Enhancing Lae-Lae Island sustainability: computer vision based waste detection and analysis - Nasir, Arnold; Syariati, Kasmir; Suardi, Citra; Sundoro, David; Lordianto, Reinaldo Lewis (Bulletin of Social Informatics Theory and Application, 2024)
In our study, "Enhancing Lae-Lae Island Sustainability: Computer Vision-Based Waste Detection and Analysis," we investigate novel approaches to address plastic pollution challenges in coastal ecosystems, focusing on Lae-Lae Island. Through a multidisciplinary approach, we uncover valuable insights for effective waste management and environmental conservation. Spatial analysis identifies concentrated plastic pollution hotspots, offering actionable data for targeted cleanup strategies. Temporal trend analysis reveals waste accumulation patterns, facilitating adaptive waste management decisions. Furthermore, we examine the impact of environmental factors on waste density, aiding in proactive pollution mitigation. Central to our research is the evaluation of computer vision technology, which demonstrates high precision, recall, and an F1-score of approximately 87.8%. These results signify the technology's potential to revolutionize waste detection and monitoring, enabling efficient resource allocation, real-time surveillance, and rapid pollution response. In conclusion, our study provides a data-driven framework for sustainable plastic waste management on Lae-Lae Island, offering insights applicable to coastal regions worldwide. By embracing technology and innovation, we pave the way for cleaner, more resilient coastal ecosystems, underscoring the importance of proactive environmental stewardship.