dzetsaka VS Machine-Learning

Compare dzetsaka vs Machine-Learning and see what are their differences.

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dzetsaka Machine-Learning
2 1
74 404
- -
0.0 0.0
over 2 years ago 3 months ago
Python Python
GNU General Public License v3.0 only MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

dzetsaka

Posts with mentions or reviews of dzetsaka. We have used some of these posts to build our list of alternatives and similar projects.
  • How would i clean manmade features like airports and canals from gis data from an entire country.
    1 project | /r/gis | 21 Apr 2023
  • Remote Sensing and Mangroves
    1 project | /r/remotesensing | 29 Jan 2021
    You can use Sentinel 2 data (https://scihub.copernicus.eu/) and perform a supervised classification in QGIS using dsetzaka plugin (https://github.com/nkarasiak/dzetsaka) and extract boundaries. First remember to install scikit learn into QGIS (be aware to install it into QGIS python repository) otherwise you won't be able to run random forest from the plugin. Then do the same for other dates and create a time serie. At this point you can easily measure area changes. Id suggest to apply NDVI, NDMI, GCI and SIPI to assess health status of vegetation

Machine-Learning

Posts with mentions or reviews of Machine-Learning. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing dzetsaka and Machine-Learning you can also consider the following projects:

qgis-latlontools-plugin - QGIS tools to capture and zoom to coordinates using decimal, DMS, WKT, GeoJSON, MGRS, UTM, UPS, GEOREF, ECEF, H3, and Plus Codes notation. Provides external map support, MGRS & Plus Codes conversion and point digitizing tools.

sklearn-project-template - Machine learning template for projects based on sklearn library.

pycm - Multi-class confusion matrix library in Python

autoscraper - A Smart, Automatic, Fast and Lightweight Web Scraper for Python

RubixML - A high-level machine learning and deep learning library for the PHP language.

Gumbi - Gaussian Process Model Building Interface

qgis-densityanalysis-plugin - QGIS plugin that automates the creation of density heatmaps with a heatmap explorer to examine the areas of greatest concentrations. It includes H3, geohash, and polygon density map algorithms along with several styling algorithms.

gmr - Gaussian Mixture Regression

svm-pytorch - Linear SVM with PyTorch

crispy - Crispy is a machine-learning algorithm to make video-games montages efficiently. It uses a neural network to detect highlights in the video-game frames

SuperStyl - Supervised Stylometry

nyc_traffic_flask - Flask App with leaflet.js that can perform NYC Traffic Prediction