tnp38 Goto Github PK

2021-unavco-course-gmtsar

30-days-of-python

30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.

30daychartchallenge

airborne_co2

R tutorial for visualizing airborne, in-situ CO2 from netCDF

awesome-chatgpt-prompts

This repo includes ChatGPT prompt curation to use ChatGPT better.

cambodia_cube

A collection of jupyter notebooks and scripts for drought assessment using the open data cube framework

cv

:scroll: My Data Driven CV written with R, YAML, & LaTeX

deltax_workshop_2022

Delta-X Applications Workshop

from-0-to-research-scientist-resources-guide

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

gisruk-rmd

Template for reproducible research paper in Rmd format

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

land-cover

Code for training and testing deep learning based land cover models.

landcover

Land Cover Mapping

landsat_smw_lst

Land Surface Temperature from Landsat on Google Earth Engine

ml-course-notes

🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.

ml4eo-bootcamp-2021

Machine Learning for Earth Observation Training of Trainers Bootcamp

pandas_exercises

Practice your pandas skills!

plotneuralnet

Latex code for making neural networks diagrams

remote-sensing-textbook

Textbook that provides JavaScript and Python code to create open reproducible remote sensing analyses and workflows in Google Earth Engine.

ressources_spatial_datascience

rexamples

worked R examples

rusle_gee

The RUSLE model on Google Earth Engine

satellite-image-deep-learning

Resources for deep learning with satellite & aerial imagery

satellite-imagery-road-extraction

Developed a Software for semantic segmentation of remote sensing imagery using Fully Convolutional Networks (FCNs). Initially, this software developed for extracting the road network from high-resolution remote sensing imagery. And now, this software can be used to extract various features (Semantic segmentation of remote sensing imagery). This project can also extract from Vector Data. This is part of my Internship at ISRO (Indian SPace Research Organization)'s NRSC (National Remote Sensing Centre) campus.

time-series-with-python

tnp38

Config files for my GitHub profile.

vegetation_forecasts

A repository for testing ecological forecasting methods of satellite derived vegetation indices