municipal Survey | Aerial photogrammetry | 1.0 m | ~3,000 | EPSG:31984 | 2016-2017 | 333 km² |
-**Ground Truth**: 27,000+ handcrafted annotations (Bonn), plus automated pairs/triplets
-**Splits**: 80% train / 10% validation / 10% test (stratified by city and difficulty)
-**Total Size**: ~[X] GB
-**Patch Size**: 333 × 333 pixels (physical extent: 166.5m × 166.5m for 0.5m data)
 ---
@@ -56,434 +138,46 @@ GeoMatch-DEM aggregates high-resolution elevation data from three distinct urban
 | Repository | Link | Notes |
 |------------|------|-------|
-| **Zenodo (Primary)** | [https://doi.org/10.5281/zenodo.XXXXXXX](https://doi.org/10.5281/zenodo.XXXXXXX) | DOI-backed, permanent archive |
-| Hugging Face Datasets | [https://huggingface.co/datasets/paeslemesa/geomatch-dem](https://huggingface.co/datasets/paeslemesa/geomatch-dem) | Streaming loader available |
-| Academic Torrents | [Link TBD] | For large-scale institutional downloads |
 ### Quick Download
 ```bash
 # Using zenodo_get (pip install zenodo_get)
-zenodo_get 10.5281/zenodo.XXXXXXX
 # Or wget/curl (specific file)
-wget https://zenodo.org/record/XXXXXXX/files/GeoMatch-DEM-v1.0.zip
 ```
 ---
 ## 🎓 Citation
 If you use this dataset in your research, please cite:
 ```bibtex
-@dataset{correa_2026_geomatch_dem,
-  author       = {Correa, S. P. L. P. and Santos, A. de Paula and Oliveira, H. N.},
-  title        = {{GeoMatch-DEM: Multi-City Digital Elevation Model Dataset for Local Feature Matching}},
   year         = 2026,
   publisher    = {Zenodo},
-  version      = {1.0},
-  doi          = {10.5281/zenodo.XXXXXXX},
-  url          = {https://doi.org/10.5281/zenodo.XXXXXXX},
-  note         = {Contains data derived from IEEE GeoNRW, GeoSampa, and Fortaleza City Hall}
-}
-```
-### Original Data Source Citations
-You must also cite the original data providers:
-**Bonn — IEEE GeoNRW:**
-```bibtex
-@misc{ieee_grss_2020_geonrw,
-  author       = {{IEEE GRSS DASE Committee}},
-  title        = {{IEEE GeoNRW Dataset}},
-  year         = 2020,
-  publisher    = {IEEE DataPort},
-  doi          = {10.21227/nd5j-5q42},
-  url          = {https://dx.doi.org/10.21227/nd5j-5q42}
-}
-```
-**São Paulo — GeoSampa:**
-```bibtex
-@misc{prefeitura_sp_geosampa,
-  author       = {{Prefeitura de São Paulo, Secretaria Municipal de Urbanismo e Licenciamento}},
-  title        = {{GeoSampa - Sistema de Informação Geográfica - Dados de Altimetria}},
-  url          = {https://geosampa.prefeitura.sp.gov.br/},
-  note         = {LiDAR data processed for this dataset using PDAL}
-}
-```
-**Fortaleza — Municipal Survey:**
-```bibtex
-@misc{fortaleza_2016_mdt,
-  author       = {{Prefeitura Municipal de Fortaleza, SEUMA}},
-  title        = {{Modelo Digital de Terreno - Levantamento Aerofotogramétrico 2016-2017}},
-  year         = 2016,
-  organization = {Esteio Engenharia e Aerolevantamento S/A},
-  url          = {[URL if available]},
-  note         = {Survey dates: 2016-12-20, 23, 27; 2017-01-06}
 }
 ```
----
-## 🗂️ Dataset Structure
-```
-GeoMatch-DEM-v1.0/
-├── README.md                          # This file
-├── CITATION.cff                       # Citation File Format
-├── LICENSE                            # CC BY 4.0 full text
-├── data/
-│   ├── bonn/
-│   │   ├── processed/                 # Standardized GeoTIFF tiles (1m res)
-│   │   │   ├── bonn_001.tif
-│   │   │   └── ...
-│   │   └── annotations/               # Bonn-specific ground truth
-│   │       ├── handcrafted_pairs.csv  # 27,000+ verified annotations
-│   │       └── synthetic_pairs.csv    # Automated augmentations
-│   ├── sao_paulo/
-│   │   ├── processed/                 # 700+ GeoTIFF tiles (0.5m res)
-│   │   │   ├── sp_001.tif
-│   │   │   └── ...
-│   │   └── annotations/
-│   └── fortaleza/
-│       ├── processed/                 # ~3,000 GeoTIFF tiles (1m res)
-│       │   ├── for_001.tif
-│       │   └── ...
-│       └── annotations/
-├── splits/
-│   ├── train.csv                      # 80% of all pairs
-│   ├── val.csv                        # 10% validation
-│   └── test.csv                       # 10% test (hold-out)
-├── metadata/
-│   ├── iso19139.xml                   # ISO 19115-2 compliant metadata
-│   ├── datacite.xml                   # DataCite registration metadata
-│   ├── schemaorg.jsonld               # Schema.org/JSON-LD for web discovery
-│   └── prov/
-│       ├── provenance.ttl             # W3C PROV-O RDF triples
-│       └── processing.log             # Detailed processing history
-└── docs/
-    ├── DATASET_DESCRIPTION.md         # Full technical specifications
-    ├── PREPROCESSING.md               # Reproducible processing pipelines
-    ├── QUALITY_REPORT.md              # Per-city quality assessment
-    └── CITY_COMPARISON.md             # Cross-city characteristics analysis
-```
----
-## 🚀 Quick Start
-### Installation
-```bash
-pip install rasterio pandas numpy matplotlib
-```
-### Loading DEM Tiles
-```python
-import rasterio
-import numpy as np
-import matplotlib.pyplot as plt
-def load_dem(tile_path):
-    """
-    Load a DEM tile and return elevation data with geospatial metadata.
-    Parameters
-    ----------
-    tile_path : str or Path
-        Path to GeoTIFF file
-    Returns
-    -------
-    dem : ndarray
-        2D array of elevation values (float32)
-    transform : Affine
-        Rasterio affine transformation matrix
-    crs : CRS
-        Coordinate reference system
-    profile : dict
-        Full rasterio profile metadata
-    """
-    with rasterio.open(tile_path) as src:
-        dem = src.read(1)
-        transform = src.transform
-        crs = src.crs
-        profile = src.profile
-    # Replace nodata with NaN for visualization
-    if profile.get('nodata') is not None:
-        dem = np.where(dem == profile['nodata'], np.nan, dem)
-    return dem, transform, crs, profile
-# Example: Load a São Paulo tile
-dem, transform, crs, profile = load_dem('data/sao_paulo/processed/sp_001.tif')
-print(f"Tile shape: {dem.shape}")           # (333, 333)
-print(f"CRS: {crs}")                        # EPSG:31983
-print(f"Resolution: {transform.a}m")       # 0.5m
-print(f"Elevation range: [{np.nanmin(dem):.1f}, {np.nanmax(dem):.1f}]m")
-# Visualize
-plt.figure(figsize=(8, 8))
-plt.imshow(dem, cmap='terrain', vmin=np.nanpercentile(dem, 2), vmax=np.nanpercentile(dem, 98))
-plt.colorbar(label='Elevation (m)')
-plt.title('São Paulo DEM Sample')
-plt.axis('off')
-plt.show()
-```
-### Loading Ground Truth Pairs
-```python
-import pandas as pd
-from pathlib import Path
-# Load train split
-train_df = pd.read_csv('splits/train.csv')
-print(f"Total training pairs: {len(train_df)}")
-print(f"Cities represented: {train_df[['city_a', 'city_b']].stack().unique()}")
-# Get a verified matching pair (handcrafted annotation)
-match_row = train_df[(train_df['match_valid'] == 1) &
-                     (train_df['annotation_type'] == 'handcrafted')].iloc[0]
-dem_a, _, _, _ = load_dem(match_row['patch_a_path'])
-dem_b, _, _, _ = load_dem(match_row['patch_b_path'])
-print(f"Pair type: {match_row['city_a']} → {match_row['city_b']}")
-print(f"Match valid: {match_row['match_valid']}")
-print(f"Annotation: {match_row['annotation_type']}")
-# Display side-by-side
-fig, axes = plt.subplots(1, 2, figsize=(12, 5))
-axes[0].imshow(dem_a, cmap='terrain')
-axes[0].set_title(f"Reference: {Path(match_row['patch_a_path']).stem}")
-axes[1].imshow(dem_b, cmap='terrain')
-axes[1].set_title(f"Match: {Path(match_row['patch_b_path']).stem}")
-for ax in axes:
-    ax.axis('off')
-plt.tight_layout()
-plt.show()
-```
-### Loading Triplet Format (for Metric Learning)
-```python
-# For models using anchor-positive-negative triplets
-triplet_df = pd.read_csv('splits/train_triplets.csv')
-row = triplet_df.iloc[0]
-anchor = load_dem(row['anchor_path'])[0]
-positive = load_dem(row['positive_path'])[0]
-negative = load_dem(row['negative_path'])[0]
-# anchor and positive are matching (same location/similar)
-# anchor and negative are non-matching (different location)
-```
----
-## 🌍 Data Sources & Processing
-### Bonn, Germany — IEEE GeoNRW
-**Source**: IEEE GRSS Data and Algorithm Standard Evaluation (DASE) Committee
-**Method**: Airborne LiDAR survey
-**Resolution**: 1.0 m
-**CRS**: ETRS89 / UTM zone 32N (EPSG:25832)
-**Vertical Datum**: DHHN92 (German Height Reference System)
-**Year**: 2016–2018
-**Processing Applied**:
-- Downloaded original GeoNRW tiles covering Bonn municipal area
-- Subset to city boundaries using administrative boundary mask
-- Re-tiled to standardized 333×333 pixel patches
-- Format: GeoTIFF, 32-bit float, no compression (for fast I/O)
-- Quality: ±0.1 m vertical accuracy (highest quality tier)
-**Annotations**: 27,000+ handcrafted patch pairs for training/validation
-**Role in Dataset**: High-quality baseline for algorithm development
----
-### São Paulo, Brazil — GeoSampa
-**Source**: Prefeitura de São Paulo, SMUL (Secretaria Municipal de Urbanismo e Licenciamento)
-**Method**: Airborne LiDAR (700+ original LAS tiles)
-**Resolution**: **0.5 m** (highest in dataset)
-**CRS**: SIRGAS 2000 / UTM zone 23S (EPSG:31983)
-**Vertical Datum**: SIRGAS 2000
-**Year**: [Check GeoSampa metadata for exact survey year]
-**Processing Pipeline** (PDAL via Python):
-```python
-# Reproducible PDAL pipeline used for processing
-import pdal
-pipeline_json = """
-[
-    {
-        "type": "readers.las",
-        "filename": "input.laz"
-    },
-    {
-        "type": "filters.smrf",
-        "scalar": 1.25,
-        "slope": 0.15,
-        "threshold": 0.5,
-        "window": 16.0
-    },
-    {
-        "type": "writers.gdal",
-        "filename": "output.tif",
-        "resolution": 0.5,
-        "output_type": "max",
-        "data_type": "float32",
-        "nodata": -9999
-    }
-]
-"""
-pipeline = pdal.Pipeline(pipeline_json)
-pipeline.execute()
-```
-**Rationale**:
-- **SMRF (Simple Morphological Filter)**: Selected for ground classification in dense urban environment with mixed topography
-- **Parameters**: Tuned for São Paulo's vertical morphology (flat floodplains + steep hills)
-- **output_type="max"**: Conservative ground estimate using highest ground returns to avoid building residuals
-- **No compression**: Prioritized read speed for deep learning training (dataset stored on fast SSD/NVMe recommended)
-**Quality Notes**:
-- Expected vertical accuracy: ±0.15 m (based on GeoSampa LiDAR specifications)
-- Known artifacts: Water bodies (Tietê/Pinheiros rivers), dense vegetation gaps (>95% canopy cover)
-- Coverage gaps: Very dense urban canyons may have incomplete ground returns
----
-### Fortaleza, Brazil — Municipal Survey 2016-2017
-**Source**: Prefeitura Municipal de Fortaleza, SEUMA (Secretaria de Urbanismo e Meio Ambiente)
-**Contractor**: Esteio Engenharia e Aerolevantamento S/A
-**Method**: Aerial photogrammetric survey
-**Resolution**: 1.0 m
-**CRS**: SIRGAS 2000 / UTM zone 24S (EPSG:31984)
-**Vertical Datum**: [Local datum - verify in source]
-**Survey Dates**: 20, 23, 27 December 2016; 06 January 2017
-**Coverage**:
-- **Total Area**: 333 km² (full municipal coverage)
-- **Detailed Area**: 20 km² high-density core at enhanced resolution
-- **Detailed Neighborhoods**: Mucuripe, Meireles, Varjota, Aldeota, Dionísio Torres, Joaquim Távora, plus partial: Papicu, Vicente Pinzon, Cocó, São João do Tauape, Fátima, José Bonifácio, Centro, Praia de Iracema, Cais do Porto
-**Processing**:
-- Pre-processed DEM delivered by contractor
-- Ground filtering applied by Esteio Engenharia (methodology: proprietary)
-- Point density: 1 point/m² (general area), higher in detailed zone
-- Subset to 333×333 pixel tiles matching dataset standard
-**Quality Notes**:
-- Expected vertical accuracy: ±0.3 m (typical for 1:1000 photogrammetry)
-- **Important**: 2016-2017 acquisition date—significant urban development since then
-- Limitations: Photogrammetric noise higher than LiDAR; vegetation filtering less reliable
-- Coastal areas: Possible tide-related artifacts in beach zones
----
-## 🏷️ Ground Truth & Annotations
-### Annotation Types
-| Type | Count | Method | Purpose |
-|------|-------|--------|---------|
-| **Handcrafted** | 27,000+ (Bonn) | Manual expert verification | High-confidence training seed, benchmark evaluation |
-| **Synthetic** | [X] | Geometric transforms (rotation, scale, translation) | Data augmentation, invariance training |
-| **Automated** | [X] | SIFT+RANSAC candidate generation | Scale training data |
-### CSV Schema
-**Pair Format** (`splits/*.csv`):
-| Column | Type | Description |
-|--------|------|-------------|
-| `patch_a_path` | str | Relative path to first patch |
-| `patch_b_path` | str | Relative path to second patch |
-| `match_valid` | int (0/1) | 1 = geometrically matching, 0 = negative pair |
-| `city_a` | str | Source city of patch A (`bonn`, `sao_paulo`, `fortaleza`) |
-| `city_b` | str | Source city of patch B |
-| `overlap_percent` | float | Geographic overlap for intra-city pairs |
-| `transform_matrix` | str | JSON 3×3 homography (for synthetic pairs) |
-| `annotation_type` | str | `handcrafted`, `synthetic`, or `automated` |
-| `difficulty` | str | `easy`, `medium`, `hard` (for handcrafted) |
-| `split` | str | `train`, `val`, or `test` |
-**Triplet Format** (if applicable):
-- `anchor_path`, `positive_path`, `negative_path`
-- Same metadata columns as above
-### Cross-City Strategy
-- **Intra-city pairs** (70%): Both patches from same city (baseline difficulty)
-- **Inter-city pairs** (30%): Patches from different cities (tests generalization)
-- **Hard negatives**: Geographically close but non-matching (e.g., adjacent tiles)
----
-## 📋 Data Splits
-Stratified random sampling ensuring:
-- Balanced city representation
-- Balanced annotation types
-- No geographic leakage (patches from same source tile kept in same split)
-```python
-# Load splits
-train = pd.read_csv('splits/train.csv')      # 80%
-val = pd.read_csv('splits/val.csv')          # 10%
-test = pd.read_csv('splits/test.csv')        # 10%
-# Verify stratification
-print(train['city_a'].value_counts())
-print(train['annotation_type'].value_counts())
-```
----
-## ⚠️ Quality & Usage Notes
-### Per-City Characteristics
-| Aspect | Bonn | São Paulo | Fortaleza |
-|--------|------|-----------|-----------|
-| **Acquisition** | LiDAR | LiDAR | Photogrammetry |
-| **Accuracy** | ±0.1 m | ±0.15 m | ±0.3 m |
-| **Terrain** | Temperate, rolling | Tropical, flat+steep | Coastal, flat |
-| **Urban density** | Medium | Very high | High (coastal) |
-| **Age** | 2016-2018 | [Year] | 2016-2017 (dated) |
-| **Best for** | Baseline quality | High-res features | Domain robustness |
-### Known Limitations
-1. **CRS Differences**: Each city uses different UTM zones. Users must handle reprojection for cross-city visualization (model training uses normalized patches).
-2. **Resolution Mismatch**: 0.5m (SP) vs 1.0m (Bonn/Fortaleza). Consider resampling or multi-scale training.
-3. **Temporal Gap**: Fortaleza data is 8-9 years old; significant urban change expected.
-4. **Compression**: São Paulo tiles are uncompressed for training speed—expect larger file sizes.
-5. **Water Artifacts**: All cities have elevation artifacts over water bodies (rivers, reservoirs).
-### Recommended Usage
-- **Training**: Use all cities for domain generalization
-- **Validation**: Use handcrafted Bonn annotations for reliable metrics
-- **Testing**: Hold-out test set includes inter-city pairs for generalization assessment
-- **Domain Adaptation**: Bonn→São Paulo→Fortaleza provides increasing difficulty curve
 ---
@@ -503,46 +197,72 @@ Under the following terms:
 When using this dataset, your publication or product must include:
 1. **Dataset citation** (see [Citation](#-citation) section)
-2. **Original source acknowledgments**:
-   - "Bonn data derived from IEEE GeoNRW Dataset © IEEE GRSS"
-   - "São Paulo data derived from GeoSampa © Prefeitura de São Paulo"
-   - "Fortaleza data derived from Municipal Survey © Prefeitura de Fortaleza"
 ---
-## 🐛 Issues & Support
-- **Bug reports**: [GitHub Issues](https://github.com/[your-repo]/GeoMatch-DEM/issues)
-- **Questions**: [GitHub Discussions](https://github.com/[your-repo]/GeoMatch-DEM/discussions)
-- **Email**: [your-email@institution.edu]
-### Known Issues
-- [ ] Fortaleza download in progress (ETA: [date])
-- [ ] Total dataset size TBD after Fortaleza integration
-- [ ] Zenodo DOI pending registration
 ---
-## 🗺️ Related Resources
-- [IEEE GeoNRW Dataset](https://ieee-dataport.org/open-access/geonrw)
-- [GeoSampa Portal](https://geosampa.prefeitura.sp.gov.br/)
-- [FAIR Principles](https://www.go-fair.org/fair-principles/)
-- [ISO 19115-2 Standard](https://www.iso.org/standard/67039.html)
 ---
 ## 📅 Changelog
 ### v1.0 (2026-03-30)
 - Initial release
 - Bonn: 27,000+ handcrafted annotations
-- São Paulo: 700+ tiles processed (0.5m)
-- Fortaleza: Integration in progress
 ---
-**Maintainer**: Sabrina Correa | Universidade Federal de Vicosa | sabrina.correa@ufv.br
-**Last Updated**: 2026-03-30

 - dem
 - depth
 - cv
+pretty_name: MatchGeo-DEM
 size_categories:
 - 1K<n<10K
 ---
 [![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-city 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 standardized 333×333 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 cities across 6 continents — from Antarctica to the Sahara
 - **Standardized format**: All cities processed to 333×333 pixel GeoTIFF tiles
+- **Rich annotations**: 20,000+ verified patch pairs (GER_BN and BRA_SP)
 - **Cross-city evaluation**: Explicit intra-city and inter-city test splits
 - **FAIR compliant**: ISO 19115-2 metadata, DOI registration, open access
 ---
+## 🗺️ Dataset Coverage
+| City | Country | Acquisition | Resolution | Year | Terrain | Labelled |
+|------|---------|-------------|------------|------|---------|----------|
+| Antarctic Peninsula (ATA_MV) | Antarctica | REMA (Satellite) | 1.0 m | 2009–2024 | Polar, ice | ❌ |
+| São Paulo (BRA_SP) | Brazil | Airborne LiDAR | **0.5 m** | 2020 | Tropical, urban | ✅ |
+| Wutai Shan (CHN_WS) | China | UAV SfM | 1.0 m | 2021 | Mountainous | ❌ |
+| El Hierro (ESP_EH) | Spain | Airborne LiDAR | 1.0 m | 2022–2025 | Volcanic, coastal | ❌ |
+| Lahti (FIN_LM) | Finland | LiDAR + Photogrammetry | 2.0 m | 2020–2026 | Temperate, lakes | ❌ |
+| Bonn (GER_BN) | Germany | Airborne LiDAR | 1.0 m | 2016–2018 | Temperate, rolling | ✅ |
+| Sinabung Volcano (IDN_SV) | Indonesia | UAS SfM | 0.87 m | 2018 | Volcanic, tropical | ❌ |
+| Almaty City (KAZ_AC) | Kazakhstan | Pleiades Tristereo | 1.0 m | 2017 | Semi-arid, urban | ❌ |
+| Wadi Al-Akhdar (KSA_WA) | Saudi Arabia | SPOT 6 Stereo | 1.6 m | 2016 | Desert, graben | ❌ |
+| Hebron Fault (NAM_HF) | Namibia | WorldView-3 Stereo | 0.53 m | 2017 | Arid, fault zone | ❌ |
+| Kapiti Coast (NZL_KP) | New Zealand | Airborne LiDAR | 1.0 m | 2010–2025 | Coastal, temperate | ❌ |
+| Tarlac (PHL_TA) | Philippines | Airborne LiDAR | 1.0 m | 2014–2017 | Tropical, flat | ❌ |
+| Grand Canyon (USA_GC) | United States | LiDAR + IfSAR | 10.0 m | 2020–2026 | Desert, canyon | ❌ |
+**Total Size**: ~47.2 GB
+**Total Tiles**: 20,793 (333×333 px patches)
+**Labelled Tiles**: 213 (Bonn)
+**Ground Truth Annotations**: 27,000+ handcrafted point annotations (Bonn)
+---
+## 📊 Per-City Statistics
+| City | Source | Method | Resolution | Labelled Tiles | Total Tiles | CRS | Year | Area |
+|------|--------|--------|------------|----------------|-------------|-----|------|------|
+| **ATA_MV** | REMA | Satellite stereophotogrammetry | 1.0 m | 0 | 5,625 | EPSG:3031 | 2009–2024 | Antarctic Peninsula |
+| **BRA_SP** | GeoSampa | Airborne LiDAR | **0.5 m** | 0 | 558 | EPSG:31983 | 2020 | São Paulo municipal |
+| **CHN_WS** | OpenTopography | UAV SfM | 1.0 m | 0 | 1,076 | EPSG:32649 | 2021 | Wutai Shan region |
+| **ESP_EH** | PNOA-LiDAR | Airborne LiDAR | 1.0 m | 0 | 2,460 | EPSG:3040 | 2022–2025 | El Hierro, Canary Islands |
+| **FIN_LM** | NLS Finland | LiDAR + Photogrammetry | 2.0 m | 0 | 248 | EPSG:3067 | 2020–2026 | Lahti region |
+| **GER_BN** | Geobasis NRW | Airborne LiDAR | 1.0 m | 213 | 1,759 | EPSG:25832 | 2016–2018 | Bonn city limits |
+| **IDN_SV** | OpenTopography | UAS SfM | 0.87 m | 0 | 181 | EPSG:32647 | 2018 | Sinabung Volcano |
+| **KAZ_AC** | OpenTopography | Pleiades Tristereo | 1.0 m | 0 | 887 | EPSG:32643 | 2017 | Almaty City |
+| **KSA_WA** | OpenTopography | SPOT 6 Stereo | 1.6 m | 0 | 3,880 | EPSG:32637 | 2016 | Wadi Al-Akhdar Graben |
+| **NAM_HF** | OpenTopography | WorldView-3 Stereo | 0.53 m | 0 | 1,457 | EPSG:32733 | 2017 | Hebron Fault |
+| **NZL_KP** | LINZ | Airborne LiDAR | 1.0 m | 0 | 1,776 | EPSG:2193 | 2010–2025 | Kapiti Coast |
+| **PHL_TA** | LiPAD | Airborne LiDAR | 1.0 m | 0 | 286 | EPSG:32651 | 2014–2017 | Tarlac region |
+| **USA_GC** | USGS 3DEP | LiDAR + IfSAR | 10.0 m | 0 | 600 | EPSG:6341 | 2020–2026 | Grand Canyon |
+---
+## 📁 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/                     # 333×333 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    # 4,000+ 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/               # 20,000+ 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                  # 333×333 patch extraction
+    ├── fix_nodata.py                  # NoData standardization
+    ├── write_qgis_metadata.py         # QGIS .qmd generator
+    └── cleanup_aux_xml.py             # Remove QGIS temp files
+```
 ---
 | Repository | Link | Notes |
 |------------|------|-------|
+| **Zenodo (Primary)** | [https://doi.org/10.5281/zenodo.19339008](https://doi.org/10.5281/zenodo.19339008) | 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.19339008
 # Or wget/curl (specific file)
+wget https://zenodo.org/record/19339008/files/MatchGeo-DEM-v1.1.zip
 ```
 ---
 ## 🎓 Citation
 If you use this dataset in your research, please cite:
 ```bibtex
+@dataset{correa_2026_matchgeo,
+  author       = {Correa, S. P. L. P. and  Panzini, D 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.,  Panzini, D., 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.19339008
+### Source Dataset Citations
+When using specific cities, also cite the original sources (see `DATASET_DESCRIPTION.md` Section 8 for full BibTeX).
 ---
 When using this dataset, your publication or product must include:
 1. **Dataset citation** (see [Citation](#-citation) section)
+2. **Original source acknowledgments** (per city):
+   - *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 standardised PDAL pipeline:
+1. **Acquisition** — Raw data from source portals (LAZ, DEM, point clouds)
+2. **Preprocessing** — City-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 333×333 pixel grid
+6. **Annotation** — Handcrafted keypoints (Bonn, São Paulo)
+7. **Metadata** — ISO 19115-2 + OGC 23-008r3 compliant per-city 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
+- Reorganised into `data/` folder with per-city metadata
+- Added QGIS `.qmd` metadata files
+- Standardised NoData values to -9999.0
+- Added BigTIFF, tiled, DEFLATE compression
+- Updated to MatchGeo branding
 ### v1.0 (2026-03-30)
 - Initial release
 - Bonn: 27,000+ 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**: 2026-07-06