[general]
name=CA-Markov 土地利用变化预测
description=CA-Markov land-use change analysis, model training and scenario prediction based on random-forest transition potentials.
about=CA-Markov land change modelling exposed as three Processing algorithms: transfer
 analysis (fast transition-matrix preview), model training (random-forest transition
 potential maps) and multi-step scenario prediction with accuracy assessment.
 The bundled engine (from the SharpGIS project, MIT licensed) runs in a separate Python
 subprocess, so it never loads GDAL into the QGIS process.
 Requires numpy, scipy, scikit-learn, rasterio and matplotlib; QGIS 3.28 or newer
 (validated on 3.40.15 LTR). All input rasters must share the same grid and CRS.
 Outputs: a prediction GeoTIFF, a report, figures and a metrics JSON, shown in a
 dockable five-tab result panel.
 中文：提供转移分析、训练模型、变化预测三个处理算法，结果以非模态停靠面板展示。
version=0.1.1
qgisMinimumVersion=3.28
qgisMaximumVersion=3.99
author=SharpGIS contributors
email=LQC20504@outlook.com
category=Raster
icon=resources/icon.svg
tags=camarkov,markov,ca,landuse,landcover,raster,processing,prediction
homepage=https://github.com/LQC20504-collab/qgis-camarkov
repository=https://github.com/LQC20504-collab/qgis-camarkov
tracker=https://github.com/LQC20504-collab/qgis-camarkov/issues
hasProcessingProvider=True
experimental=False
deprecated=False
supportsQt6=True
changelog=0.1.1 - Add explicit Bandit nosec annotations for the intentionally used subprocess calls and best-effort cleanup handlers; no functional changes.
