Bats, Spectrograms, and Deep Learning: Unmasking the Secrets of Mining Area Caves

  • Arthur Gonsales Instituto Tecnológico Vale
  • Vitor C. A. Santos Instituto Tecnológico Vale
  • Giulliana Appel Instituto Tecnológico Vale
  • Leonardo Trevelin Instituto Tecnológico Vale
  • Valeria Tavares Instituto Tecnológico Vale
  • Ronnie Alves Instituto Tecnológico Vale


Detecting and classifying bat species in mining region caves is crucial for ore extraction activities, environmental impact reduction, and worker safety. This study focuses on using bat echolocation call data collected by VALE, a mining company, to develop a deep learning-based system for species recognition. By applying transfer learning on a pre-trained MobileNetV2 model, the system achieved an impressive 95.21% accuracy in classifying spectrograms of bat echolocation calls from three different species. This outperformed other models tested. Implementing this system would enhance VALE's cave inspection processes, ensuring worker safety and bat population preservation in mining regions.

Palavras-chave: deep learning, cave bats, spectrograms, classification


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GONSALES, Arthur; SANTOS, Vitor C. A.; APPEL, Giulliana; TREVELIN, Leonardo; TAVARES, Valeria; ALVES, Ronnie. Bats, Spectrograms, and Deep Learning: Unmasking the Secrets of Mining Area Caves. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 1186-1194. ISSN 2763-9061. DOI: