Update README.md

README.md

@@ -2,15 +2,30 @@ This repository contains preprocessed data from the paper *CROMA: Remote Sensing
 
 We did not create these datasets—if you use them, please cite the original papers!
 
-All data is stored as PyTorch tensors; images are normalized 8-bit integers. To use these data with CROMA, convert tensors to floats and divide by 255.
+All data is stored as PyTorch tensors (inside python dictionaries); images are normalized 8-bit integers. To use these data with CROMA, convert tensors to floats and divide by 255.
 
 **DFC 2020**
+```python
+dfc = torch.load("DFC_preprocessed.pt")  # pixel annotations with 8 classes
+train_images = dfc["train_images"]  # shape (46_152, 14, 96, 96), first 12 channels are S2, last 2 channels are S1
+train_labels = dfc["train_labels"]  # shape (46_152, 96, 96)
+validation_images = dfc["validation_images"]  # shape (8_874, 14, 96, 96), first 12 channels are S2, last 2 channels are S1
+validation_labels = dfc["validation_labels"]  # shape (8_874, 96, 96)
+```
+
 ```bib
 Naoto Yokoya, Pedram Ghamisi, Ronny Hänsch, and Michael Schmitt.
 2020 IEEE GRSS Data Fusion Contest: Global Land Cover Mapping With Weak Supervision.
 IEEE Geoscience and Remote Sensing Magazine, 2020.
 ```
 **Dynamic World (DW) — Expert**
+```python
+dw = torch.load("DynamicWorld_Expert_preprocessed.pt")  # pixel annotations with 9 classes (-1 as not-labeled)
+train_images = dw["train_images"]  # shape (20_422, 14, 96, 96), first 12 channels are S2, last 2 channels are S1
+train_labels = dw["train_labels"]  # shape (20_422, 96, 96)
+validation_images = dw["val_images"]  # shape (51_022, 14, 96, 96), first 12 channels are S2, last 2 channels are S1
+validation_labels = dw["val_labels"]  # shape (51_022, 96, 96)
+```
 ```bib
 Christopher F Brown, Steven P Brumby, Brookie Guzder-Williams, Tanya Birch, Samantha Brooks Hyde,
 Joseph Mazzariello, Wanda Czerwinski, Valerie J Pasquarella, Robert Haertel, Simon Ilyushchenko, et al.
@@ -18,13 +33,62 @@ Dynamic World, Near real-time global 10 m land use land cover mapping.
 Scientific Data, 2022.
 ```
 **MARIDA**
+```python
+marida = torch.load("MARIDA_preprocessed.pt")  # pixel annotations with 15 classes (-1 as not-labeled)
+train_images = marida["train_images"]  # shape (1_682, 11, 96, 96)
+train_labels = marida["train_labels"]  # shape (1_682, 96, 96)
+validation_images = marida["validation_images"]  # shape (1_615, 11, 96, 96)
+validation_labels = marida["validation_labels"]  # shape (1_615, 96, 96)
+```
 ```bib
 Katerina Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Dionysios E Raitsos, and Konstantinos Karantzalos.
 MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data.
 PloS one, 2022.
 ```
 **Canadian Cropland**
+```python
+crops = torch.load("Canadian_Cropland_preprocessed.pt")  # image annotations with 10 classes
+
+# 2017
+train_images_2017 = crops["2017"]["train_imgs"]  # shape (9_898, 13, 65, 65)
+train_labels_2017 = crops["2017"]["train_labels"]  # shape (9_898)
+validation_images_2017 = crops["2017"]["val_imgs"]  # shape (2_075, 13, 65, 65)
+validation_labels_2017 = crops["2017"]["val_labels"]  # shape (2_075)
+test_images_2017 = crops["2017"]["test_imgs"]  # shape (2_138, 13, 65, 65)
+test_labels_2017 = crops["2017"]["test_labels"]  # shape (2_138)
 
+# 2018
+train_images_2018 = crops["2018"]["train_imgs"]  # shape (12_789, 13, 65, 65)
+train_labels_2018 = crops["2018"]["train_labels"]  # shape (12_789)
+validation_images_2018 = crops["2018"]["val_imgs"]  # shape (2_714, 13, 65, 65)
+validation_labels_2018 = crops["2018"]["val_labels"]  # shape (2_714)
+test_images_2018 = crops["2018"]["test_imgs"]  # shape (2_822, 13, 65, 65)
+test_labels_2018 = crops["2018"]["test_labels"]  # shape (2_822)
+
+# 2019a
+train_images_2019a = crops["2019a"]["train_imgs"]  # shape (11_628, 12, 65, 65)
+train_labels_2019a = crops["2019a"]["train_labels"]  # shape (11_628)
+validation_images_2019a = crops["2019a"]["val_imgs"]  # shape (2_486, 12, 65, 65)
+validation_labels_2019a = crops["2019a"]["val_labels"]  # shape (2_486)
+test_images_2019a = crops["2019a"]["test_imgs"]  # shape (2_517, 12, 65, 65)
+test_labels_2019a = crops["2019a"]["test_labels"]  # shape (2_517)
+
+# 2019b
+train_images_2019b = crops["2019b"]["train_imgs"]  # shape (4_094, 13, 65, 65)
+train_labels_2019b = crops["2019b"]["train_labels"]  # shape (4_094)
+validation_images_2019b = crops["2019b"]["val_imgs"]  # shape (821, 13, 65, 65)
+validation_labels_2019b = crops["2019b"]["val_labels"]  # shape (821)
+test_images_2019b = crops["2019b"]["test_imgs"]  # shape (858, 13, 65, 65)
+test_labels_2019b = crops["2019b"]["test_labels"]  # shape (858)
+
+# 2020
+train_images_2020 = crops["2020"]["train_imgs"]  # shape (15_475, 12, 65, 65)
+train_labels_2020 = crops["2020"]["train_labels"]  # shape (15_475)
+validation_images_2020 = crops["2020"]["val_imgs"]  # shape (3_318, 12, 65, 65)
+validation_labels_2020 = crops["2020"]["val_labels"]  # shape (3_318)
+test_images_2020 = crops["2020"]["test_imgs"]  # shape (3_339, 12, 65, 65)
+test_labels_2020 = crops["2020"]["test_labels"]  # shape (3_339)
+```
 ```bib
 Amanda A Boatswain Jacques, Abdoulaye Baniré Diallo, and Etienne Lord.
 Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification.