Detalhes bibliográficos
| Ano de defesa: |
2024 |
| Autor(a) principal: |
Eloiza Marques |
| Orientador(a): |
Jose Marcato Junior |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Fundação Universidade Federal de Mato Grosso do Sul
|
| Programa de Pós-Graduação: |
Não Informado pela instituição
|
| Departamento: |
Não Informado pela instituição
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Link de acesso: |
https://repositorio.ufms.br/handle/123456789/11079
|
Resumo: |
The management of dam safety is crucial for disaster prevention and the preservation of water resources. In the state of Mato Grosso do Sul, the Institute of Environment of Mato Grosso do Sul (Imasul) plays a key role in overseeing these structures, following the guidelines of the National Policy on Dam Safety (PNSB) and utilizing the National Information System on Dam Safety (SNISB) for the monitoring and classification of dams. This study evaluates Imasul's performance within the SNISB context and analyzes the effectiveness of image segmentation models, such as Segformer and DeepLabV3+, for mapping dam reservoirs. The research compares the results of both models, highlighting the superiority of the Segformer, which showed better performance metrics, such as precision, sensitivity, and accuracy. The findings reveal the potential of artificial intelligence and remote sensing in environmental monitoring, providing more accurate tools for dam management and safety |
