Welcome to the Mississippi State University Geospatial Computing for Environmental Research (GCER) Lab in the Department of Agricultural and Biological Engineering. We study land and water resources management using Earth Observation data. Our scientific work addresses many relevant Earth's process questions using deep learning models, including land cover and land use change, water quantity and quality, agricultural conservation practices, and climate change. Learn more about us at gcerlab.com.
Here you can find software, tutorials, and tools developed in our lab and that are openly available for the community.
- Couse Python Foundation for Agricultural and Biosystems Engineering: focuses on basic concepts of Python with concise explanations and examples that you need as a practitioner. Author: Vitor Martins
- geospatial_project_example: A guide on how to create a geospatial project organized as a Python package. Author: Lucas Ferreira
- AerOC Scan: a package designed to download, filter, and correct AERONET-OC (Aerosol Robotic Network-Ocean Color) data. Author: Rejane Paulino
- geocoreg: A package to simplify geospatial data co-registration supporting integration with xarray. Author: Lucas Ferreira
- pacereader: a package designed to facilitate the use of PACE (Plankton, Aerosol, Cloud, ocean Ecosystem) hyperspectral data by reading the original data in .netCDF format and converting it into a georeferenced GeoTIFF file. Authors: Rejane Paulino and Thainara Lima
- fastnanquantile: A faster alternative for numpy's nanquantile function. Author: Lucas Ferreira
- L-CONNECT: combines machine learning and spectral similarity features to predict water surface connectivity between floodplain lakes and their main river. Author: Rejane Paulino
