Drawing on experience from foss4g conferences, open source projects, multiple software foundations, and some great employers - I would like to share a bit of what I have learned.

This keynote will provide a little bit of amusement, some fun photos, entertaining challenges that have been overcome, and ideas for what is next.

Jody Garnett

Community & Foundation"

Luiz Motta

Use cases & applications"

Alexandre Assuncao

Academic Track"

The project, which began in 2009 using proprietary software, transitioned to open-source in 2017. To address limitations and support multiple agencies, we ported Inteligeo version 4 functionality to GeoNode, creating Inteligeo 5 in 2022. Each agency has a unique perspective: PF was involved from the start, CENSIPAM deployed for internal use, ANA for external use, and ICMBIO is integrating it into their processes.

Why customize at all? Why not just use the software as it is? Why not develop it from scratch? There are several reasons why one should (and shouldn't) customize, and then there are several ways how to do it right once you commit to it. We share our experiences: the good and the bad, and the lessons learned, when customizing GeoNode for the Brazilian government.

Why customize?

• Get shiny new functionality!
• Jump-start development
• Optimize processes with tailored deployments and workflows
• Integrate with existing systems and infrastructure (authentication, 170TB raster storage, Brasil Mais imagery)
• Comply with internal and government standards

Challenges:

• Non-standard deployment
• Training and documentation
• Balancing customization with community support
• Syncing with upstream changes
• Managing a huge codebase with extra stuff that you don't need

How to do it right (aspirational):

• Seek sponsorships (SGD/MGI, JICA, FINEP, INTERPOL)
• Selfless and selfish reasons to contribute back to the community
• Have a clear strategy for upstream syncing
• Keep it simple. Minimize customization to essential features
• Design independent, standalone components
• Engage upstream developers when possible

Our experience is particularly relevant to the Amazon region, as the tool directly supports the Federal Police surveillance and conservation efforts in the area and is being integrated by the agencies of the other speakers.

We welcome feedback and collaboration ideas from the FOSS4G community during the Q&A session!

Daniel Araújo Miranda

Transition to FOSS4G"

The conception of this project as a digital platform linked to the Brazilian Agricultural Observatory aims to publish indicators and parameters derived from well-founded technical and scientific data, capable of evaluating the effective performance of the national agricultural sector at the municipal or state level, contributing to sectoral policies and planning and management processes aimed at building sustainable agriculture and the correct positioning of the country on the international scene. Thus, the general objective is to develop an intelligent environment that automates and manages the IAS pipelines in a data storage organization environment based on the medallion architecture to be the basis of the data panel for publishing the indicators.

A data pipeline is a succession of connected phases that enable the collection, storage, modification, analysis, and representation of data, with the purpose of acquiring meaningful insights and supporting informed choices (CALANCA, 2023). A data lakehouse, the destination of the project pipelines, is “like a modern data platform built from a combination of a data lake and a data warehouse” (ORACLE CLOUD INFRASTRUCTURE, 2023), using “the flexible storage of unstructured data from a data lake and the management capabilities and tools of data warehouses, and then strategically deploying them together as a larger system” (ORACLE CLOUD INFRASTRUCTURE, 2023). The medallion architecture is the sequential structuring of data storage that aims to logically organize the data in the lakehouse, aiming to incrementally and progressively improve the structure and quality of the data as it flows through the three layers of the architecture (ARQUITETURA medallion, 2024). The terms bronze (raw data from the source), silver (transformation and validation of the data), and gold (refined and enriched data for use in projects) describe the quality of the data during the process (SKAYA et al, 2024) . Pipeline management is performed by Apache Airflow (version 2.44), an open-source platform for developing, scheduling, and monitoring batch-oriented workflows based on the Python programming language, which allows you to create workflows connected to virtually any technology (WHAT is Airflow™?, 2023). The Airflow execution environment was structured in Docker, an open-source platform that allows you to create and manage containers as modular virtual machines that contain the essentials for their execution. The developed image is available on GitHub.

Carlos Eduardo Mota

Use cases & applications"

Fabrício Lisboa Vieira Machado

Academic Track"

GPSSample is a free Android 8+ application available in six languages: English, French, Spanish, Portuguese, Russian, and Bahasa. It is designed for field practitioners with limited mobile networks or Wi-Fi. The app was developed using the Kotlin open-source language and it uses the open-source SQLite database. User guides, GPSSample Decoder application decrypting data, demonstration videos, and Quarto analyses are available through the GPSSample GitHub site.
Use Cases and Road Map: Development lessons learned will be presented from two public health GPSSample application pilots in India and Kenya. We aim to engage the FOSS4G development community to enhance GPSSample’s geospatial functionalities and learn best practices on maintaining and updating open-source code. GPSSample currently uses Mapbox. Ideally in a future version, users will also be able to select OpenStreetMap for the base map and the app will include navigation using offline turn-by-turn instructions.

Amber Dismer, Joel Adegoke

Academic Track"

Preventing deforestation requires understanding its root causes, particularly the capital availability to the farm sector. In many tropical countries, rural credit is available as loans at subsidized interest rates to improve agricultural production or support agricultural costs (Servo, 2019). However, these loans may be leading to more deforestation. Some studies have analyzed this issue on a municipal scale, but few peer-reviewed studies have linked rural credit to individual property-scale deforestation. Recently, the NGO Greenpeace (Greenpeace, 2024) and the Climate Policy Initiative (Mourão et al., 2024) published two studies showing the relationship between rural credit and deforestation. Understanding this relationship can improve public policies to prevent deforestation from happening even before it starts.

Methods
In this study, I used open data and FOSS4G to quantify the amount of Rural Credit released to rural properties that committed Deforestation. The datasets came from different open data sources. The Central Bank of Brazil provides data on rural credit on the SICOR System. The National Institute of Space Research (INPE) provides data on deforestation in the Terrabrasilis system. The Brazilian Forest Service provided data for each property's Rural Environmental Registry (CAR), providing their boundaries. The Brazilian Institute of Geography and Statistics (IBGE) provides data for administrative boundaries (state and municipality).

Using the Terra library in CRAN-R,. I processed the data sets from three states that contributed the most to deforestation: Rondônia, Mato Grosso, and Pará. I used a Spatialite database and QGIS Geographic Information System to check the results. The novelty here is that by using R scripts, it was possible to rebuild the relational database from SICOR in a geospatial environment, providing a reproducible environment. All steps are described below.

First, using R, all the data needed for the analysis was downloaded from their source and loaded into the R environment. The second step, still using R, was to recreate the SICOR, CAR, and PRODES Deforestation tables and populate them into a Spatialite (SQLite) database. This step provides a valuable tool for monitoring by both environmental agencies and the banks that provide loans for rural credit.

The next step was to intersect the deforestation data with the CAR property boundaries, calculating the amount of deforestation on each property using PRODES data between 2008 and 2023. Next, the total number of loans between 2013 and 2023 was identified for each property. All these steps were processed using the Terra library in R.

Results
In 1992, the Brazilian Parliament enacted Law 4,829, creating subsidies for rural credit, known as Safra Plan. The interest rates of the Safra Plan have always been significantly lower than those practiced in the market. In March 2019, for example, while the average interest rate on loans for non-rural purposes stood at 31.6% per year, rural credit was observed at 10.8% p.y. on market rates, and even lower with controlled rates observing an average rate of 6.1% p.y.(Servo, 2019) .

The results show that from 2013 to 2023, more than BRL 17 billion was loaned to properties with some deforestation in these three states (RO, PA, MT). Counting deforestation from August 2008 to July 2023, 8197 km² is the total amount of clearing in properties that received rural credit in those same three states, representing 8.5% of all deforestation for the period.

George Porto Ferreira

Academic Track"

Traditionally, many organizations have relied on no-code geospatial software with click-based interfaces. Due to their accessibility and user-friendly interface, these tools allow team members to visualize and manipulate geospatial data without the need for programming knowledge. However, as data starts to become more complex, these tools often present scalability limitations, restricting the full potential of geospatial data applications.

This paper explores the benefits of transitioning to a hybrid approach by integrating Python and open-source geospatial libraries into the data processing phase of geospatial analysis. By presenting the possible advantages gained and providing a hands-on example of Python use in geospatial data, the aim of this paper is to demonstrate how Python can play a pivotal role in overcoming the limitations of no-code solutions.

Python can enhance the data extraction phase, enabling integration with various data sources and APIs and connecting to external databases and web services. This capability supports consistent data exchange and real-time data integration. This phase can also be automated, summarizing all steps into a script that can be applied to every new dataset.

The processed data can be visualized using various libraries in a Python environment or used as input for traditional geospatial software. The hybrid approach leverages the user-friendly visualization tools of no-code software while enabling more sophisticated data processing capabilities.

The output data can also be used as input for developing custom algorithms, including the integration of machine learning models and artificial intelligence. This step enables a wide range of applications, such as urban feature prediction, classification or segmentation of remote sensing data, and clustering of spatial data.

Adopting a hybrid approach significantly enhances an organization's analytical capabilities. These advanced analyses provide deeper insights into spatial patterns and trends that manual methods alone may not reveal.

The hands-on example, based on census data from the Brazilian Institute of Geography and Statistics (IBGE), demonstrates geospatial data processing with Python and the GeoPandas library, both open-source solutions. This example will include the use of Python in geospatial data processing through the following steps: data extraction, data processing, and customized algorithm application.

In conclusion, integrating Python into these workflows enhances flexibility and analytical capabilities, allowing organizations to innovate in their solutions and create new opportunities for products and services. Coding elevates data-driven decision-making and enables more sophisticated and scalable analyses, particularly when dealing with large and complex datasets. This case study serves as an inspiring example for organizations and researchers aiming to maximize the potential of their geospatial data, highlighting the significant benefits of combining traditional geospatial software with powerful open-source tools.

This work received financial support from the State of São Paulo Research Foundation (FAPESP) (grant 2023/15663-7, 2024/05553-2, 2024/05727-0, 2024/05481-1)."

Kauê de Moraes Vestena

Academic Track"