A road mobile mapping device for supervised classification of amphibians on roads

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife-Vehicle Collisions: A Review, Challenges, and New Perspectives

Worldwide, the persistent trend of human and animal life losses, as well as damage to properties due to wildlife-vehicle collisions (WVCs) remains a significant source of concerns for a broad range of stakeholders. To mitigate their occurrences and impact, many approaches are being adopted, with varying successes. Because of their increased versatility and increasing efficiency, Artificial Intelligence-based methods have been experiencing a significant level of adoption. The present work extensively reviews the literature on intelligent systems incorporating sensor technologies and/or machine learning methods to mitigate WVCs. Included in our review is an investigation of key factors contributing to human-wildlife conflicts, as well as a discussion of dominant state-of-the-art datasets used in the mitigation of WVCs. Our study combines a systematic review with bibliometric analysis. We find that most animal detection systems (excluding autonomous vehicles) are relying neither on state-of-the-art datasets nor on recent breakthrough machine learning approaches. We, therefore, argue that the use of the latest datasets and machine learning techniques will minimize false detection and improve model performance. In addition, the present work covers a comprehensive list of associated challenges ranging from failure to detect hotspot areas to limitations in training datasets. Future research directions identified include the design and development of algorithms for real-time animal detection systems. The latter provides a rationale for the applicability of our proposed solutions, for which we designed a continuous product development lifecycle to determine their feasibility.

An Improved Mobile Mapping System to Detect Road-Killed Amphibians and Small Birds

Roads represent a major source of mortality for many species. To mitigate road mortality, it is essential to know where collisions with vehicles are happening and which species and populations are most affected. For this, moving platforms such as mobile mapping systems (MMS) can be used to automatically detect road-killed animals on the road surface. We recently developed an MMS to detect road-killed amphibians, composed of a scanning system on a trailer. We present here a smaller and improved version of this system (MMS2) for detecting road-killed amphibians and small birds. It is composed of a stereo multi-spectral and high definition camera (ZED), a high-power processing laptop, a global positioning system (GPS) device, a support device, and a lighter charger. The MMS2 can be easily attached to any vehicle and the surveys can be performed by any person with or without sampling skills. To evaluate the system’s effectiveness, we performed several controlled and real surveys in the Évora district (Portugal). In real surveys, the system detected approximately 78% of the amphibians and birds present on surveyed roads (overlooking 22%) and generated approximately 17% of false positives. Our system can improve the implementation of conservation measures, saving time for researchers and transportation planning professionals.