(untitled) (REMUNOM, 2026)
The advancement of geotechnologies has significantly expanded the possibilities for analysis and management of spatial information, with Geographic Information Systems (GIS) becoming essential tools for processing and interpreting territorial data. Among the available platforms, QGIS stands out as an open-source software widely used in academic, technical, and institutional activities related to geoprocessing. However, many workflows carried out within this environment still depend on manual and repetitive procedures, which may result in longer processing times, operational inconsistencies, and reduced efficiency when handling large volumes of spatial data. In this context, process automation emerges as a relevant strategy to optimize workflows and increase productivity in projects involving spatial analysis. The Python programming language has broad applicability in the development of scripts capable of automating tasks within QGIS, enabling the sequential execution of operations and the standardization of analytical processes. Therefore, this study aims to present the development of automated tools designed to optimize geoprocessing workflows in the QGIS environment. The research is based on the implementation of automated routines developed in Python and integrated into the software environment, enabling greater efficiency in the processing and management of geographic data. The results indicate that the automation of procedures significantly contributes to reducing task execution time while also promoting greater reproducibility of analyses and standardization of geoprocessing processes. Thus, the development of automated tools represents an effective alternative to improve spatial data management and enhance operational efficiency in activities that rely on Geographic Information Systems.