228 - Semantic segmentation of aerial (satellite) imagery using U-net

This video demonstrates the process of pre-processing aerial imagery (satellite) data, including RGB labels to get them ready for U-net. The video also demonstrates the process of training a U-net and making predictions.

Code generated in the video can be downloaded from here:
https://github.com/bnsreenu/python_fo...

My Github repo link:
https://github.com/bnsreenu/python_fo...

Dataset from: https://www.kaggle.com/humansintheloo...

The dataset consists of aerial imagery of Dubai obtained by MBRSC satellites and annotated with pixel-wise semantic segmentation in 6 classes. The total volume of the dataset is 72 images grouped into 6 larger tiles. The classes are:

Building: #3C1098
Land (unpaved area): #8429F6
Road: #6EC1E4
Vegetation: #FEDD3A
Water: #E2A929
Unlabeled: #9B9B9B

Images come in many sizes: 797x644, 509x544, 682x658, 1099x846, 1126x1058, 859x838, 1817x2061, 2149x1479​

Need to preprocess so we can capture all images into numpy arrays. ​
Crop to a size divisible by 256 and extract patches.​

​Masks are RGB and information provided as HEX color code.​

Need to convert HEX to RGB values and then convert RGB labels to integer values and then to one hot encoded. ​

​Predicted (segmented) images need to converted back into original RGB colors. ​

​Predicted tiles need to be merged into a large image by minimizing blending artefacts (smooth blending). ​(Next video)