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assessing_cnn_rf_and_svm_geospatial_ai_deep_learning_models

Explore the power of Geospatial AI with an in-depth assessment of CNN, RF, and SVM Deep Learning models. This GitHub repository contains code and resources for comparing and contrasting these models in geospatial applications. Enhance your understanding of AI-driven geospatial analysis.

estimating_crop_evapotranspiration_uncertainty_with_hrmet

Simulating crop evapotranspiration uncertainty by perturbing weather inputs to the HRMET model in Jupyter Notebooks. Focuses on temperature and radiation errors impacting spatial ET estimates via Monte Carlo methods.

hrmet_in_python

Translating the HRMET evapotranspiration model to Python and applying it over gridded crop fields to estimate spatial ET uncertainty resulting from weather input errors. Focuses on perturbing inputs like temperature and radiation based on sensor specs and propagating through the Penman-Monteith model via Monte Carlo simulation.

kwakuopokuware401

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simulating_land_use_change_with_markov_chains_and_python