Deep Learning with TensorFlow:
Tutorials for modeling LULC.¶
Authored by Development Seed engineers Lillianne Thomas and Ryan Avery
These materials are designed to provide TensorFlow expertise via tutorials and science support, vis a vis suggestions for acquisition and processing of data inputs, training, testing, and evaluation of TensorFlow models as well as different TensorFlow / deep learning techniques demoed in Colab notebooks using real data.
The content of this workshop assumes general familiarity with geospatial data such as satellite imagery, raster and vector formats, file formats such as GeoTIFF and GeoJSON, the python programming language and Google Colab. Having knowledge of numpy, rasterio, geopandas and sci-kit learn is a plus.
Links
How to run the notebook code
A major advantage of executable books is that the reader may enjoy running the source code, modifying them and playing around. No downloading, installation or configuration are required. Simply go to
https://developmentseed.github.io/tensorflow-eo-training/docs/index.html,
and in the left menu select any topic, click the “rocket” icon at the top right of the screen, and choose “Colab.” This will launch the page in a virtual runtime environment hosted by Google. From there, the code can be run using a free GPU.
For local running, the code for each topic in the form of Jupyter notebooks can be downloaded by clicking the “arrow-down” icon at the top right of the screen.
How to access the data
These tutorials will make use of open source data hosted on Radiant Earth MLHub. Please register an account with MLHub and obtain your unique API key in advance of starting these tutorials.
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Built with Jupyter Book 2.0 tool set, as part of the ExecutableBookProject.
ISBN: *(tbd)