228 Semantic segmentation of aerial satellite imagery using Unet

>> YOUR LINK HERE: ___ http://youtube.com/watch?v=jvZm8REF2KY

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)

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