More (Enough) Is Better: Towards Few-Shot Illegal Landfill Waste Segmentation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Molina, M (Autor) Outra; Veloso, B (Autor) FEP; Ferreira, CA (Autor) Outra; Rita Ribeiro (Autor) FCUP; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024)

Páginas: 4547-4554

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-5X7

DOI: 10.3233/faia241047

Abstract (EN): <jats:p>Image segmentation for detecting illegal landfill waste in aerial images is essential for environmental crime monitoring. Despite advancements in segmentation models, the primary challenge in this domain is the lack of annotated data due to the unknown locations of illegal waste disposals. This work mainly focuses on evaluating segmentation models for identifying individual illegal landfill waste segments using limited annotations. This research seeks to lay the groundwork for a comprehensive model evaluation to contribute to environmental crime monitoring and sustainability efforts by proposing to harness the combination of agnostic segmentation and supervised classification approaches. We mainly explore different metrics and combinations to better understand how to measure the quality of this applied segmentation problem.</jats:p>

Idioma: Inglês

Tipo (Avaliação Docente): Científica