Name: William F Basener
Type: User
Company: Univeristy of Virginia
Bio: Professor of Data Science in the UVA School of Data Science
Emeritus Professor in the RIT School of Mathematical Sciences
Location: Charlottesville, VA
Blog: https://datascience.virginia.edu/
William F Basener's Projects
ACOLITE: generic atmospheric correction module
Bayesian estimation of probability distributions for parameters in IPF models using NetLogo.
This is a GitHub repository for our Bayeisan Machine Learning textbook, which includes the PDF for the book and accompanying Python notebooks.
This is a repository of notebooks for demonstrating and teaching machine learning classification in Python.
Computing conjugate priors using binomial distributions
Context geo-tiles in Python
C++ code for reading hyperspectral images and libraries, whitening data, and target detection.
This is an autoencoder that learns the radiative atmosphere transfer to convert hyperspectral data from radiance to reflectance.
Code for adding GIS info to ENVI files and creating GeoTIFFs
Images collected over Haiti following the 2010 earthquake.
Hyperspectral-Target-Detection
Python tools and GUI for analysis (target detection, PCA, material identification, library management) involving spectral images.
Analysis of the variation in a spectral library of ice in the artic.
Jupyter notebook showing how to build an image classifier with Python and Tensorflow
labelImg2 with rotated box and extra label support
Python Software and Tools for organizing Spectral Signatures, Files, and preparing Libraries
LiDAR processing (reading .las and .laz files, creating binned raster images, etc.)
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
Notebooks and code to accompany Machine Learning for Spectral Imaging course
The presentation and notebook presented in the MAFAT challenge meetup 01.02.2023
This is a neural network in python built using only numpy and similar basic libraries.
This is a repository for experimental code testing some Bayesian Elo methods for predicting NFL outcomes. For houw these might be used, see: https://projects.fivethirtyeight.com/2022-nfl-predictions/
analysis of vegetation spectra, and hopefully classification on imagery, from a spectral library
Label Rotated Rect On Images for training
library for nice plots for spectral libraries using plotly
A spectral library for ASD spectra collected as part of the UVA UPWINS project.
my blog