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[general]
name=Downscale Anything with Random Forest
email=firmanafrianto@mail.ugm.ac.id
author=Firman Afrianto
qgisMinimumVersion=3.0
description=Downscale Anything with Random Forest is a QGIS Processing plugin that allows 
    flexible dasymetric downscaling of coarse rasters to user-defined resolutions. 
    The algorithm uses high-resolution covariates and Random Forest regression to redistribute values. 
    Key features include:
    1. User-defined target resolution.
    2. Random Forest regression with fast pixel sampling.
    3. Support for random or spatial-block splitting.
    4. Evaluation metrics (R², RMSE, MAE) and OOB score.
    5. Outputs raster, CSV (evaluation + feature importance), and PNG plots.
about=This plugin provides a practical way to perform machine-learning-based downscaling in QGIS. 
    It is suitable for spatial analysis, environmental modeling, and urban/regional studies. 
    Example use cases include downscaling VIIRS Nighttime Lights (avg_rad), Land Surface Temperature (LST), 
    population density (e.g., WorldPop), and other gridded indicators (e.g., PM2.5, precipitation, 
    socio-economic indices). 
    Created by Firman Afrianto.
version=1.0.0
tracker=https://github.com/firmanaf/DownscaleAnythingRF/issues
repository=https://github.com/firmanaf/DownscaleAnythingRF
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category=Raster Analysis
changelog=1.0.0 - Initial stable release with raster downscaling, CSV evaluation, and diagnostic plots.
tags=downscaling,random forest,raster,spatial analysis,regression,AI,Urban Planning
homepage=https://github.com/firmanaf/DownscaleAnythingRF
icon=icon.png
experimental=False
deprecated=False
qgisMaximumVersion=3.99
plugin_dependencies=scikit-learn,pandas,rasterio,GDAL,matplotlib
