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--- |
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license: mit |
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task_categories: |
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- image-feature-extraction |
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language: |
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- en |
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tags: |
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- sentinel |
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- satelite |
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- photo |
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- earthloc |
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pretty_name: 'EarthLoc 2021 Database ' |
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size_categories: |
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- 100K<n<1M |
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--- |
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# 🌍 Sentinel 2021 Image WebDataset |
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This dataset contains 150k 1024x1024 satellite images accross (9,10,11) zooms and bounded within (-60,60) latitude. |
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Stored using the [WebDataset](https://github.com/webdataset/webdataset) format. |
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The data is **sharded across 11 `.tar` archives**, and each sample contains: |
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- A JPEG image `.jpg` |
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- A unique key `__key__` corresponding to the original image path |
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- Companion **`.index`** file for fast random access stored next to the shard |
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This key encodes all relevant **metadata** about the image, including its bounding box, nadir, zoom, area parameters. |
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## Note this is not the whole data used to train the model. 3 more datasets are needed for years 2018,2019,2020 you can download them from https://github.com/gmberton/EarthLoc if you want to train from scratch. |
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--- |
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## 🗂️ Dataset Structure |
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Each shard (e.g., `shard-000000.tar`) contains up to 15,000 samples. Every sample includes: |
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- `jpg`: The image bytes (decoded automatically) |
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- `__key__`: A string derived from the image's original path |
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The key format encodes metadata in the filename using the following structure: |
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@ lat1 @ lon1 @ lat2 @ lon2 @ lat3 @ lon3 @ lat4 @ lon4 |
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@ image_id @ timestamp @ nadir_lat @ nadir_lon @ sq_km_area @ orientation @.jpg |
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> Commas (`,`) in the keys have been used to replace dots (`.`) due to WebDataset's format. You’ll need to reverse this replacement (`replace(',', '.')`) when decoding. |
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For more details on how the key structure encodes geospatial metadata, refer to the [EarthLoc repository](https://github.com/gmberton/EarthLoc). |
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--- |
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## 🧪 Example: Displaying the First Image in a Shard |
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You can inspect a sample using the following code in a Jupyter notebook: |
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```python |
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import webdataset as wds |
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from PIL import Image |
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import matplotlib.pyplot as plt |
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# Path to a WebDataset .tar file |
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tar_path = "./shard-000000.tar" |
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# Create a WebDataset iterator |
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dataset = wds.WebDataset(tar_path).decode("pil") |
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# Load the first sample |
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sample = next(iter(dataset)) |
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# Access image and key |
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image = sample["jpg"] # PIL Image |
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key = sample["__key__"].replace(',', '.') |
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# Display the image |
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plt.imshow(image) |
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plt.axis("off") |
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plt.title(f"Key: {key}") |
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plt.show() |
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# Print the key (for metadata parsing) |
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print(f"Key: {key}") |
