RS-Visao-Real
Real-time satellite imagery for Rio Grande do Sul — built during the 2024 floods.
git clone https://github.com/fernandofatech/RS-Visao-Real.gitRS Visão Real is an open-source Python tool that collects and surfaces real-time satellite imagery of Rio Grande do Sul, built during the 2024 floods to support situational awareness and emergency response.
Why this project exists
In April and May of 2024, Rio Grande do Sul faced one of the worst climate disasters in Brazilian history. Entire cities were submerged, and the lack of accessible geospatial data hampered rescue coordination and communication with affected populations.
I built RS Visão Real as a direct response to that gap: a project that consumes open satellite imagery sources, processes the data with Python, and makes it available in a structured form for people who need to make decisions in the field or coordinate operations remotely. The focus is not technical sophistication for its own sake — it is real utility in a crisis context.
The project is part of the public portfolio ecosystem I maintain under the fernando.moretes.com domain, where I document solutions applied to concrete problems. It also serves as a reference for how I structure data-oriented Python projects with clear modular separation, even under tight development timelines.
What this project delivers
How the project works
The core of the project is pure Python, organised into modules with separated responsibilities. The general flow follows three stages: acquisition, processing, and exposure of data.
In the acquisition stage, the system queries open satellite imagery sources — typically publicly available remote sensing products such as those from programmes like Copernicus/Sentinel or equivalents — and downloads the relevant geographic cutouts for RS. In the processing stage, GIS libraries from the Python ecosystem (such as rasterio, shapely, or similar, as declared in the repository's requirements.txt) are used to clip, reproject, and analyse the imagery. In the exposure stage, the processed data is made available in a consumable form — whether as local files, visualisations, or endpoints, depending on how the project has evolved.
The modular structure means each of these responsibilities lives in its own space in the codebase, making it straightforward to swap one data source for another or add a new output type without rewriting the entire pipeline. For a project born in response to an emergency, that separation was a deliberate decision: I needed something that could grow without becoming a fragile monolith.
Data flow — from satellite to analysis
The pipeline spans three zones: external open sources, local Python processing, and output for consumption.
- Sentinel / Copernicus · or equivalent
- Other Open · Satellite APIs
- Acquisition · Module
- GIS Processing · (rasterio / shapely)
- Processed · Imagery Store
- Visualisation / · Export
- Emergency · Analyst / Operator
Installation and usage
- 1
Clone the repository
Clone the repo and enter the project directory. You will need Python 3.8+ installed.
- 2
Create and activate a virtual environment
Isolate project dependencies with a virtualenv before installing any packages.
- 3
Install dependencies
The
requirements.txtfile declares all required libraries, including the GIS packages from the Python ecosystem. - 4
Explore the modular structure
Browse the source tree to understand how the acquisition, processing, and output modules are organised. The README describes the project as following a production-oriented layout.
- 5
Run the pipeline
Refer to the project's entry-point modules to run acquisition and processing. Contributions and issues are welcome if the run documentation needs expanding.
# 1. Clone
git clone https://github.com/fernandofatech/RS-Visao-Real.git
cd RS-Visao-Real
# 2. Create and activate virtualenv
python3 -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
# 3. Install dependencies
pip install --upgrade pip
pip install -r requirements.txt
# 4. Verify environment
python --version
pip list | grep -E 'rasterio|shapely|gdal|numpy'
# 5. Explore entry points (adjust to actual module names in the repo)
# python -m rs_visao_real.acquire
# python -m rs_visao_real.processOrigin context
This project was built during a real emergency — the historic 2024 floods in Rio Grande do Sul. That means some design decisions prioritised delivery speed over documentation completeness. The roadmap explicitly includes improvements to test coverage and documentation. If you are evaluating the project as an engineering reference, take that context into account: the value is in the structure and intent, not only in the current state of the code.
Frequently asked questions
Which satellite imagery sources are supported?
The README mentions open real-time satellite imagery but does not explicitly name specific sources. Given the context (RS, 2024, GIS, Python), the most likely sources are ESA Copernicus/Sentinel products, which are free and widely used in disaster response. Check the source code and requirements.txt to confirm the exact integrations.
Can I use this project for regions other than RS?
The modular structure was designed to be adaptable. The geographic focus on Rio Grande do Sul is likely parametrised in the acquisition modules (bounding box, IBGE code, or similar). Adapting to another region should be a matter of adjusting those parameters, not rewriting the pipeline.
Is the project still active?
Yes — the status badge in the README reads active. The roadmap includes continuous improvements, expanded test coverage, and documentation enhancements, suggesting ongoing development beyond the immediate emergency response.
What is the relationship with the `fernando.moretes.com` domain?
The project is part of a public portfolio ecosystem I maintain under that domain. 'Moretes' is a portfolio context — not a separate company — that groups applied projects demonstrating solution architecture, automation, and pragmatic engineering.
Who this project is for
RS Visão Real is relevant to three distinct profiles. Emergency response teams and disaster researchers who need a working Python foundation for consuming and processing open satellite imagery in crisis contexts — especially in Brazil. Software engineers and architects who want to see how I structure a data-oriented Python project under real time pressure: the modularisation decisions, virtualenv usage, separation of responsibilities. Open-source contributors interested in contributing to a project with direct social impact — the roadmap is open and issues are welcome. What this project is not: it is not a production-ready SaaS platform with an SLA, it does not have a documented graphical interface, and test coverage is still expanding as the roadmap itself acknowledges. If you need a managed geospatial analysis solution, look at platforms like Google Earth Engine or AWS SageMaker Geospatial. If you want to understand how to build the pipeline yourself — or contribute to something that was built to solve a real problem — this repository is worth reading.