Aplicação de Deep Learning para Detecção de Ocupações Irregulares em Áreas de Cerrado em Brasília-DF
Uma Abordagem de Baixo Custo com QGIS e Google Earth
DOI:
https://doi.org/10.26848/rbgf.v19.01.p171-185Keywords:
ocupações irregulares, Deep Learning, cerrado, Google Earth, MapFlowAbstract
The monitoring of irregular settlements in Cerrado areas is crucial due to the environmental and urban impacts, particularly in the Federal District (DF) region. This study aimed to evaluate the application of Deep Learning techniques for the automatic detection of buildings in Google Earth images, using the MapFlow plugin in QGIS. Images from 2020, 2022, and 2024 from an area near the Santarém camp in Samambaia-DF were analyzed, comparing the effectiveness of the "Building" and "Aerial" models. The results indicated that the "Aerial" model achieved higher sensitivity in detecting constructions compared to the "Building" model, despite a slight increase in false positive rates. Additionally, a significant expansion of settlements was observed over the analyzed years. The use of Deep Learning proved to be a promising alternative to traditional monitoring, providing greater accuracy and efficiency in pattern recognition within images with limited resolution. Therefore, the study highlights the potential of these tools to support land management and combat irregular settlements, proposing a low-cost and accessible approach for environmental monitoring.
Keywords: irregular settlements, Deep Learning, Cerrado, Google Earth, MapFlow.
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Dr. em Geografia pela Universidade de Brasília
Analista de Planejamento Urbano e Infraestrutura do Governo do Distrito Federal
Assessor da Unidade Geoprocessamento e monitoramento da Secretaria de Estado de Proteção da Ordem Urbanística do Distrito Federal
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