2024
Autores
Davari, N; Veloso, B; Ribeiro, RP; da Gama, JMP;
Publicação
Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, SAC 2024, Avila, Spain, April 8-12, 2024
Abstract
2024
Autores
Alcoforado, A; Okamura, LH; Fama, IC; Dias Bueno, BF; Lavado, AM; Ferraz, TP; Veloso, B; Reali Costa, AH;
Publicação
Proceedings of the 16th International Conference on Computational Processing of Portuguese, PROPOR 2024, Santiago de Compostela, Galicia/Spain, 12-15 March, 2024
Abstract
2024
Autores
Molina, M; Ribeiro, RP; Veloso, B; Gama, J;
Publicação
Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24-26, 2024, Proceedings, Part I
Abstract
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise, the challenge lies in training models with high-resolution literature datasets and adapting them to open-access low-resolution images. Considering the substantial quality differences and limited annotation, this research explores the adaptability of models across these domains. Motivated by the necessity for a comprehensive evaluation of waste detection algorithms, it advocates cross-domain classification and super-resolution enhancement to analyze the impact of different image resolutions on waste classification as an evaluation to combat the proliferation of illegal landfills. We observed performance improvements by enhancing image quality but noted an influence on model sensitivity, necessitating careful threshold fine-tuning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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