FOSS4G 2024 Academic Track

With the increasing presence of spatialized data and information
in people's daily lives, the constant need to make data-driven
decisions, and the expansion of artificial intelligence
technologies in society, this work seeks a technological solution
focused on simplifying geospatial analyses. The goal is to
democratize access to and understanding of these resources for
common users without the need for advanced knowledge of the
specific geographic information tools currently most used.
To this end, a system was developed that transforms natural
language questions directly into SQL queries, specifically using
PostgreSQL/PostGIS (Li and Jagadish, 2014; Ramsey, 2007).
This system is based on a chat model built on Gemini, which
interprets user queries and generates the corresponding SQL
queries. The back-end API executes these queries and returns the
results, which are visualized in an intuitive and interactive
graphical interface. This allows for dynamic exploration of
geospatial data, facilitating the analysis and visualization of
complex information without the need for advanced technical
knowledge in SQL. The integration of natural language
processing (NLP) and geospatial database queries represents a
significant innovation. This system reduces the learning curve
associated with traditional GIS tools, making the technology
accessible to a broader audience (Craglia et al., 2012). By using
the Gemini model, the system can understand and process a wide
range of natural language inputs, translating them into precise
SQL queries that interact with the geospatial database.
To demonstrate the system's effectiveness, a case study was
conducted using a database composed of 20 tables containing
data released by the National Water Agency (ANA), the Brazilian
Institute of Geography and Statistics (IBGE), and the Energy
Research Company (EPE), which were adjusted for reading by
the system. This database includes a data dictionary that provides
detailed information on what each value represents and its
corresponding context.
The results were evaluated based on the accuracy of answers
given to 192 questions posed within the context of the case study.
Out of these 192 questions, 167 answers were correct, yielding
an accuracy rate of 87% in the total evaluated, allowing detailed
visualization of the geometries and information required by the
user's query in the developed interface. Enhanced accessibility
for non-technical users is one of the most significant benefits
identified in this work, as there is no need for in-depth technical
knowledge in spatial data filters, in addition to the reduced query
time and the ability to generate valuable insights from these data
sets.
This work also highlights the importance of an intuitive and
interactive graphical interface system. The interface allows the
visualization of layers and tables resulting from the query,
enabling users to dynamically explore, filter, and manipulate the
data according to their needs, and obtain insights that would be
difficult to achieve without advanced knowledge of GIS tools (Li
and Wang, 2013).
It is important to note that this work represents a first step in an
initiative for this technological solution model implementing
Artificial Intelligence, from which it was possible to identify
several points of improvement not only in the model but also in
the databases and their construction and acquisition process. The
case study demonstrated that the system can accurately interpret
and execute user queries, providing reliable and relevant results.
With this approach, new possibilities are opened for the
exploration and analysis of geospatial data, enhancing decisionmaking based on information obtained from various areas such
as environmental monitoring, urban planning, and territorial
planning. Future work should focus on improving the system's
capabilities, expanding its application domains, and exploring
new ways to integrate emerging technologies, continuing to drive
innovation in this critical area.