Hi Geospatial Enthusiast!

Would you like to classify land cover classification using a deep learning model such as Convolution Neural Network? In this video, I will show you how to use Earth Engine imagery and samples to build a land cover classification model in TensorFlow (Keras).

GEE script: https://code.earthengine.google.com/4...
Colab script: https://colab.research.google.com/dri...

The image used is the 2023 Landsat 8 and 9 composite of Jambi City, Indonesia. The land cover classes I use are builtup, bare land, water, wetland, herbaceous, dry shrub, wet shrub, palm oil, and plantation forest. Other than the spectral band from Landsat, the features or variables I use are the spectral indices and elevation from SRTM. The images/variables and extracted samples will then be exported to Google Drive where they will be mounted and used in Colab.

For the model, I am using a combination of Dense and Convolutional 1dimensional layers. My model is far from perfect, you could improve it more. Also, this is 1 dimensional (tabular), you could use more dimensions using spatial data instead of extracted tabular data.

I hope this video helps with your research or project.

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