Land Cover Classification Random Forest

Last update: Jan 21, 2022

Overview

Land_Cover_Classification_Random_Forest

You can perform Land Cover Classification on Satellite Images using Random Forest and visualize the result using Earthpy package. Make sure to install the required packages and such as

pip install gdal pip install earthpy

These packages require C++ build dependencies so be sure to install Visual C++ 14.0 or higher. If you want to avoid all hussle of installing packages then just opt for Google Colab. Be sure to upload images and ground truth file from The Sundarbans Dataset (https://earthdata.nasa.gov/worldview/worldview-image-archive/the-sundarbans). For ease I am also uploading the data with this repository

Owner

Dr. Sander Ali Khowaja

Assistant Professor, Department of Telecommunication, Faculty of Engineering and Technology.

GitHub Repository

flexible time-series processing & feature extraction

A corona statistics and information telegram bot.

206 Dec 28, 2022

Python Machine Learning Jupyter Notebooks (ML website)

Python Machine Learning Jupyter Notebooks (ML website) Dr. Tirthajyoti Sarkar, Fremont, California (Please feel free to connect on LinkedIn here) Also

2.6k Jan 03, 2023

Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.

Trading Tesla with Machine Learning and Sentiment Analysis An interactive program to train a Random Forest Classifier to predict Tesla daily prices us

31 Nov 17, 2022

Anytime Learning At Macroscale

On Anytime Learning At Macroscale Learning from sequential data dumps (key) Requirements Python 3.7 Pytorch 1.9.0 Hydra 1.1.0 (pip install hydra-core

8 Mar 29, 2022

A repository to work on Machine Learning course. Select an algorithm to classify writer's gender, of Hebrew texts.

MachineLearning A repository to work on Machine Learning course. Select an algorithm to classify writer's gender, of Hebrew texts. Tested algorithms:

1 Feb 01, 2022

Python module for data science and machine learning users.

dsnk-distributions package dsnk distribution is a Python module for data science and machine learning that was created with the goal of reducing calcu

1 Nov 23, 2021

Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.

25 Dec 28, 2022

Extreme Learning Machine implementation in Python

Python-ELM v0.3 --- ARCHIVED March 2021 --- This is an implementation of the Extreme Learning Machine [1][2] in Python, based on scikit-learn. From

511 Dec 20, 2022

ML-powered Loan-Marketer Customer Filtering Engine

In Loan-Marketing business employees are required to call the user's to buy loans of several fields and in several magnitudes. If employees are calling everybody in the network it is also very length

13 Jul 02, 2022

Pydantic based mock data generation

This library offers powerful mock data generation capabilities for pydantic based models. It can also be used with other libraries that use pydantic as a foundation, for example SQLModel, Beanie and

396 Dec 28, 2022

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 10x Larger Models 10x Faster Trainin

8.4k Dec 30, 2022

SmartSim makes it easier to use common Machine Learning (ML) libraries like PyTorch and TensorFlow

SmartSim makes it easier to use common Machine Learning (ML) libraries like PyTorch and TensorFlow, in High Performance Computing (HPC) simulations and workloads.

139 Jan 01, 2023

Machine Learning toolbox for Humans

Reproducible Experiment Platform (REP) REP is ipython-based environment for conducting data-driven research in a consistent and reproducible way. Main

663 Dec 31, 2022

Provide an input CSV and a target field to predict, generate a model + code to run it.

automl-gs Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learn

1.8k Jan 04, 2023

A machine learning web application for binary classification using streamlit

Machine Learning web App This is a machine learning web application for binary classification using streamlit options this application contains 3 clas

1 Dec 20, 2021

Dual Adaptive Sampling for Machine Learning Interatomic potential.

DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong

6 Jul 06, 2022

LightGBM + Optuna: no brainer

AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin

22 Dec 15, 2022

A machine learning project that predicts the price of used cars in the UK

Car Price Prediction Image Credit: AA Cars Project Overview Scraped 3000 used cars data from AA Cars website using Python and BeautifulSoup. Cleaned t

7 Oct 13, 2022

Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Dec 28, 2022

Implemented four supervised learning Machine Learning algorithms

Implemented four supervised learning Machine Learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs), details see README_Report.

0 Jan 31, 2022