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| ## Sample Benchmark Datasets | ||
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| | id | id_train | ds_size | Urban Region | Country | Continent | Urban Type | Density | Roof Type | Download | | ||
| |---- |---------|--------|------------------|-------------|--------------|-------------|---------|----------|-----------| | ||
| | 1 | 364 | 399 | Kakuma | Kenya | Africa | Refugee Camp | Sparse | Metal | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_364/preprocessed.zip/) | | ||
| | 2 | 370 | 168 | Denver | USA | America North | Peri-Urban | Grid | Shingles | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_370/preprocessed.zip/) | | ||
| | 3 | 372 | 420 | Montevideo | Uruguay | America South | Urban | Grid | Cement | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_372/preprocessed.zip/) | | ||
| | 4 | 373 | 399 | Montevideo Dense | Uruguay | America South | Urban | Dense | Cement | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_373/preprocessed.zip/) | | ||
| | 5 | 391 | 231 | Kutupalong | Bangladesh | Asia | Refugee Camp | Dense | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_391/preprocessed.zip/) | | ||
| | 6 | 394 | 504 | Gornja Rijeka | Croatia | Europe | Rural | Sparse | Shingles | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_394/preprocessed.zip/) | | ||
| | 7 | 397 | 756 | Melbourne | Australia | Oceania | Urban | Grid | Cement | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_397/preprocessed.zip/) | | ||
| | 8 | 398 | 152 | Pemba | Tanzania | Africa | Rural | Sparse | Metal | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_398/preprocessed.zip/) | | ||
| | 9 | 399 | 294 | Christchurch | New Zealand | Oceania | Peri-Urban | Sparse | Shingles | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_399/preprocessed.zip/) | | ||
| | 10 | 456 | 147 | Pergamino | Argentina | America South | Peri-Urban | Grid | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_456/preprocessed.zip/) | | ||
| | 11 | 459 | 105 | Silvania | Brazil | America South | Rural | Sparse | Shingles | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_459/preprocessed.zip/) | | ||
| | 12 | 462 | 147 | Desa Kulaba | Indonesia | Asia | Rural | Sparse | Metal | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_462/preprocessed.zip/) | | ||
| | 13 | 463 | 168 | Roseau | Dominica | America Central | Peri-Urban | Sparse | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_463/preprocessed.zip/) | | ||
| | 14 | 485 | 147 | Pallabi Dhaka | Bangladesh | Asia | Urban | Dense | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_485/preprocessed.zip/) | | ||
| | 15 | 488 | 168 | Dhaka | Bangladesh | Asia | Urban | Dense | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_488/preprocessed.zip/) | | ||
| | 16 | 489 | 147 | Ggaba | Uganda | Africa | Peri-Urban | Dense | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_489/preprocessed.zip/) | | ||
| | 17 | 529 | 546 | Inagi | Japan | Asia | Peri-Urban | Sparse | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_529/preprocessed.zip/) | | ||
| | 18 | 508 | 226 | Tchiniambi | DRC | Africa | Peri-Urban | Dense | Metal | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_508/preprocessed.zip/) | | ||
| | 19 | 539 | 672 | Staraya Russa | Russia | Europe | Rural | Sparse | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_539/preprocessed.zip/) | | ||
| | 20 | 530 | 189 | Banyuwangi | Indonesia | Asia | Urban | Dense | Shingles | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_530/preprocessed.zip/) | | ||
| | 21 | 526 | 672 | Dzaleka | Malawi | Africa | Refugee Camp | Dense | Metal | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_526/preprocessed.zip/) | | ||
| | 22 | 523 | 252 | Bogota | Colombia | America South | Urban | Grid | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_523/preprocessed.zip/) | | ||
| | 23 | 524 | 315 | Soudure | Niger | Africa | Rural | Dense | Mixed | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_524/preprocessed.zip/) | | ||
| | 24 | 525 | 420 | Quincy | USA | America North | Peri-Urban | Grid | Shingles | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_525/preprocessed.zip/) | | ||
| | 25 | 528 | 756 | Ngaoundere | Cameroon | Africa | Peri-Urban | Grid | Metal | [Download](https://fair-dev.hotosm.org/api/v1/workspace/download/training_528/preprocessed.zip/) | | ||
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| Source and Credit : Anna Zanchetta | ||
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| ## **How to Get the Data** | ||
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| You can use the following Python script to generate download links for all available training datasets: | ||
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| ```python | ||
| # List of training IDs from the table | ||
| training_ids = [ | ||
| 364, 370, 372, 373, 391, 394, 397, 398, 399, 456, | ||
| 459, 462, 463, 485, 488, 489, 529, 508, 539, 530, | ||
| 526, 523, 524, 525, 528 | ||
| ] | ||
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| base_url = "https://fair-dev.hotosm.org/api/v1/workspace/download/training_{}/preprocessed.zip/" | ||
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| download_links = [base_url.format(train_id) for train_id in training_ids] | ||
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| for link in download_links: | ||
| print(link) | ||
| ``` | ||
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| if you want to get area of interest for those training area , it would be inside `training_{}/aois.geojson` | ||
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| & Training details can be found from API call like this : https://fair-dev.hotosm.org/api/v1/training/364/ | ||
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| # **Dataset Structure & Download Guide** | ||
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| ## **Overview** | ||
| This dataset consists of **256x256 pixel image tiles** that follow the **Mercator tiling scheme**. Each tile is associated with: | ||
| - **Imagery ("chips/")** | ||
| - **Vector labels ("labels/")** | ||
| - **Binary masks ("binarymasks/") (optional)** | ||
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| The filenames follow this **naming convention**: | ||
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| **OAM-{mercantile_tile_x}-{mercantile_tile_y}-{zoom_level}.ext** | ||
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| Where: | ||
| - **OAM**: Prefix indicating OpenAerialMap (or similar sources). | ||
| - **mercantile_tile_x / mercantile_tile_y**: Tile coordinates in the Mercator grid. | ||
| - **zoom_level**: The zoom level of the tile. | ||
| - **ext**: `.tif` (imagery), `.geojson` (labels), `.mask.tif` (binary masks). | ||
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| --- | ||
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| ## **Folder Structure** | ||
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| ### **1. Chips (`chips/`)** | ||
| - **Contains satellite/aerial imagery tiles**. | ||
| - Each `.tif` file corresponds to a **specific Mercator grid tile**. | ||
| - Example: `OAM-1251460-1026614-21.tif`. | ||
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| ### **2. Labels (`labels/`)** | ||
| - **Contains vector annotations (GeoJSON)**. | ||
| - Each file is clipped to the **exact boundary** of the corresponding image tile. | ||
| - Example: `OAM-1251450-1026604-21.geojson` matches `OAM-1251450-1026604-21.tif`. | ||
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| ### **3. Binary Masks (`binarymasks/`)** | ||
| - **Rasterized (burned) version of the labels**. | ||
| - Binary format (**0/1**) indicating building footprints. | ||
| - **Not required** if users prefer to generate masks from `chips/` and `labels/`. | ||
| - Example: `OAM-1251456-1026606-21.mask.tif`. | ||
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