Autonomous litter surveying and human activity monitoring for governance intelligence in coastal eco-cyber-physical systems

The human impact on the coastal ecosystems is a global environmental concern. Due to the growing urbanization, industrialization, and transportation, this impact on the living and non-living components of the coastal area is expected to further increase in the coming years. Artificial intelligence based automation of the coastal monitoring, including data collection, analysis and decision making, provides real-time insights and opportunities for large-scale coastal management and governance. In this paper, a framework for autonomous litter surveying and human activity monitoring for governance intelligence in coastal eco-cyber-physical systems (ecoCystem) is presented. A large dataset of more than 20,000 images focused on smart coastal management is collected to model the real world scenarios. A combination of various artificial intelligence based methods are used for automatic detection and classification of various litter in the coastal environment. Furthermore, the proposed framework is capable of autonomous monitoring of humans activities and detection of illegal entry of vehicles and boats to the beach area. The accuracy of the proposed autonomous system is 87% for correct classification of fully visible litter and 95% for fully visible vehicles. The experimental results show that the application of computer vision and machine learning for autonomous litter classification shows promising results for increasing the speed and scale of litter surveying in the coastal area. Further training of the artificial intelligence models is necessary for increasing the accuracy of the proposed framework and real-world deployment in the coastal environment. The proposed human activity monitoring system can be used for autonomous coastal law enforcement and real-time and active protection of the coastal zones.