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)