GlobalWasteData: A Large-Scale, Integrated Dataset for Robust Waste Classification and Environmental Monitoring
University of Gujrat（瓜贾特大学）；Rhineland-Palatinate Technical University of Kaiserslautern-Landau（凯斯贝伦-巴拉丁技术大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；Intelligentx GmbH
Misbah Ijaz, Saif Ur Rehman Khan, Abd Ur Rehman, Tayyaba Asif, Sebastian Vollmer, Andreas Dengel, Muhammad Nabeel Asim
The growing amount of waste is a problem for the environment that requires efficient sorting techniques for various kinds of waste. An automated waste classification system is used for this purpose. The effectiveness of these Artificial Intelligence (AI) models depends on the quality and accessibility of publicly available datasets, which provide the basis for training and analyzing classification algorithms. Although several public waste classification datasets exist, they remain fragmented, inconsistent, and biased toward specific environments. Differences in class names, annotation formats, image conditions, and class distributions make it difficult to combine these datasets or train models that generalize well to real world scenarios. To address these issues, we introduce the GlobalWasteData (GWD) archive, a large scale dataset of 89,807 images across 14 main categories, annotated with 68 distinct subclasses. We compile this novel integrated GWD archive by merging multiple publicly available datasets into a single, unified resource. This GWD archive offers consistent labeling, improved domain diversity, and more balanced class representation, enabling the development of robust and generalizable waste recognition models. Additional preprocessing steps such as quality filtering, duplicate removal, and metadata generation further improve dataset reliability. Overall, this dataset offers a strong foundation for Machine Learning (ML) applications in environmental monitoring, recycling automation, and waste identification, and is publicly available to promote future research and reproducibility.
Boston University（波士顿大学）；American University in Cairo（开罗美国大学）；Worcester Polytechnic Institute（伍斯特理工学院）；University of Washington（华盛顿大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of conte…
Earthrise Media（地球上升传媒）；University of Georgia（佐治亚大学）；Minderoo Foundation（明德路基金会）
Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. The system works at continental scale. We evaluated performance in Indonesia and detected 374 waste aggregations, more than double the number of sites found in public databases. The same system deployed across twelve countries in Southeast Asia identifies 996 subsequently confirmed waste sites. For each detected site, we algorithmically monitor…
The ever-increasing amount of global refuse is overwhelming the waste and recycling management industries. The need for smart systems for environmental monitoring and the enhancement of recycling processes is thus greater than ever. Amongst these efforts lies IBM's Wastenet project which aims to improve recycling by using artificial intelligence for waste classification. The work reported in this paper builds on this project through the use of transfer learning and data augmentation techniques to ameliorate classification accuracy. Starting with a convolutional neural network (CNN), a systemat…
Underwater Waste Detection Using Deep Learning A Performance Comparison of YOLOv7 to 10 and Faster RCNN
Faculty of Computing, Sri Lanka Institute of Information Technology（计算学院，斯里兰卡信息科技学院）；Faculty of Information Technology and Communication Sciences, Tampere University（信息科技与通信科学学院，塔尔皮埃大学）；Computing Centre, Faculty of Engineering, University of Peradeniya（工程学院计算机中心，珀德尼亚大学）
Public health and habitat quality are crucial goals of urban planning. In recent years, the severe social and environmental impact of illegal waste dumping sites has made them one of the most serious problems faced by cities in the Global South, in a context of scarce information available for decision making. To help identify the location of dumping sites and track their evolution over time we adopt a data-driven model from the machine learning domain, analyzing satellite images. This allows us to take advantage of the increasing availability of geo-spatial open-data, high-resolution satellit…
Sorting of Smartphone Components for Recycling Through Convolutional Neural Networks
Institute of Informatics, Federal University of Rio Grande do Sul（南里奥格兰德联邦大学信息学研究所）；LACOR – Department of Materials, Federal University of Rio Grande do Sul（南里奥格兰德联邦大学材料系LACOR实验室）
