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CASE STUDY

• SDM1 : Montane woodcreper - Gecomputation
• SDM1 : Montane woodcreper - Model
• SDM1 : Montane woodcreper - Gecomputation for the Random Forest model using GRASS
• SDM1 : Montane woodcreper - Random Forest Model using GRASS
• SDM2 : Varied Thrush - Model
• Manipulate GSIM files
• Data type in GTiff
• Temporal interpolation of landsat images
• Dynamic Time Warping
• Estimating nitrogen and phosphorus concentrations in streams and rivers
• Estimating nitrogen concentrations in streams and rivers using NN
• Autoencoder (AE), Variational Autoencoder (VAE) and Generative Adversarial Network (GAN)
• LSTM Network
• Estimation of tree height using GEDI dataset - Data explore
• Estimation of tree height using GEDI dataset - Predictors extraction at point location
• Estimation of tree height using GEDI dataset - Random Forest prediction
• Estimation of tree height using GEDI dataset - Support Vector Machine for Regression (SVR) - 2022
• Estimation of tree height using GEDI dataset - Support Vector Machine for Regression (SVR) - 2023
• Estimation of tree height using GEDI dataset - Support Vector Machine for Regression (SVR) - 2024
• Exercise: explore the other parameters offered by the SVM library and try to make the model better. Some suggestions:
• Estimation of tree height using GEDI dataset - Perceptron 1 - 2022
• Estimation of tree height using GEDI dataset - Perceptron 1 - 2023
• Estimation of tree height using GEDI dataset - Perceptron - 2024
• Estimation of tree height using GEDI dataset - Perceptron tree prediction - 2023
• Estimation of tree height using GEDI dataset - Perceptron complete - 2024
• Estimation of tree height using GEDI dataset - Clean Data - Perceptron 2 - 2022
• Estimation of tree height using GEDI dataset - Neural Network 1
• Estimation of tree height using GEDI dataset - Neural Network 1 - 2024
• Neural Nets (pt.3), Interpretability and Convolutional Neural Networks
• Using Multi-layer Perceptron and Convolutional Neural Networks for Satellite image classification - 2022.
• Using Multi-layer Perceptron and Convolutional Neural Networks for Satellite image classification - 2023
• Using CNNs for a image dataset
• Prithvi 100M model
• Proposed exercises
• Autoencoder (AE), Variational Autoencoder (VAE)
• Implementing an Autoencoder
• Autoencoding MNIST
• Section 2
• Section 3 - Generative Models
• Using LSTM for time-series predictions
• Using GPT to implement a Convolutional Neural Networks for Satellite image classification.
• Classification in Python using pyjeo and sklearn
• Google Earth Engine use via Python, containers and other mythical beasts