Name: Ethan Kyzivat
Type: User
Company: Brown University Department of Earth, Environmental & Planetary Sciences
Bio: Daly Postdoctoral Fellow at Harvard Earth & Planetary Science - MethaneSAT, inverse modeling, aquatic sciences.
Twitter: ethankyzivat
Location: Providence, RI
Blog: ethan.kyziv.at
Ethan Kyzivat's Projects
Functions for processing of river discharge data made through acoustic doppler current profiler measurements.
Cloud utils for Planet UDM-2
Deep Learning for Satellite Imagery
Scripts for working with Hydrolite DFX and similar echosounders
A conda environment and docker container to support using earth and environmental data in Python.
My professional website
Github user page for Ethan Kyzivat
πβ¨ Help beginners to contribute to open source projects
A user-space file system for interacting with Google Cloud Storage
A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets (for EDK pull requests).
A set of bash shell scripts using gdal for raster manipulation.
test repository
A growing set of image processing functions in matlab
practicing jupyter notebook
Extrapolating lake-size distribution to non-inventoried lakes and calculating vegetation coverage and methane emissions.
Scripts for analyzing land cover raster data
Search and download Landsat scenes from EarthExplorer.
Source material for Python Like You Mean it
Large language model tools for reading scientific papers.
a library of matlab functions for processing raster-format (spatially referenced gridded) satellite and climate model data in MatlabΒ©
Utility codes to read MethaneSAT/AIR L1B / L2 / L3 files and produce quick diagnostic plots
Code and resources for the Northern Change Research Lab group at Brown.
LLM Chain for answering questions from documents with citations
Working version of Basic Super-Resolution Toolbox for class project.
Modifications to https://github.com/HMS-IDAC/PixelClassifier
Simple CLI for Planet ordersV2 API
PyTrx is a Python object-oriented programme created for the purpose of calculating real-world measurements from oblique images and time-lapse image series. Its primary purpose is to obtain velocities, surface areas, and distances from oblique, optical imagery of glacial environments.
Random forest classification for wetland vegetation from synthetic aperture radar dataset
Python modules and IPython Notebooks, for the book "Introduction to Statistics With Python"