upload airquality_s5p dataset

airquality_s5p/airquality_s5p.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:96d82c0e7b8678cd9d8168324a7f2fae3a3f7ed03e97911a5c8b5738eecb6880
+size 272559465

airquality_s5p/dataset_airquality_s5p.py

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+import torch
+from torch.utils.data import DataLoader, Dataset
+import cv2
+import os
+import rasterio
+import numpy as np
+from pyproj import Transformer
+from datetime import date
+
+class S5P_EEAAirQualityDataset(Dataset):
+    '''
+    1973/494 train/test air quality dataset for NO2 and O3, measure from S5P, label from EEA
+    annual: 1x56x56x1 annual avg
+    seasonal: 4x56x56x1 seasonal avg
+    s5p nodata: -inf
+    label nodata: -3.4e38 # this needs to be masked out for loss and metric calculation
+    '''
+
+    def __init__(self, root_dir, modality='no2', mode='annual', split='train', meta=False):
+        self.root_dir = root_dir
+        self.mode = mode
+        self.modality = modality
+
+        if self.mode == 'annual':
+            mode_dir = 's5p_annual'
+        elif self.mode == 'seasonal':
+            mode_dir = 's5p_seasonal'
+
+        self.img_dir = os.path.join(root_dir, modality, split, mode_dir)
+        self.label_dir = os.path.join(root_dir, modality, split, 'label_annual')
+
+        self.fnames = sorted(os.listdir(self.label_dir))
+   
+        self.meta = meta
+        if self.meta:
+            self.reference_date = date(1970, 1, 1)
+
+    def __len__(self):
+        return len(self.fnames)
+    
+    def __getitem__(self, idx):
+        fname = self.fnames[idx] # label filename
+        label_path = os.path.join(self.label_dir, fname)
+        img_path = os.path.join(self.img_dir, fname.replace('.tif', ''))
+        img_fnames = os.listdir(img_path)
+        img_paths = []
+        for img_fname in img_fnames:
+            img_paths.append(os.path.join(img_path, img_fname))
+        
+        # img
+        imgs = []
+        meta_infos = []
+        for img_path in img_paths:
+            with rasterio.open(img_path) as src:
+                img = src.read(1)
+                img = cv2.resize(img, (56,56), interpolation=cv2.INTER_CUBIC)
+                img[np.isnan(img)] = 0
+                img = torch.from_numpy(img).float()
+                img = img.unsqueeze(0)
+
+                if self.meta:
+                    cx,cy = src.xy(src.height // 2, src.width // 2)
+                    crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326')
+                    lon, lat = crs_transformer.transform(cx,cy)
+                    #lon, lat = cx, cy
+                    img_fname = os.path.basename(img_path)
+                    date_str = img_fname.split('_')[0][:10]
+                    date_obj = date(int(date_str[:4]), int(date_str[5:7]), int(date_str[8:10]))
+                    delta = (date_obj - self.reference_date).days
+                    meta_info = np.array([lon, lat, delta, 0]).astype(np.float32)
+                else:
+                    meta_info = np.array([np.nan,np.nan,np.nan,np.nan]).astype(np.float32)
+
+            imgs.append(img)
+            meta_infos.append(meta_info)
+        if self.mode == 'seasonal':
+            # pad to 4 images if less than 4
+            while len(imgs) < 4:
+                imgs.append(img)
+                img_paths.append(img_path)
+                meta_infos.append(meta_info)
+        # label
+        with rasterio.open(label_path) as src:
+           label = src.read(1)
+           label = cv2.resize(label, (56,56), interpolation=cv2.INTER_CUBIC) # 0-650
+           label[label<-1e10] = np.nan
+           label[label>1e10] = np.nan
+           #label[np.isnan(label)] = -1e10
+           label = torch.from_numpy(label.astype('float32'))
+        #nan_mask = (label > -1e10)
+
+        if self.mode == 'annual':
+            return imgs[0], meta_infos[0], label#, nan_mask #,label_path
+        elif self.mode == 'seasonal':
+            return imgs[0], imgs[1], imgs[2], imgs[3], meta_infos[0], meta_infos[1], meta_infos[2], meta_infos[3], label#, nan_mask #,label_path
+        
+if __name__ == '__main__':
+    dataset = S5P_EEAAirQualityDataset(root_dir='./airquality_s5p', modality='no2', mode='annual', split='train')
+    dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
+    for i, data in enumerate(dataloader):
+        print(data[0].shape, data[1].shape, data[2].shape, data[3].shape)
+        break