README.md

---
license: cc-by-4.0
---

<center>
  <img src="demo/logo.png" width=85%>
</center>


# SEN2NAIP

The 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 training. SEN2NAIP is structured into two components to provide a broad spectrum
of research and application needs. The first component comprises a cross-sensor dataset of 2,851 pairs of LR images from Sentinel-2 L2A and HR
images from the National Agriculture Imagery Program (NAIP). Leveraging this dataset, we developed a degradation model capable of converting NAIP
images to match the characteristics of Sentinel-2 imagery (S2like). Subsequently, this degradation model was utilized to create the second component,
a synthetic dataset comprising 17,657 NAIP and S2like image pairs. With the SEN2NAIP dataset, we aim to provide a valuable resource that facilitates
the exploration of new techniques for enhancing the spatial resolution of Sentinel-2 satellite imagery.

# DOWNLOAD DATASET

```
from huggingface_hub import hf_hub_download

# Donwload cross-sensor dataset
hf_hub_download(
    repo_id="isp-uv-es/SEN2NAIP",
    repo_type="dataset",
    filename="cross-sensor/cross-sensor.zip"
)

# Donwload synthetic dataset
for i in range(1, 19):
    hf_hub_download(
        repo_id="isp-uv-es/SEN2NAIP",
        repo_type="dataset",
        filename="synthetic/synthetic_%02d.zip" % i
    )
```



# REPRODUCIBLE EXAMPLES

## Load cross-sensor dataset

```{python}
import rioxarray
import torch

DEMO_PATH = "https://huggingface.co/datasets/isp-uv-es/SEN2NAIP/resolve/main/demo/"

cross_sensor_path = DEMO_PATH + "cross-sensor/ROI_0000/"
hr_data = rioxarray.open_rasterio(cross_sensor_path + "hr.tif")
lr_data = rioxarray.open_rasterio(cross_sensor_path + "lr.tif")
hr_torch = torch.from_numpy(hr_data.to_numpy()) / 255
lr_torch = torch.from_numpy(lr_data.to_numpy()) / 10000
```


## Load Synthetic dataset

Available methods: **vae_histogram_matching**, **vae_histogram_matching**, **gamma_multivariate_normal_90**, **gamma_multivariate_normal_75**, **gamma_multivariate_normal_50**,
**gamma_multivariate_normal_25**, **gamma_multivariate_normal_10**.



```{python}
import opensr_degradation
import rioxarray
import datasets
import requests
import tempfile
import torch
import json


def load_metadata(metadata_path: str) -> dict:
    tmpfile = tempfile.NamedTemporaryFile(suffix=".json")
    with requests.get(metadata_path) as response:
        with open(tmpfile.name, "wb") as file:
            file.write(response.content)
        metadata_json = json.load(open(tmpfile.name, "r"))
    return metadata_json

DEMO_PATH = "https://huggingface.co/datasets/isp-uv-es/SEN2NAIP/resolve/main/demo/"

# Synthetic LR and HR data ------------------------------
synthetic_path = DEMO_PATH + "synthetic/ROI_0001/"

hr_early_data = rioxarray.open_rasterio(synthetic_path + "early/01__m_4506807_nw_19_1_20110818.tif")
hr_early_torch = torch.from_numpy(hr_early_data.to_numpy()) / 255
hr_early_metadata = load_metadata(synthetic_path + "late/metadata.json")
hr_early_torch_hat = opensr_degradation.main.predict_table(
    hr_early_torch, hr_early_metadata["sim_histograms"], "gamma_multivariate_normal_50"
)

hr_late_data = rioxarray.open_rasterio(synthetic_path + "late/02__m_4506807_nw_19_060_20210920.tif")
hr_late_torch = torch.from_numpy(hr_late_data.to_numpy()) / 255
hr_late_metadata = load_metadata(synthetic_path + "late/metadata.json")
hr_late_torch_hat = opensr_degradation.main.predict_table(
    hr_late_torch, hr_late_metadata["sim_histograms"], "gamma_multivariate_normal_50"
)

import matplotlib.pyplot as plt
fig, ax = plt.subplots(2, 2, figsize=(10, 5))
ax = ax.flatten()
ax[0].imshow(hr_early_torch[[3, 1, 2]].permute(1, 2, 0))
ax[0].set_title("Original")
ax[1].imshow(hr_early_torch_hat[[3, 1, 2]].permute(1, 2, 0)*3)
ax[1].set_title("Degraded")
ax[2].imshow(hr_late_torch[[3, 1, 2]].permute(1, 2, 0))
ax[2].set_title("Original")
ax[3].imshow(hr_late_torch_hat[[3, 1, 2]].permute(1, 2, 0)*3)
ax[3].set_title("Degraded")
# remove axis and space
for a in ax:
    a.axis("off")
plt.tight_layout()
plt.show()
```

<center>
  <img src="demo/image_demo.png" width=75%>
</center>

# CITATION

TODO!