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:
[ Ссылка ]
My Github repo link:
[ Ссылка ]
Dataset from: [ Ссылка ]
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)
