LULC Satellite Image Classification Using Deep Learning: How to Evaluate Model Accuracy in Colab

In this Tutorial, we will learn together how to evaluate the performance of a trained deep learning model for land use land cover classification.
The code examples are available on our GitHub page:
github.com/BEE...
The EuroSat dataset can be found in the following link:
github.com/phe...
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[@tadelemelese4180]

Your teaching method is truly exceptional! I have a question and I'd appreciate it if you could create a video tutorial. How can we annotate or label Landsat or Sentinel satellite images for land use and land cover change analysis to train a deep learning model?

[@BEEiLabTV]

Thanks for your message.
We will work on it.
One way to have labels for change detection in LULC is to classify the image for both years and then compare the pixels one by one to generate a change map.

[@manuelfosalau1367]

Thank you so much for your useful videos and dedication! Could you please also explain to us or give me some useful directions to how could I see the actual land use classification by the deep learning model? One way would be in a GIS software. Is there also another way? For example, to make a map based on a satellite imagery at a certain moment, classifying it in the 10 categories from the EuroSat. Thank you!

[@BEEiLabTV]

Glad To hear it was useful.
Actually, the satellite image classification can be classified in two scenarios. The first one is similar to what you watched on this channel and this is also known as scene classification.
On the other hand, in some cases to have a classified map, you need to predict the class of each pixel. In this scenario, you need to train a model and then pass an image patch to the trained model. The predicted value belongs to the center pixel in the patch image. For instance, if you are going to use Sentinel-2 satellite data, you can use a patch size of 9*9. Notably, the EuroSat dataset with this structure can't be used for pixel-based classification.
We hope to prepare a comprehensive tutorial for this approach as soon as possible.