Improving Dataset Handling for Sentinel-1 and Sentinel-2 Images (#1)

- Improving Dataset Handling for Sentinel-1 and Sentinel-2 Images (e9615edd5bd54edb0850902cdd4ef517f6969d7b)


Co-authored-by: Raiden Williams <rwilliams@users.noreply.huggingface.co>

README.md

@@ -97,22 +97,53 @@ Ready to start using **[CloudSEN12](https://cloudsen12.github.io/)**?
 
 <br>
 
-### **np.memmap shape information**
+<be>
+
+# **Dataset information, working with np.memmap:**
+
+Sentinel-1 and Sentinel-2 collect images that span an area of 5090 x 5090 meters at 10 meters per pixel.
+This results in 509 x 509 pixel images, presenting a challenge.
+
+**Given each layer is a two-dimensional matrix, true image data is held from pixel (1,1) to (509,509)**
+
+The subsequent images have been padded with three pixels around the image to make the images 512 x 512, a size that most models accept.
+
+To give a visual representation of where the padding has been added:
+x marks blank pixels stored as black (255)
+
+	xxxxxxxxxxxxxx
+	x			xx
+	x           xx
+	x           xx
+	x           xx
+	x           xx
+	xxxxxxxxxxxxxx
+	xxxxxxxxxxxxxx
+
+The effects of the padding can be mitigated by adding a random crop within (1,1) to (509, 509)
+or completing a center crop to the desired size for network architecture.
+
+### The current split of image data is into three categories:
+
+- Training: 84.90 % of total
+- Validation: 5.35 % of total
+- Testing: 9.75 % of total
+
+For the recomposition of the data to take random samples of all 10,000 available images,
+we can combine the np.memmap objects and take random selections at the beginning of each trial,
+selecting random samples of the 10,000 images based on the desired percentage of the total data available.
+
+This approach ensures the mitigation of training bias based on the original selection of images for each category.
 
 <br>
 
+### **Example**
 **train shape: (8490, 512, 512)**
 <br>
 **val shape: (535, 512, 512)**
 <br>
 **test shape: (975, 512, 512)**
-
 <br>
-
-### **Example**
-
-<br>
-
 ```py
 import numpy as np
 
@@ -135,7 +166,6 @@ y = np.memmap('test/manual_hq.dat', dtype='int8', mode='r', shape=test_shape)
 <br>
 
 
-
 This work has been partially supported by the Spanish Ministry of Science and Innovation project 
 PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the 
 **[SemantiX project](https://austria-in-space.at/en/projects/2019/semantix.php)**.