Sandip Rijal's repositories
Awesome-Deep-Learning-Resources
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
Binary_image_classification_deep_learning
Here we classified cell if its malaria infected or not
Canopy_height_model
This code uses data collected by the NEON Airborne Observation Platform to distinguish spectral properties of chronic N and P addition
EnvDatSci22
Code sprints and project folder for CEE/EAR 609
Sandipriz
Config files for my GitHub profile.
test_project
for demo on github
Data-visualization-and-analysis
It has all the code used by the beginner during practice
datacube-core
Open Data Cube analyses continental scale Earth Observation data through time
dataflowr-notebooks
code for deep learning courses
detectree2
Python package for automatic tree crown delineation based on the Detectron2 implementation of Mask R-CNN
eo-learn-examples
Examples of Earth observation workflows that extract valuable information from satellite imagery, giving you hints and ideas how to use the EO data.
FloodNet
Official implementation of https://arxiv.org/abs/2105.08655 paper
gee-tutorials
This code base is collection of codes that are freely available for google earth engine. This is the collection of tutorials prepared by multiple individuals that were shared publicly as documents for learning purposes. These documents has been converted to web pages and are made easy access to the normal users via web page.
Landsat-Classification-Using-Convolution-Neural-Network
Source code and files mentioned in the medium post titled "Is CNN equally shiny on mid-resolution satellite data?" available at https://towardsdatascience.com/is-cnn-equally-shiny-on-mid-resolution-satellite-data-9e24e68f0c08
LiDAR-and-Hyperspectral-Fusion-classification
Landcover classification using the fusion of HSI and LiDAR data.
LLM-cookbook-for-open-science
Cookbook to use LLMs effectively for sciences, in various levels of expertise
python_for_image_processing_APEER
https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
Sandipriz.github.io
Personal Portfolio
Semantic-segmentation-of-LandCover.ai-dataset
An implementation of Deeplabv3plus in TensorFlow2 for semantic land cover segmentation
shinyuieditor
A GUI for laying out a Shiny application that generates clean and human-readable UI code
spatial-prediction-eml
Online tutorial on how to use Ensemble Machine Learning for spatial and spatiotemporal interpolation / predictions