Environmental crime currently represents the third largest criminal activity worldwide while threatening ecosystems as well as human health. Among the crimes related to this activity, improper waste management can nowadays be countered more easily thanks to the increasing availability and decreasing cost of Very-High-Resolution Remote Sensing images, which enable semi-automatic territory scanning in search of illegal landfills. This paper proposes a pipeline, developed in collaboration with professionals from a local environmental agency, for detecting candidate illegal dumping sites leveraging a classifier of Remote Sensing images. To identify the best configuration for such classifier, an extensive set of experiments was conducted and the impact of diverse image characteristics and training settings was thoroughly analyzed. The local environmental agency was then involved in an experimental exercise where outputs from the developed classifier were integrated in the experts' everyday work, resulting in time savings with respect to manual photo-interpretation. The classifier was eventually run with valuable results on a location outside of the training area, highlighting potential for cross-border applicability of the proposed pipeline.

The detection and characterization of illegal solid waste disposal sites are essential for environmental protection, particularly for mitigating pollution and health hazards. Improperly managed landfills contaminate soil and groundwater via rainwater infiltration, posing threats to both animals and humans. Traditional landfill identification approaches, such as on-site inspections, are time-consuming and expensive. Remote sensing is a cost-effective solution for the identification and monitoring of solid waste disposal sites that enables broad coverage and repeated acquisitions over time. Earth Observation (EO) satellites, equipped with an array of sensors and imaging capabilities, have been providing high-resolution data for several decades. Researchers proposed specialized techniques that leverage remote sensing imagery to perform a range of tasks such as waste site detection, dumping site monitoring, and assessment of suitable locations for new landfills. This review aims to provide a detailed illustration of the most relevant proposals for the detection and monitoring of solid waste sites by describing and comparing the approaches, the implemented techniques, and the employed data. Furthermore, since the data sources are of the utmost importance for developing an effective solid waste detection model, a comprehensive overview of the satellites and publicly available data sets is presented. Finally, this paper identifies the open issues in the state-of-the-art and discusses the relevant research directions for reducing the costs and improving the effectiveness of novel solid waste detection methods.

A new study warned that artificial intelligence technology could cause a significant surge in electricity consumption. The paper, published in the journal Joule, details the potential future energy output of AI systems, noting that generative AI technology relies on powerful servers and that increased use could drive a spike in demand for energy. The authors point to tech giant Google in one such example, noting that AI only accounted for 10-15% of the company's total electricity consumption in 2021. But as AI technology continues to expand, Google's energy consumption could start to be on the scale of a small country. "The worst-case scenario suggests Google's AI alone could consume as much electricity as a country such as Ireland (29.3 TWh per year), which is a significant increase compared to its historical AI-related energy consumption," the authors wrote.

Leachates from garbage dumps can significantly compromise their surrounding area. Even if the distance between these and the populated areas could be considerable, the risk of affecting the aquifers for public use is imminent in most cases. For this reason, the delimitation and monitoring of the leachate plume are of significant importance. Geoelectric data (resistivity and IP), and surface methane measurements, are integrated and classified using an unsupervised Neural Network to identify possible risk zones in areas surrounding a landfill. The Neural Network used is a Kohonen type, which generates; as a result, Self-Organizing Classification Maps or SOM (Self-Organizing Map). Two graphic outputs were obtained from the training performed in which groups of neurons that presented a similar behaviour were selected. Contour maps corresponding to the location of these groups and the individual variables were generated to compare the classification obtained and the different anomalies associated with each of these variables. Two of the groups resulting from the classification are related to typical values of liquids percolated in the landfill for the parameters evaluated individually. In this way, a precise delimitation of the affected areas in the studied landfill was obtained, integrating the input variables via SOMs. The location of the study area is not detailed for confidentiality reasons.

James Howells' life changed when he threw out a hard drive about the size of an iPhone 6. Howells, from the city of Newport in southern Wales, had two identical laptop hard drives squirreled away in a drawer in 2013. One was blank; he says the other contained 8,000 bitcoins -- now worth about $181 million, even after the recent crypto crash. He'd meant to throw out the blank one, but instead the drive containing the cryptocurrency ended up going to the local dump in a garbage bag. Nine years later, he's determined to get back his stash, which he mined in 2009. Howells, 36, is hoping local authorities will let him stage a high-tech treasure hunt for the buried bitcoins.

The use of AI for reverse logistics management enables your business to address issues related to waste management and environmental sustainability. Reverse logistics is a part of a bigger concept known as the circular economy, which seeks to tackle problems such as waste reduction, pollution, biodiversity loss and climate change. For building sustainability, governing bodies around the world create product take-back laws, which hold producers financially accountable for factors related to waste recycling, handling of perishable goods and the production of circular products. Managing these activities effectively offers a far greater challenge in reverse logistics for your business. The use of new approaches and tools like AI for reverse logistics can help in overcoming the challenges associated with reverse logistics.

In a move to increase soft plastics recycling, engineering researchers at the University of Sydney are creating a smart, automated robotic system that uses artificial intelligence to sort recyclable waste. Soft plastics lack adequate recycling methods because they easily entangle in waste-separation machinery, which often leads to mechanical failure and contamination of other recyclable materials such as paper. Because of this problem, current recycling methods rely on the manual sorting of soft plastics. Despite an improvement in plastic recycling in recent years, landfill is a growing issue. Soft plastics like cling wrap and plastic bags are a major contributor to the problem, with 94% going to landfill in 2016–17.

Great to have you on the show, Ali. Thank you, Stephan, for having me here. It's a real pleasure to have me here. I listen to your podcast. It's great to be on it. It's great to have you on it. I've been trying to get you on this show for a while now. I'm really happy to have that finally come to pass. I'd love for our listeners to understand a bit more about the under the hood of Trendy Butler. How is this powered by an AI where all your competitors–the different subscription box companies–they seem to rely on human stylists, and you've got an AI? Most of them use a lot of AI to make their decision making. Except with us, we rely heavily on our AI. Obviously, there is still a stylist because as much as you could throw as many AIs as you want on to this, still there's an element of creativity that's needed for a person to be applying to this. You never could control, let's say the system picking an orange short with a purple shirt, you got to sometimes stop it. Most of it is actually done through our essentially wired AI system that picks these clothes for people now. The way it works under the hood, it's a little different from how others do it. I'm sure you heard of things like Stitch Fix, Trunk Club, and all these guys. The little difference is that, instead of having stylists being assisted by this AI to make these decisions, we do it the other way around. We have the AI make the decisions, and the stylists really look and see if they made these maybe improper decision making. The way it works, usually how people attack the problem or others attack the problem, is that each person they bring them up, and then look at the historical goals that they've… Because throughout the process when you signed up, you essentially are asked multiple questions. Those questions essentially help us guide our styling methods and questionnaires. The way we do it is we turn this process upside down. When we acquire a customer, we ask them a set of questions. Those questions could be from your sizing, your preference in patterns, the type of pants that you like. Then we go a little further, we look at your occupation, we look at your location, the location tells us a lot about you. That tells us the type of clothes that you want. If you send someone in Florida a heavy jacket, I'm sure they're not going to like it. The location makes a lot of sense. Occupation makes a lot of sense.

Artificial intelligence technologies can be used to help buildings and spaces track their waste in real-time and engage users by nudging them to correctly sort their waste. According to a study by the World Bank, 98% of the world's waste is sent to landfills, dumped into oceans or being incinerated, even though a high majority of daily consumables are recyclable. This is primarily due to the high level of contaminants found in recyclables, making previously clean material practically unrecyclable and financially unmarketable. In Toronto, for every percentage point decreased in contaminated waste can create up to $1 million in recycling cost savings every year, which can be attributed to the management and sorting costs incurred by the waste hauling and collection companies. Intuitive is a Canadian company which seeks to achieve zero waste through their AI solution, Oscar.

Innovation Saskatchewan plans to use robots to help reduce the amount of solid waste that is placed in landfills. The group unveiled its new technology at Innovation Place on the University of Regina campus on Tuesday from its two winners of the 2019 Innovation Saskatchewan challenge. Prairie Robotics used artificial intelligence and cameras to capture waste data in real time while researchers at the U of R created a system designed to handle Saskatchewan's extreme weather all while being able to weigh moving vehicles. The province expects the two solutions to be beneficial in help to reduce sold waste dumped into landfills by 30 per cent by 2030. "Our tech community has developed a tracking and reporting mechanism using artificial intelligence which can reduce the expense of landfill operations and lead to long-term environmental efficiencies," Innovation Minister Tina Beaudry-Mellor said.