In our rapidly advancing world, the environmental challenges we face are becoming more complex, with illegal waste disposal emerging as a major threat. The improper disposal of waste not only poses risks to human health but also wreaks havoc on our ecosystems . Fortunately, advanced technologies have paved the way for innovative solutions, particularly in the realm of illegal waste disposal detection. In this regard, and by utilizing the most recent developments in Artificial Intelligence (AI) in the fields of computer vision and multimodal analytics, the PERIVALLON project, is aiming to deliver an enhanced and comprehensive intelligence picture of organized environmental crime. Furthermore, it will develop effective and efficient tools and solutions for detecting and preventing such types of criminal activities and for assessing their environmental impact. These tools and solutions will be based on geospatial intelligence, remote sensing, scanning, online monitoring, analysis, correlation, risk assessment, and predictive analytics technologies.
In this article, we delve into the significance of detecting and combating illegal waste disposal for a sustainable and healthier future and how the PERIVALLON outputs related to this topic will be validated into the project’s Pilot Use Case 1.
The Scope of the Problem:
Illegal waste disposal is a global issue that transcends geographical boundaries. Improper disposal practices lead to the contamination of soil, water, and air, causing irreversible damage to ecosystems. From hazardous materials seeping into groundwater to plastic pollution in our oceans, the consequences are dire . To address this menace effectively, we need robust systems for detecting and preventing illegal waste disposal activities.
The PERIVALLON solution:
Having identified all these havoc practices the PERIVALLON project has created a scenario utilizing the acquisition of high-resolution satellite images and aerial photographs which will then be imported into the geospatial intelligence platform. Then, the platform will automatically find possible locations for disposing of waste, producing a prioritized list of those locations. Criminal networks are profiting substantially from illicit waste disposal operations by taking advantage of the high costs connected with legal waste treatment. The criteria for the sites’ prioritisation will be determined and suggested by the platform, using also auxiliary information. Since the sites will be constantly monitored the illegal landfills detection as well as the classification of the environmental risks, can benefit from a multi-temporal analysis of a collection of images acquired at different times. But this is only the beginning! The platform will allow geospatial analysts to identify suspicious waste disposal sites which then can be further examined extracted from UAV and satellite imagery. All the aforementioned tools and practices will result in the creation of a 3D model of the scanned region of interest providing further insight of the criminal site and further information such as volume estimation, surface measurements and possibly recognition of specific targets.
Illegal waste disposal detection is an indispensable component of our fight against environmental degradation. As stewards of our planet, it is our collective responsibility to leverage these tools and work towards a future where illegal waste disposal is no longer a threat to our environment and well-being. If you are interested into the PERIVALLON project, stay tuned with our social media and discover more on the project’s website!
[1.] Baldé, C.P., Forti V., Gray, V., Kuehr, R. and Stegmann,P. (2017). The Global E-waste Monitor – 2017, United Nations University (UNU), International Telecommunication Union (ITU) & International Solid Waste Association (ISWA).
[2.] Kaza, S., Yao, L., Perinaz, B.T. and van Woerden, F. (2018). What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. World Bank Group, Urban Development Series. Available at: https://openknowledge.worldbank.org/server/api/core/bitstreams/92a50475-3878-5984-829e-0a09a6a9badc/content