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

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.

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")
lr_hat, hr_hat = opensr_degradation.main.get_s2like(
    image=hr_early_torch,
    table=hr_early_metadata["sim_histograms"],
    model="gamma_multivariate_normal_50"
)


import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 3, figsize=(10, 5))
ax[0].imshow(hr_early_torch[[3, 1, 2]].permute(1, 2, 0))
ax[0].set_title("NAIP")
ax[1].imshow(hr_hat[[3, 1, 2]].permute(1, 2, 0)*3)
ax[1].set_title("NAIPhat")
ax[2].imshow(lr_hat[[3, 1, 2]].permute(1, 2, 0)*3)
ax[2].set_title("S2like")
plt.show()

CITATION

TODO!