TreeSpirit: Illegal logging detection and alerting system using audio identification over an IoT network

Kalhara, P. G.; Jayasinghearachchd, V. D.; Dias, Alair; Ratnayake, V. C.; Jayawardena, Chandimal; Kuruwitaarachchi, N.

doi:10.1109/skima.2017.8294127

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…In [30], the proposed system detects and locates chainsaws in the forest. It envisages a three-tier architecture based on the Time Difference Of Arrival (TDOA) and multilateralism.…”

Section: Audio Classificationmentioning

confidence: 99%

Monitoring Illegal Tree Cutting through Ultra-Low-Power Smart IoT Devices

Andreadis

Giambene

Zambon

2021

Sensors

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Forests play a fundamental role in preserving the environment and fighting global warming. Unfortunately, they are continuously reduced by human interventions such as deforestation, fires, etc. This paper proposes and evaluates a framework for automatically detecting illegal tree-cutting activity in forests through audio event classification. We envisage ultra-low-power tiny devices, embedding edge-computing microcontrollers and long-range wireless communication to cover vast areas in the forest. To reduce the energy footprint and resource consumption for effective and pervasive detection of illegal tree cutting, an efficient and accurate audio classification solution based on convolutional neural networks is proposed, designed specifically for resource-constrained wireless edge devices. With respect to previous works, the proposed system allows for recognizing a wider range of threats related to deforestation through a distributed and pervasive edge-computing technique. Different pre-processing techniques have been evaluated, focusing on a trade-off between classification accuracy with respect to computational resources, memory, and energy footprint. Furthermore, experimental long-range communication tests have been conducted in real environments. Data obtained from the experimental results show that the proposed solution can detect and notify tree-cutting events for efficient and cost-effective forest monitoring through smart IoT, with an accuracy of 85%.

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“…In [30], the proposed system detects and locates chainsaws in the forest. It envisages a three-tier architecture based on the Time Difference Of Arrival (TDOA) and multilateralism.…”

Section: Audio Classificationmentioning

confidence: 99%

Monitoring Illegal Tree Cutting through Ultra-Low-Power Smart IoT Devices

Andreadis

Giambene

Zambon

2021

Sensors

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“…In [17], the authors proposed a three-tier architecture that could be used for monitoring a forest. The architecture was aimed at continuously monitoring a forest area to recognize illegal logging by using chainsaw noise identification methods on wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Illegal Logging Detection Based on Acoustic Surveillance of Forest

et al. 2020

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In this article, we present a framework for automatic detection of logging activity in forests using audio recordings. The framework was evaluated in terms of logging detection classification performance and various widely used classification methods and algorithms were tested. Experimental setups, using different ratios of sound-to-noise values, were followed and the best classification accuracy was reported by the support vector machine algorithm. In addition, a postprocessing scheme on decision level was applied that provided an improvement in the performance of more than 1%, mainly in cases of low ratios of sound-to-noise. Finally, we evaluated a late-stage fusion method, combining the postprocessed recognition results of the three top-performing classifiers, and the experimental results showed a further improvement of approximately 2%, in terms of absolute improvement, with logging sound recognition accuracy reaching 94.42% when the ratio of sound-to-noise was equal to 20 dB.

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“…Similarly, systems that use a combination of UAVs, thermal cameras and deep learning have demonstrated the possibility of remotely patrolling large wildlife reserves by detecting poachers and wildlife in near real-time [21]. Likewise, acoustic systems that use deep neural networks can be used to autonomously identify anthropogenic sounds to detect activities like illegal logging and immediately alert protected-area managers [22]. Such systems can be important in scaling up environmental conservation initiatives.…”

Section: Magazinementioning

confidence: 99%