tim-r413 Goto Github PK

cover

This code develops calibration models using linear regression models with in-situ field data. The calibration models are then used to predict biomass (log), nitrogen percent, and nitrogen content for Landsat images from 2006-2016. Model results and data tables are output as separate files for each field season (i.e. winter and spring seasons).

cover-crop-classification

Repository housing the data and code for a Machine Learning model that classifies species of cover crops using Tensor Flow

cover-crop-image-segmentation

Repository containing the data, code, and results for the CoverCrop Image Segmentation project with Tensorflow

deep-learning-handbook

A series of modules meant to assist in learning different areas of deep learning through interactive tutorials.

gee-bm-and-nitrogen-prediction

gypsum

mcnn-seq

Code for Deeply synergistic optical and SAR time series for crop dynamic monitoring

semantic-shapes

A semantic segmentation pipeline for custom image annotation

senior-design-2020-gee-app

Repository for the the GEE app design project

tensorflow-windows-wheel

Tensorflow prebuilt binary for Windows