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)

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

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.

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

Kaagle Competition. Use cartographic variables to classify forest categories.

ML model to classify forest cover types using environmental data (UCI Covertype Dataset).

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.