AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data (2025)
- Authors:
- USP affiliated authors: TRAINA, AGMA JUCI MACHADO - ICMC ; RODRIGUES JUNIOR, JOSÉ FERNANDO - ICMC ; SILVA, JOÃO PEDRO DA - ICMC ; AGUIAR, ERIKSON JÚLIO DE - ICMC
- Unidade: ICMC
- DOI: 10.1109/CBMS65348.2025.00180
- Subjects: APRENDIZADO COMPUTACIONAL; SENSORIAMENTO REMOTO; INDICADORES SOCIOECONÔMICOS; SAÚDE PÚBLICA; BRASIL
- Keywords: AI; Remote Sensing; Public Health; Analytics
- Agências de fomento:
- Language: Inglês
- Objetivos de Desenvolvimento Sustentável (ODS):
03. Saúde e bem-estar
10. Redução das desigualdades
11. Cidades e comunidades sustentáveis
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2025
- Source:
- Título: Proceedings
- ISSN: 2372-9198
- Conference titles: IEEE International Symposium on Computer-Based Medical Systems - CBMS
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
SILVA, João Pedro da et al. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data. 2025, Anais.. Los Alamitos: IEEE, 2025. Disponível em: https://doi.org/10.1109/CBMS65348.2025.00180. Acesso em: 13 fev. 2026. -
APA
Silva, J. P. da, Aguiar, E. J. de, Spadon, G., Traina, A. J. M., & Rodrigues Junior, J. F. (2025). AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS65348.2025.00180 -
NLM
Silva JP da, Aguiar EJ de, Spadon G, Traina AJM, Rodrigues Junior JF. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data [Internet]. Proceedings. 2025 ;[citado 2026 fev. 13 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180 -
Vancouver
Silva JP da, Aguiar EJ de, Spadon G, Traina AJM, Rodrigues Junior JF. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data [Internet]. Proceedings. 2025 ;[citado 2026 fev. 13 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180 - On the power of CNNs to detect slums in Brazil
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Informações sobre o DOI: 10.1109/CBMS65348.2025.00180 (Fonte: oaDOI API)
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