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align=\"center\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Python-3.9%2B-blue\" alt=\"Python versions\"\u003e\n  \u003ca href=\"https://pypi.org/project/pylst/\"\u003e\u003cimg src=\"https://img.shields.io/pypi/v/pylst.svg?color=brightgreen\" alt=\"PyPI version\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/pylst/\"\u003e\u003cimg src=\"https://img.shields.io/pypi/pyversions/pylst.svg\" alt=\"Python versions\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://img.shields.io/badge/license-MIT-green.svg\" alt=\"License\"\u003e\n  \u003ca href=\"https://github.com/Azad77/pylst/actions\"\u003e\u003cimg src=\"https://img.shields.io/github/actions/workflow/status/Azad77/pylst/ci.yml?branch=main\" alt=\"CI status\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://zenodo.org/doi/10.5281/zenodo.18636233\"\u003e\u003cimg src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.18636233.svg\" alt=\"DOI\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003ch1 align=\"center\"\u003epylst\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cstrong\u003eA Python package for processing and visualizing Landsat Land Surface Temperature (LST) data\u003c/strong\u003e\n  \u003cbr\u003e\n  \u003cem\u003eFrom raw Landsat thermal bands to ready-to-analyze LST maps — with minimal code.\u003c/em\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e •\n  \u003ca href=\"#quick-start\"\u003eQuick Start\u003c/a\u003e •\n  \u003ca href=\"#features\"\u003eFeatures\u003c/a\u003e •\n  \u003ca href=\"#documentation\"\u003eDocumentation\u003c/a\u003e •\n  \u003ca href=\"#citation\"\u003eCitation\u003c/a\u003e •\n  \u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\n\u003c/p\u003e\n\n## Features\n\n- Retrieve and preprocess Landsat thermal infrared (TIR) data via Google Earth Engine (using `earthengine-api` \u0026 `geemap`)\n- Compute Land Surface Temperature (LST) using standard single-channel / split-window algorithms\n- Atmospheric correction support (basic emissivity \u0026 water vapor adjustments)\n- Flexible visualization: single-date maps, time-series animations, zonal statistics\n- Export results as GeoTIFF, NetCDF or interactive maps (folium / ipyleaflet)\n- Built on trusted libraries: rasterio, numpy, pandas, matplotlib, opencv-python, scikit-learn\n\n## Installation\n\nThe easiest way is via **pip** (recommended in a virtual environment):\n\n```bash\npip install pylst","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fazad77%2Fpylst","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fazad77%2Fpylst","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fazad77%2Fpylst/lists"}