Upload main.json
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main.json
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1 |
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{
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"type": "Collection",
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"stac_version": "1.0.0",
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"stac_extensions": [
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"https://stac-extensions.github.io/contacts/v0.1.1/schema.json"
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],
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"id": "SEN2NAIPv2-real",
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"description": "<img src='images/taco.png' alt='drawing' width='50%'/>\nThe increasing demand for high spatial resolution in remote sensing imagery has led to the necessity of super-resolution (SR) algorithms that convert low-resolution (LR) images into high-resolution (HR) ones. To address this need, we introduce SEN2NAIP, a large remote sensing dataset designed to support conventional and reference-based SR model. This dataset is a variation of the SEN2NAIP `synthetic large dataset`. We select Sentinel-2 images that fall within a 30-day window of the corresponding NAIP image. Histogram matching is used to ensure consistent color distribution between the LR and HR images. A manual visual inspection is then conducted to discard any poor-quality images. The LR image is generated following the SEN2NAIPmethodology.<center>\n<img src='images/map.png' alt='drawing' width='50%'/>\n</center>\n*The spatial coverage of the dataset. The patch size is LR 130 \u00d7 130 and HR 520 \u00d7 520, respectively.",
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"license": "cc0-1.0",
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"extent": {
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},
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"temporal": {
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"interval": [
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[
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"2015-06-23T00:00:00Z",
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"2023-06-23T00:00:00Z"
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]
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]
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}
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},
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{
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"href": "collection.json",
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"rel": "self",
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"type": "application/json",
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"title": "An ML-STAC Collection JSON file"
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}
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],
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"item_assets": {
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"collection": {
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"title": "An ML-STAC Item JSON file",
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"description": null,
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"type": "application/json",
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"roles": [
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"mlstac-collection"
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]
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},
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"data": {
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"title": "A collection of .mls files",
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"description": null,
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"type": "application/mls; profile=cloud-optimized",
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"roles": [
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"data"
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"metadata": {
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"title": "A collection of .parquet files",
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"type": "application/parquet",
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"roles": [
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"metadata"
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]
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}
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},
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"title": "A larget dataset for super-resolution of Sentinel-2",
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"keywords": [
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"naip",
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"sentinel-2",
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"image-segmentation",
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"deep-learning",
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"remote-sensing"
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"url": "https://www.esa.int/"
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"train": {
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"name": "train",
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"data_files": [
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"https://huggingface.co/datasets/tacofoundation/SEN2NAIPv2-real/resolve/main/train/train.tortilla"
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],
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"data_descriptions": [
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"The training set contains 20000 patches of 520x520 HR pixels and 130x130 LR pixels labels."
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"data_checksum": [
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"metadata_file": "https://huggingface.co/datasets/tacofoundation/SEN2NAIPv2-real/resolve/main/train/metadata.parquet",
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"metadata_description": "The metadata of the training set.",
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"metadata_checksum": 279547
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"data_descriptions": [
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"data_descriptions": [
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"metadata_file": "https://huggingface.co/datasets/tacofoundation/SEN2NAIPv2-real/resolve/main/test/metadata.parquet",
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302 |
+
"full_width_half_max": 34.0
|
303 |
+
},
|
304 |
+
{
|
305 |
+
"name": "B04",
|
306 |
+
"index": 3,
|
307 |
+
"common_name": "red",
|
308 |
+
"description": "Band 4 - Red - 10m",
|
309 |
+
"unit": "nm",
|
310 |
+
"center_wavelength": 664.5,
|
311 |
+
"full_width_half_max": 29.0
|
312 |
+
},
|
313 |
+
{
|
314 |
+
"name": "B05",
|
315 |
+
"index": 4,
|
316 |
+
"common_name": "red edge 1",
|
317 |
+
"description": "Band 5 - Vegetation red edge 1 - 20m",
|
318 |
+
"unit": "nm",
|
319 |
+
"center_wavelength": 704.5,
|
320 |
+
"full_width_half_max": 13.0
|
321 |
+
},
|
322 |
+
{
|
323 |
+
"name": "B06",
|
324 |
+
"index": 5,
|
325 |
+
"common_name": "red edge 2",
|
326 |
+
"description": "Band 6 - Vegetation red edge 2 - 20m",
|
327 |
+
"unit": "nm",
|
328 |
+
"center_wavelength": 740.5,
|
329 |
+
"full_width_half_max": 13.0
|
330 |
+
},
|
331 |
+
{
|
332 |
+
"name": "B07",
|
333 |
+
"index": 6,
|
334 |
+
"common_name": "red edge 3",
|
335 |
+
"description": "Band 7 - Vegetation red edge 3 - 20m",
|
336 |
+
"unit": "nm",
|
337 |
+
"center_wavelength": 783.0,
|
338 |
+
"full_width_half_max": 18.0
|
339 |
+
},
|
340 |
+
{
|
341 |
+
"name": "B08",
|
342 |
+
"index": 7,
|
343 |
+
"common_name": "NIR",
|
344 |
+
"description": "Band 8 - Near infrared - 10m",
|
345 |
+
"unit": "nm",
|
346 |
+
"center_wavelength": 840.0,
|
347 |
+
"full_width_half_max": 114.0
|
348 |
+
},
|
349 |
+
{
|
350 |
+
"name": "B8A",
|
351 |
+
"index": 8,
|
352 |
+
"common_name": "red edge 4",
|
353 |
+
"description": "Band 8A - Vegetation red edge 4 - 20m",
|
354 |
+
"unit": "nm",
|
355 |
+
"center_wavelength": 864.5,
|
356 |
+
"full_width_half_max": 19.0
|
357 |
+
},
|
358 |
+
{
|
359 |
+
"name": "B09",
|
360 |
+
"index": 9,
|
361 |
+
"common_name": "water vapor",
|
362 |
+
"description": "Band 9 - Water vapor - 60m",
|
363 |
+
"unit": "nm",
|
364 |
+
"center_wavelength": 945.0,
|
365 |
+
"full_width_half_max": 18.0
|
366 |
+
},
|
367 |
+
{
|
368 |
+
"name": "B10",
|
369 |
+
"index": 10,
|
370 |
+
"common_name": "cirrus",
|
371 |
+
"description": "Band 10 - Cirrus - 60m",
|
372 |
+
"unit": "nm",
|
373 |
+
"center_wavelength": 1375.5,
|
374 |
+
"full_width_half_max": 31.0
|
375 |
+
},
|
376 |
+
{
|
377 |
+
"name": "B11",
|
378 |
+
"index": 11,
|
379 |
+
"common_name": "SWIR 1",
|
380 |
+
"description": "Band 11 - Shortwave infrared 1 - 20m",
|
381 |
+
"unit": "nm",
|
382 |
+
"center_wavelength": 1613.5,
|
383 |
+
"full_width_half_max": 89.0
|
384 |
+
},
|
385 |
+
{
|
386 |
+
"name": "B12",
|
387 |
+
"index": 12,
|
388 |
+
"common_name": "SWIR 2",
|
389 |
+
"description": "Band 12 - Shortwave infrared 2 - 20m",
|
390 |
+
"unit": "nm",
|
391 |
+
"center_wavelength": 2199.5,
|
392 |
+
"full_width_half_max": 173.0
|
393 |
+
}
|
394 |
+
],
|
395 |
+
"axis": null,
|
396 |
+
"sensor": "Sentinel2 - MSI"
|
397 |
+
},
|
398 |
+
"ml_split_strategy": "stratified",
|
399 |
+
"ml_raw_data_url": "https://eo4society.esa.int/projects/opensr/",
|
400 |
+
"ml_discussion_url": "https://huggingface.co/datasets/tacofoundation/SEN2NAIPv2-real/discussions",
|
401 |
+
"ml_paper": "https://www.google.com/"
|
402 |
+
}
|