Shazam for bats: Internet of Things for continuous real‐time biodiversity monitoring. Issue 3 (3rd October 2021)

• Record Type: Journal Article
• Authors: Gallacher, Sarah; Wilson, Duncan; Fairbrass, Alison; Turmukhambetov, Daniyar; Firman, Michael; Kreitmayer, Stefan; Mac Aodha, Oisin; Brostow, Gabriel; Jones, Kate
• Other Names: Hancke Gerhard P. guestEditor.; Reza Salehizadeh Mohammad guestEditor.; Liu Xuan guestEditor.; Hu Jie guestEditor.; Abu‐Mahfouz Adnan M. guestEditor.; Thomos Nikolaos guestEditor.; Ishihara Susumu guestEditor.; Savaglio Claudio guestEditor.
• Is Part Of: IET smart cities. Volume 3:Issue 3(2021)
• Page Start: 171
• Page End: 183
• Publication Date: 2021-10-03
• Subjects: Smart cities -- Periodicals; City planning -- Technological innovations -- Periodicals; Cities and towns -- Growth -- Periodicals; Sustainable urban development -- Periodicals; Sustainable development; City planning -- Technological innovations; Cities and towns -- Growth; Periodicals; 307.76
• DOI: 10.1049/smc2.12016
• Languages: English
• ISSNs: 2631-7680

Abstract

Abstract: Biodiversity surveys are often required for development projects in cities that could affect protected species such as bats. Bats are important biodiversity indicators of the wider health of the environment and activity surveys of bat species are used to report on the performance of mitigation actions. Typically, sensors are used in the field to listen to the ultrasonic echolocation calls of bats or the audio data is recorded for post‐processing to calculate the activity levels. Current methods rely on significant human input and therefore present an opportunity for continuous monitoring and in situ machine learning detection of bat calls in the field. Here, we show the results from a longitudinal study of 15 novel Internet connected bat sensors—Echo Boxes—in a large urban park. The study provided empirical evidence of how edge processing can reduce network traffic and storage demands by several orders of magnitude, making it possible to run continuous monitoring activities for many months including periods which traditionally would not be monitored. Our results demonstrate how the combination of artificial intelligence techniques and low‐cost sensor networks can be used to create novel insights for ecologists and conservation decision‐makers.