Introduction In the previous post, we have successfully trained a model for lake segmentation. In this post, we are going to have a look at how its performance can be quantitatively and qualitatively assessed as well as how it can… Read More »Semantic Segmentation of Geospatial Imagery with Deep Learning Part 6 – Model Inference and Evaluation
Introduction In the previous post, we have defined the U-Net architecture and defined a class to access our training samples. Hence, we are ready to train the model. This first requires to split up our image crops into different datasets… Read More »Semantic Segmentation of Geospatial Imagery with Deep Learning Part 5 – Training the Model
Introduction In the previous post, we have successfully created image crops that we are going to use for training the U-Net model. In this post, we are going to talk about how the U-Net architecture looks like and how it… Read More »Semantic Segmentation of Geospatial Imagery with Deep Learning Part 4 – U-Net
Introduction In the previous post of this series, we have acquired the relevant datasets and transformed the lake vector dataset into a raster file that aligns with the SWISSIMAGE dataset. In this blog post, we are going to use these… Read More »Semantic Segmentation of Geospatial Imagery with Deep Learning Part 3 – Training Samples
Introduction In the previous post, we have set up our working environment. In this blog post, we are going to have a look at from where we can obtain suitable training datasets. Training a deep learning model is only possible… Read More »Semantic Segmentation of Geospatial Imagery with Deep Learning Part 2 – Data Acquisition