![]() Using the mean IOU loss function, we find that most of the times the first few epochs of the model tend to get stuck in a local minimum, In addition, we will be using a custom loss function of mean IOU to evaluate our model. We might as well add some dropout layers in-between some of the layers. Our UNET architecture for the 8 bands data will be as follows: The models are created at script UNET_Model.py. I've created 2 U-NET segmentation models, and trained both of them. ![]() Since the dataset includes around 7000 3-band images of size (406,438,3) and their corresponding 8-band images of size (101,110,8), To transform these polygons into masked images, we will be using the Create_Masks_For_Data.py script.Ī 3 band image and its corresponding mask look like: The labeled masks are given in geotiff format - meaning that each mask is basically a polygon whose verticesĪre given by geographic coordinates. Next, we will create the corresponding masks for the data. To do so, first of all we will need to split the data into the different directories - using the Split_Data.py script. The lower 'img' directories will hold the images, and are needed for the data iterators. geojson files that are used to create the corresponding mask for eachģ band image and its 8 band counterpart, and we will not use the vectors directly for training the model. Where each of the Training, Validation and Test data groups will hold the 3 band images,Ĩ band images, and corresponding masks, seperate from each other. We would like our preprocessed data to be stored as follows: 'SpaceNet 1: Building Detection v1' Dataset fromĪWS, using AWS CLI as explained in the following link. Spacenet-Building-Detection Dependencies:
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