OpenClimateGIS is a set of geoprocessing and calculation tools for CF-compliant climate datasets.
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Updated
Mar 18, 2023 - Python
OpenClimateGIS is a set of geoprocessing and calculation tools for CF-compliant climate datasets.
This is a OpenMetadata custom connector to any spatial data format which can be read through fiona (the OGR part of the excellent GDAL library).
Open source canopy classification system
Performs a Koppen Geiger Classification using just NC files with temperature and precipitation as input.
An interactive python-based tool to download time-series data for all sensors available on Google Earth Engine's data catalog.
A Python module for remote sensing index calculations and processing. Mostly used for my experimentation with new concepts and techniques.
Processamento de imagens de satélite via RasterIO, Numpy e Matplotlib.
GlobeAnim is a Python tool that projects a GeoTIFF file onto a globe and rotates it, creating an animated GIF.
Criador de mosaicos de imagens Sentinel-2 RGB e IRG para Portugal, com serviço WMS, com suporte temporal
基于GDAL开发的快速功能函数库。 For GDAL secondary packaging function library.
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Airflow task & operator running python code in conda/mamba environments
Presentation for WLIA 2022: PDF Maps: From Static to Interactive to “Wait, I Didn’t Know PDFs Could Do That!”
py-location api of Indonesia Administration boundaries
This repository presents the Colour Pattern Regression (CPR) algorithm QGIS3 plugin. The code determines the relationship between aerial images and raster maps according to the decomposition into RGB spaces of the aerial images and the calculation of a linear regression with the raster map using three coefficients - one for each RGB space.
[This project was completed in April 2023] GeocoderPL is an application written in Python, which can be used for geocoding address points in Poland along with the possibility to display basic information about a given address point and the building assigned to this address.
This is a script that reads in Landsat-8 data, Esri Sentinel-2 10m land cover time series data and train a random forest classification algorithm to estimate fractional built cover at 30m scale. The trained model can be used to produce fractional land cover for other regions.
The repository is a duplicate of the local folder which contains codes created by Yuanzhan Gao (yg8ch@virginia.edu) to conduct NDVI pixel value extractions on NASA's satellite data. Please see the README file for more information.
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