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PedroLuizskt / repository
Automated geospatial intelligence pipelines for Brazilian hydrology. A Python toolkit using Google Earth Engine to generate reports on water dynamics (JRC), topography (MERIT), and micro-basins.
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Automated geospatial intelligence pipelines for hydrological, topographic, and surface water dynamics analysis of Brazilian municipalities.
This repository contains a suite of high-performance Python pipelines designed to democratize access to complex geospatial data. Leveraging the Google Earth Engine (GEE) cloud computing architecture, these scripts generate production-grade technical reports, statistical insights, and high-resolution maps for any municipality in Brazil.
The project is engineered with a focus on Object-Oriented Programming (OOP), ensuring that the code is modular, scalable, and easily reproducible by students, environmental analysts, and data engineers.
To transform raw, petabyte-scale satellite data into actionable local insights, allowing researchers to visualize:
The repository is divided into three distinct analytical modules:
01_hydro_atlas_analysis.ipynb)02_jrc_surface_water.ipynb)03_merit_topography.ipynb)The project follows a standard data science directory structure to ensure reproducibility.
brazil-hydro-dynamics/
│
├── inputs/ # Input vector data (Must be provided by the user)
│ ├── BR_Municipios_2024/ # IBGE Municipal boundaries shapefiles
│ └── BR_UF_2024/ # IBGE State boundaries shapefiles
│
├── notebooks/ # Jupyter Notebooks (The core pipelines)
│ ├── 01_hydro_atlas_analysis.ipynb
│ ├── 02_jrc_surface_water.ipynb
│ └── 03_merit_topography.ipynb
│
├── output/ # Automatically generated PDFs and TIFs
│ └── (Generated reports will appear here)
│
├── requirements.txt # Python dependencies
├── .gitignore # Git configuration
└── README.md # Project documentation
git clone https://github.com/PedroLuizskt/brazil-hydro-dynamics.git
cd brazil-hydro-dynamics
It is recommended to use a virtual environment.
pip install -r requirements.txt
Key libraries: geemap, earthengine-api, geopandas, rasterio, matplotlib, seaborn.
To run the scripts for Brazilian municipalities, you need the official shapefiles from IBGE (2024).
inputs/ folder exactly as shown in the Directory Structure above.On your first run, you will need to authenticate:
python -c "import ee; ee.Authenticate()"
The scripts are designed to be run interactively via Jupyter Notebooks or VS Code.
notebooks/ folder.NOME_MUNICIPIO and SIGLA_UF to your target location.Example Configuration:
@dataclass
class HydroConfig:
# ... paths ...
NOME_MUNICIPIO: str = 'Manaus' # Change this
SIGLA_UF: str = 'AM' # Change this
# ... parameters ...
output/ folder for the PDF report and GeoTIFF files.This project relies on scientifically validated global datasets:
| Dataset | Provider | Resolution | Application |
|---|---|---|---|
| HydroATLAS | WWF / McGill University | Level 10/12 | Catchment characteristics and discharge modeling. |
| Global Surface Water | EC Joint Research Centre | 30m | Analyzing surface water extent and change over time. |
| MERIT Hydro | University of Tokyo | ~90m | High-accuracy global hydrography maps (DEM + Flow Dir). |
This is an open educational project. Contributions are welcome! If you want to add new indices (e.g., NDVI, EVI) or improve the architecture:
git checkout -b feature/NewIndex).Distributed under the MIT License. See LICENSE for more information.
Developed with 🌎 & 🐍 for the Open Science Community.