Electronic waste generation worldwide stood at roughly 62 million metric tons in 2022. Several factors, such as increased spending power, and the availability of electronics, has fueled e-waste generation in recent decades, making it the fastest growing waste stream worldwide. This trend is expected to continue, with annual e-waste generation forecast at 82 million metric tons in 2030.

How much e-waste do people produce?

Globally, e-waste generation per capita averaged 7.8 kilograms in 2022. However, this differs greatly depending on the region. While Asia produces the most e-waste worldwide in volume, Europe and Oceania were the regions with the highest e-waste generation per capita, at 17.6 and 16.1 kilograms respectively.

E-waste disposal

In 2022, the share of e-waste formally collected and recycled worldwide stood at 22.3 percent. Meanwhile, around 48 million metric tons are estimated to have been collected informally, with 29 percent of this value being disposed as residual waste, most likely ending up in landfills. Due to the hazardous materials that are often used in electronics, improper e-waste disposal is a growing environmental concern worldwide.

The dataset contains year- and state-wise total quantity of electronic waste (E-waste) which is collected and processed.

Note:

The blank cells in the dataset represent no data being reported by the respective states

E-waste generation worldwide has nearly doubled since 2010, from 33.8 million metric tons to roughly 62 million tons in 2022. Electronic waste is one of the fastest growing waste streams, with global e-waste generation projected to reach 82 million metric tons by 2030.

What makes up electronic waste? In 2022, small equipment, such as vacuum cleaners, microwaves, toasters, and electric kettles made up the largest share of global electronic waste generation, at more than 20.4 million metric tons. Another 15 million metric tons of large equipment waste was also generated that year. Although still accounting for less than one percent of e-waste generated worldwide, the growth in solar PV capacity worldwide has seen photovoltaic panels as a growing waste stream.

Where is electronic waste generated?

China is by far the largest e-waste generating country worldwide, with more than 12 million metric tons generated in 2022. In fact, Asia accounted for nearly half of all e-waste generated that year. Nevertheless, when it comes to e-waste generation per capita, four of the top five countries were located in Europe, with Norway leading the ranking at 26.8 kilograms per inhabitant.

Presence of various types of household electronic waste (eWaste) and disposal methods used in previous 12 months.

The North America E-Waste Management Market Report is Segmented by Material Type (Metal, Plastic, Glass, and Other Materials), Source Type (Consumer Electronics, Industrial Electronics, and Other Sources), Application (Trashed and Recycled), and Country (United States, Canada, and Mexico). The Report Offers Market Size and Forecasts for all the Above Segments in Value (USD).

Global E-Waste Management Market size was worth around $63.15 Billion in 2023 and is predicted to grow to around $219.23 Billion by 2032

In 2022, Mexico generated 1.5 million metric tons of electronic waste, an increase of 3.5 percent in comparison to the previous year. This represented an average e-waste generation per capita of nearly 12 kilograms that year. Mexico has become one of the largest e-waste producers worldwide.

Overview

The goal of this project was to create a structured dataset which can be used to train computer vision models to detect electronic waste devices, i.e., e-waste or Waste Electrical and Electronic Equipment (WEEE). Due to the often-subjective differences between e-waste and functioning electronic devices, a model trained on this dataset could also be used to detect electronic devices in general. However, it must be noted that for the purposes of e-waste recognition, this dataset does not differentiate between different brands or models of the same type of electronic devices, e.g. smartphones, and it also includes images of damaged equipment.

The structure of this dataset is based on the UNU-KEYS classification Wang et al., 2012, Forti et al., 2018. Each class in this dataset has a tag containing its corresponding UNU-KEY. This dataset structure has the following benefits: 1. It allows the user to easily classify e-waste devices regardless of which e-waste definition their country or organization uses, thanks to the correlation between the UNU-KEYS and other classifications such as the HS-codes or the EU-6 categories, defined in the WEEE directive; 2. It helps dataset contributors focus on adding e-waste devices with higher priority compared to arbitrarily chosen devices. This is because electronic devices in the same UNU-KEY category have similar function, average weight and life-time distribution as well as comparable material composition, both in terms of hazardous substances and valuable materials, and related end-of-life attributes Forti et al., 2018. 3. It gives dataset contributors a clear goal of which electronic devices still need to be added and a clear understanding of their progress in the seemingly endless task of creating an e-waste dataset.

This dataset contains annotated images of e-waste from every UNU-KEY category. According to Forti et al., 2018, there are a total of 54 UNU-KEY e-waste categories.

Description of Classes

At the time of writing, 22. Apr. 2024, the dataset has 19613 annotated images and 77 classes. The dataset has mixed bounding-box and polygon annotations. Each class of the dataset represents one type of electronic device. Different models of the same type of device belong to the same class. For example, different brands of smartphones are labelled as "Smartphone", regardless of their make or model. Many classes can belong to the same UNU-KEY category and therefore have the same tag. For example, the classes "Smartphone" and "Bar-Phone" both belong to the UNU-KEY category "0306 - Mobile Phones". The images in the dataset are anonymized, meaning that no people were annotated and images containing visible faces were removed.

The dataset was almost entirely built by cloning annotated images from the following open-source Roboflow datasets: [1]-[91]. Some of the images in the dataset were acquired from the Wikimedia Commons website. Those images were chosen to have an unrestrictive license, i.e., they belong to the public domain. They were manually annotated and added to the dataset.

Cite This Project

This work was done as part of the PhD of Dimitar Iliev, student at the Faculty of German Engineering and Industrial Management at the Technical University of Sofia, Bulgaria and in collaboration with the Faculty of Computer Science at Otto-von-Guericke-University Magdeburg, Germany.

If you use this dataset in a research paper, please cite it using the following BibTeX: @article{iliev2024EwasteDataset, author = "Iliev, Dimitar and Marinov, Marin and Ortmeier, Frank", title = "A proposal for a new e-waste image dataset based on the unu-keys classification", journal = "XXIII-rd International Symposium on Electrical Apparatus and Technologies SIELA 2024", year = 2024, volume = "23", number = "to appear", pages = {to appear} note = {under submission} }

Contribution Guidelines

Image Collection

1. Choose a specific electronic device type to add to the dataset and find its corresponding UNU-KEY. * The chosen type of device should have a characteristic design which an object detection model can learn. For example, CRT monitors look distinctly different than flat panel monitors and should therefore belong to a different class, regardless that they are both monitors. In contrast, LED monitors and LCD monitors look very similar and are therefore both labelled as Flat-Panel-Monitor in this dataset.
2. Collect images of this type of device. * Take note of the license of those images and their author/s to avoid copyright infringement. * Do not collect images with visible faces to protect personal data and comply w

The Electronic Goods Recycling industry has performed well, with increased direct and indirect subsidization driving growth. Electronics recyclers earn most of their revenue from direct or indirect government subsidization. Over the past decade, both the average number of electronics owned by each consumer and the rate at which electronics are replaced have grown significantly. Purchases of recycled commodities remained strong even during recent turbulence, with the doubling of aluminum and copper prices driving downstream customers to less expensive, recycled metals. Consequently, industry revenue is forecast to increase at a CAGR of 8.0% to total $28.1 billion over the five years to 2024, including growth of 6.2% in 2024 alone. The harmful effects of improperly discarded electronics on the environment and human health have driven public calls to develop electronics recycling infrastructure. While no comprehensive federal law exists to address the issue of e-waste, many municipalities and states have implemented legislation to tackle the problem. With existing laws becoming more stringent, electronic goods recyclers have flourished. Larger electronic goods recyclershave slowly captured a larger revenue share, leveraging scale to expand operations by leveraging productive but costly technology. Likewise, they have relied on their size to secure favorable supply contracts for inputs, the industry's primary expense. This has allowed them to become ever more profitable, widening profit margins across the industry. Electronics recycling will continue growing as rising public concern over e-waste waste leads to greater government regulation. Additionally, while recyclers earn most of their revenue from providing electronics recycling services, a smaller portion of industry revenue is generated by selling various precious metals extracted from the electronics recycling process. With the prices of many of these materials expected to rise in the years to come, industry revenue will rise as customers opt for less expensive recycled metals over virgin alloys. As a result, industry revenue is forecast to grow at a CAGR of 4.5% to $35.1 billion over the five years to 2029.

A current listing of NYS Registered Electronic Waste Recycling Facilities. Electronic waste types accepted vary from facility to facility.

Global Electronic Waste Management Market size will exceed a valuation of USD 121.49 billion by 2032, to grow at a CAGR of 9.60% during the forecast period.

Overview

The goal of this project was to create a structured dataset which can be used to train computer vision models to detect electronic waste devices, i.e., e-waste or Waste Electrical and Electronic Equipment (WEEE). Due to the often-subjective differences between e-waste and functioning electronic devices, a model trained on this dataset could also be used to detect electronic devices in general. However, it must be noted that for the purposes of e-waste recognition, this dataset does not differentiate between different brands or models of the same type of electronic devices, e.g. smartphones, and it also includes images of damaged equipment.

The structure of this dataset is based on the UNU-KEYS classification Wang et al., 2012, Forti et al., 2018. Each class in this dataset has a tag containing its corresponding UNU-KEY. This dataset structure has the following benefits: 1. It allows the user to easily classify e-waste devices regardless of which e-waste definition their country or organization uses, thanks to the correlation between the UNU-KEYS and other classifications such as the HS-codes or the EU-6 categories, defined in the WEEE directive; 2. It helps dataset contributors focus on adding e-waste devices with higher priority compared to arbitrarily chosen devices. This is because electronic devices in the same UNU-KEY category have similar function, average weight and life-time distribution as well as comparable material composition, both in terms of hazardous substances and valuable materials, and related end-of-life attributes Forti et al., 2018. 3. It gives dataset contributors a clear goal of which electronic devices still need to be added and a clear understanding of their progress in the seemingly endless task of creating an e-waste dataset.

This dataset contains annotated images of e-waste from every UNU-KEY category. According to Forti et al., 2018, there are a total of 54 UNU-KEY e-waste categories.

Description of Classes

At the time of writing, 22. Apr. 2024, the dataset has 19613 annotated images and 77 classes. The dataset has mixed bounding-box and polygon annotations. Each class of the dataset represents one type of electronic device. Different models of the same type of device belong to the same class. For example, different brands of smartphones are labelled as "Smartphone", regardless of their make or model. Many classes can belong to the same UNU-KEY category and therefore have the same tag. For example, the classes "Smartphone" and "Bar-Phone" both belong to the UNU-KEY category "0306 - Mobile Phones". The images in the dataset are anonymized, meaning that no people were annotated and images containing visible faces were removed.

The dataset was almost entirely built by cloning annotated images from the following open-source Roboflow datasets: [1]-[91]. Some of the images in the dataset were acquired from the Wikimedia Commons website. Those images were chosen to have an unrestrictive license, i.e., they belong to the public domain. They were manually annotated and added to the dataset.

Cite This Project

This work was done as part of the PhD of Dimitar Iliev, student at the Faculty of German Engineering and Industrial Management at the Technical University of Sofia, Bulgaria and in collaboration with the Faculty of Computer Science at Otto-von-Guericke-University Magdeburg, Germany.

If you use this dataset in a research paper, please cite it using the following BibTeX: @article{iliev2024EwasteDataset, author = "Iliev, Dimitar and Marinov, Marin and Ortmeier, Frank", title = "A proposal for a new e-waste image dataset based on the unu-keys classification", journal = "XXIII-rd International Symposium on Electrical Apparatus and Technologies SIELA 2024", year = 2024, volume = "23", number = "to appear", pages = {to appear} note = {under submission} }

Contribution Guidelines

Image Collection

1. Choose a specific electronic device type to add to the dataset and find its corresponding UNU-KEY. * The chosen type of device should have a characteristic design which an object detection model can learn. For example, CRT monitors look distinctly different than flat panel monitors and should therefore belong to a different class, regardless that they are both monitors. In contrast, LED monitors and LCD monitors look very similar and are therefore both labelled as Flat-Panel-Monitor in this dataset.
2. Collect images of this type of device. * Take note of the license of those images and their author/s to avoid copyright infringement. * Do not collect images with visible faces to protect personal data and comply w

The global electronic waste (e-waste) management market is experiencing robust growth, driven by increasing electronic device consumption, stricter environmental regulations, and a rising awareness of the environmental and health hazards associated with improper e-waste disposal. The market, estimated at $75 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $130 billion by 2033. This growth is fueled by several key factors, including the expanding adoption of electronics across developing economies, the increasing lifespan of electronic devices necessitating professional recycling, and the growing demand for precious metals recovered from e-waste. The plastic management segment holds a significant market share due to the large volume of plastic components in electronics, while the IT equipment application segment dominates due to the rapid obsolescence cycle of computers and smartphones. North America and Europe currently hold the largest market share, driven by high electronic consumption and established recycling infrastructure, however, the Asia-Pacific region is expected to demonstrate significant growth owing to rapid economic development and increasing e-waste generation. Major challenges hindering market expansion include the lack of standardized e-waste recycling practices across regions, high collection and processing costs, and the informal sector's significant role in e-waste management, which often lacks proper environmental controls. Key players like Tetronics, Umicore, and Sims Lifecycle Services are actively investing in advanced recycling technologies and expanding their global footprint to capitalize on the growing market opportunities. The future of the e-waste management market will likely be shaped by advancements in e-waste processing technologies, increased government regulations, and growing consumer awareness leading to greater participation in formal recycling programs. This will ultimately drive the market towards more sustainable and environmentally responsible practices.

The E-Waste Management Market Report is Segmented by Material (Metal, Plastic, and Others), and by Source (Household Appliances, Entertainment and Consumer Electronics, It & Telecommunication, Medical Equipment, and Others, and by Geography (North America, Europe, Asia-Pacific, Middle East & Africa, and Latin America). The Report Offers Market Sizes and Forecasts in Value (USD) for all the Above Segments.

TECNALIA WEEE HYPERSPECTRAL DATASET

We present a dataset containing hyperspectral images of Waste from Electrical and Electronic Equipment (WEEE) scrap. These dataset contains pieces of copper, brass, aluminum, stainless steel and white cooper.Images contain 76 uniformly distributed wave-lengths in the spectral range [415.05 nm, 1008.10 nm].Images were calibrated by using a white reference spectralon pattern and a dark spectralon pattern as depicted on the associated paper.

Dataset content:XXXX.mat file: It contains two variables, 'hyperfile': Contains the hyperspectral data of the image, and 'bands': It contains the reference to each band.XXXX.png file: RGB representation of the imageXXXX_gt.png file: It contains a image that assigns each pixel to a specific class according to the following index table:Dataset classes:- Background: 0- Copper: 1- Brass: 2- Aluminum: 3- Lead: 4 [NOT PRESENT]- Stainless Steel: 5- White_Copper: 6

Creators: Artzai Picon (TECNALIA) Arantza Bereciartua (TECNALIA)

Dataset citation: Picon, A., Ghita, O., Iriondo, P. M., Bereciartua, A., & Whelan, P. F. (2010, September). Automation of waste recycling using hyperspectral image analysis. In 2010 IEEE 15th Conference on Emerging Technologies & Factory Automation (ETFA 2010) (pp. 1-4). IEEE.

You can get more theoretical information on the dataset and methods used here: Picón, A., Ghita, O., Whelan, P. F., & Iriondo, P. M. (2009). Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data. IEEE Transactions on Industrial Informatics, 5(4), 483-494.

Hyperspectral deep learning methods and code on:

https://github.com/samtzai/tecnalia_weee_hyperspectral_dataset

Picon, A.; Galan, P.; Bereciartua-Perez, A.; Benito-del-Valle, L. Hyperspectral Dataset and Deep Learning Methods for Waste from Electric and Electronic Equipment Identification (WEEE). arXiv July 5, 2024. http://arxiv.org/abs/2407.04505

Discover the essential role of e-waste recycling plants in reducing environmental harm and recovering valuable materials from discarded electronics. Learn about the processes and technologies involved, from collection to recycling and reuse, and the importance of coordinated efforts for effective e-waste management.

China is by far the largest producer of electronic waste worldwide, generating more than 12 million metric tons worth in 2022. The United States followed, with roughly seven million metric tons produced. Global electronic waste generation amounted to approximately 62 million metric tons in 2022 and is expected to increase further in the coming years.

What is electronic waste?

Electronic waste is often referred to as e-waste, and is the fastest growing waste stream worldwide. E-waste consists of electronic equipment that has reached the end of its useful life. It includes a wide variety of products used in everyday life such as old phones, televisions, fridges, and air conditioners. The most common type of e-waste is small equipment as microwaves, electric kettles, and cameras.

E-waste disposal

Due to electronic products often containing harmful components, proper disposal of e-waste is imperative. However, the destination of e-waste generated worldwide still goes mostly undocumented, with millions of tons estimated to end up annually in landfills. Improper disposal can not only cause major environmental hazards, such as toxic chemical leaching; e-waste contains valuable resources such as gold, silver, and platinum. It is projected that billions of dollars’ worth of these valuable metals are discarded with e-waste every year.

India E-Waste Management Market is projected to reach USD 5,198.52 million by 2032, growing at a CAGR of 13.52% from 2024-2032.

Electronic Waste Recycling Market size was valued at USD 33.46 Billion in 2024 and is projected to reach USD 71.93 Billion by 2032, growing at a CAGR of 10.05% from 2026 to 2032

Electronic Waste Recycling Market Drivers

Rapid Technological Advancements: The constant development and release of new electronic devices lead to shorter product lifecycles and increased obsolescence.

Rising Consumer Demand: Increased consumer spending and accessibility to electronic devices contribute to a surge in e-waste generation.

Global Consumption Patterns: Increase of consumerism, and the need to always have the latest electronics.

Extended Producer Responsibility (EPR): EPR legislation holds manufacturers responsible for the end-of-life management of their products, encouraging recycling initiatives.

Hazardous Substance Restrictions: Regulations like RoHS (Restriction of Hazardous Substances) limit the use of harmful materials in electronics, promoting safer recycling processes.

E-waste a growing problem? Learn how stricter regulations and better recycling are driving the e-waste management market. Discover challenges and solutions!