Pinned Repositories
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.
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.
tnp38's Repositories
tnp38/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.
tnp38/2021-unavco-course-GMTSAR
tnp38/30-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.
tnp38/30DayChartChallenge
tnp38/Airborne_CO2
R tutorial for visualizing airborne, in-situ CO2 from netCDF
tnp38/awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT better.
tnp38/cambodia_cube
A collection of jupyter notebooks and scripts for drought assessment using the open data cube framework
tnp38/CV
:scroll: My Data Driven CV written with R, YAML, & LaTeX
tnp38/deltax_workshop_2022
Delta-X Applications Workshop
tnp38/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.
tnp38/gisruk-rmd
Template for reproducible research paper in Rmd format
tnp38/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.
tnp38/land-cover
Code for training and testing deep learning based land cover models.
tnp38/landcover
Land Cover Mapping
tnp38/Landsat_SMW_LST
Land Surface Temperature from Landsat on Google Earth Engine
tnp38/ML-Course-Notes
🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.
tnp38/ml4eo-bootcamp-2021
Machine Learning for Earth Observation Training of Trainers Bootcamp
tnp38/pandas_exercises
Practice your pandas skills!
tnp38/PlotNeuralNet
Latex code for making neural networks diagrams
tnp38/remote-sensing-textbook
Textbook that provides JavaScript and Python code to create open reproducible remote sensing analyses and workflows in Google Earth Engine.
tnp38/Ressources_spatial_datascience
tnp38/rexamples
worked R examples
tnp38/RUSLE_GEE
The RUSLE model on Google Earth Engine
tnp38/satellite-image-deep-learning
Resources for deep learning with satellite & aerial imagery
tnp38/Time-Series-With-Python
tnp38/tnp38
Config files for my GitHub profile.
tnp38/vegetation_forecasts
A repository for testing ecological forecasting methods of satellite derived vegetation indices