Forest Fragmentation for Binary and Continuous Forest Cover Rasters

Forest cover type classification/detection using linear support vector machine implemented with gradient descent (from scratch)

ML, NN, NLP, ARIMA, clustering, classification, mapping

Using the "landscapemetrics" R package to calculate metrics inside 5 km buffers.

Combining and cutting spatial variables (tree cover and deforestation data from Global Forest Change) for the neotropical and Central Africa region using buffers in GRASS GIS.

Google Earth Engine code for forest cover change mapping based on the LandTrendr algorithm

Kaagle Competition. Use cartographic variables to classify forest categories.

We do forest analytics using GIS and data visualization, in the hope of saving the planet from the plight of climate change through reforestation by offering a means for citizen-scientists to monitor ongoing efforts

Predict forest cover types using machine learning algorithms on the UCI Covertype dataset. This project uses feature selection and Random Forest classification to classify forest land cover based on environmental and geographic variables. Ideal for beginners learning feature engineering and model evaluation.