This dataset contains information on drug retrieval classification, products, and reasons.

Provision of information on of recyclable collection points in public places of Hong Kong. It covers recycling bins and collection points for paper, metals, plastics, glass bottles, fluorescent lamps, rechargeable batteries, small electrical and electrical equipment, regulated electrical equipment, clothes, barbecue forks and tetra pak.

Recyclable and Household Waste Classification Dataset

This dataset contains a comprehensive collection of 15,000 images (each 256x256 pixels) depicting various recyclable materials, general waste, and household items across 30 distinct categories. With 500 images per category and 250 images per subcategory, this dataset provides a rich and diverse resource for research and development in the fields of waste classification and recycling. By offering a large number of high-quality images, this dataset aims to support the creation of robust and accurate waste sorting and categorization systems.

Dataset Structure

The dataset is organized into a hierarchical folder structure to ensure easy navigation and accessibility. The main folder, named "images," contains subfolders representing specific waste categories or items. These subfolder names serve as the labels for their respective categories, making it convenient for researchers and developers to identify and utilize the images for their specific needs.

You have to split up the dataset into test, train, and validation manually. See Recyclable and Household Waste Classification Code for an example of how to do that.

Within each category subfolder, there are two distinct folders: 1. default: This folder contains standard or studio-like images of the waste item. These images provide a clear and controlled representation of the item, which can be useful for initial training and testing of waste classification models. Each "default" subfolder contains 250 images. 2. real_world: This folder contains images of the waste item in real-world scenarios or environments. These images capture the item in various contexts, such as in a trash bin, on the ground, or in a cluttered environment. The real-world images are essential for evaluating the performance and robustness of waste classification models in practical settings. Each "real_world" subfolder also contains 250 images.

All images in the dataset are provided in the PNG format, ensuring high quality and compatibility with a wide range of image processing and machine learning libraries.

Waste Categories

The dataset covers a wide range of waste categories and items, including: - Plastic: This category includes images of plastic water bottles, soda bottles, detergent bottles, shopping bags, trash bags, food containers, disposable cutlery, straws, and cup lids. These items represent a significant portion of the plastic waste generated in households and are crucial for recycling efforts. - Paper and Cardboard: This category includes images of newspaper, office paper, magazines, cardboard boxes, and cardboard packaging. These items are commonly recycled and play a vital role in reducing deforestation and conserving natural resources. - Glass: This category includes images of beverage bottles, food jars, and cosmetic containers made of glass. Glass is a highly recyclable material, and proper classification and sorting are essential for effective recycling processes. - Metal: This category includes images of aluminum soda cans, aluminum food cans, steel food cans, and aerosol cans. Metal waste is valuable for recycling and can be efficiently processed when correctly identified and separated. - Organic Waste: This category includes images of food waste, such as fruit peels, vegetable scraps, eggshells, coffee grounds, and tea bags. Organic waste can be composted or used for biogas production, reducing the burden on landfills and generating valuable resources. - Textiles: This category includes images of clothing and shoes. Textile waste is a growing concern, and proper classification can aid in recycling efforts and reduce the environmental impact of the fashion industry.

Please refer to the individual subfolders within the dataset for specific examples and instances of each waste category.

Dataset Usage and Applications

The Recyclable and Household Waste Classification Dataset offers a wide range of possibilities for researchers, engineers, and environmental enthusiasts. Some potential uses and applications of this dataset include: - Developing and training machine learning models for automatic waste sorting and categorization. The dataset's diverse range of images and real-world scenarios enables the creation of robust and accurate classification models that can be deployed in waste management facilities, recycling centers, and smart waste bins. - Analyzing the visual characteristics and features of different waste materials. Researchers can use the dataset to study the unique visual properties of various waste items, such as color, shape, texture, and size. This analysis can contribute to the development of more efficient and targeted waste classification algorithms. - Comparing ...

Recycling locations and the list of items one can recycle at the different locations

Information compiled by the Department of Environmental Protection (MassDEP).

Locations of public recycling bins throughout NYC. For information on what to recycle in NYC, see: https://www1.nyc.gov/assets/dsny/site/services/recycling.

A list of drop-off locations to recycle waste oil, oil filters, tires, automotive batteries, metal scrap, aluminum cans, tin cans, glass, plastic, corrugated boxes newspapers and other papers.

In 2020, ** percent of respondents mentioned partnering with a certified electronics recycling company as being a main method of handling data center electronic waste, with ** percent citing that they recycle the hardware as e-waste in-house according to standards.

Announcement for the recycling industry information on waste recycling.

This statistic shows key information on plastic recycling companies worldwide in 2017, broken down by select country. As of that time, there were ** plastic recyclers in Austria that individually processed about ***** tons of plastics per year.

44270 Global export shipment records of Recycle Yarn with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

1379 Global export shipment records of Recycle bins with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Announcement should recycle waste (waste dry batteries) historical recycling data

Dataset from National Environment Agency. For more information, visit https://data.gov.sg/datasets/d_db40d004afeb5a7f0f555fdcc34934cc/view

Aggregated total net tonnages for blue recycle, green waste and black trash barrels by month divided by total tonnages. This view includes summarized results only. Parent dataset contains sensitive route based information and is not publicly accessible.

This filtered view is result of two data transformations. The first is the monthly aggregation of daily tons for disposal locations (greenwaste, recycling, trash) from the Daily Tonnage by Service Type (https://citydata.mesaaz.gov/Environmental-Management-and-Sustainability/Daily-Tonnage-by-Service-Type/pqxn-m75b); the second transformation is derived from taking the aggregated data in Monthly Tonnage by Service Type (https://citydata.mesaaz.gov/Environmental-Management-and-Sustainability/Monthly-Tonnage-by-Service-Type/eqma-9cri) and creates a column titled recycle_diversion_rate, which makes up the data point for this measure. Total tons delivered to Recycle and Greenwaste locations/Total recycle, greenwaste, and trash tons collected.

View Arlington recycle warehouse import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

Global Construction Waste Recycling Market was valued at $28.97Bn in 2022, and is projected to $41.88Bn by 2030, CAGR of 5.40% from 2023 to 2030.

Germany PPI: Recycling(RC) data was reported at 121.400 2021=100 in Mar 2025. This records an increase from the previous number of 121.300 2021=100 for Feb 2025. Germany PPI: Recycling(RC) data is updated monthly, averaging 97.900 2021=100 from Jan 2016 (Median) to Mar 2025, with 111 observations. The data reached an all-time high of 121.400 2021=100 in Mar 2025 and a record low of 92.200 2021=100 in Jan 2016. Germany PPI: Recycling(RC) data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.I033: Producer Price Index: 2021=100.

About the Dataset: Waste Management and Recycling in India

Overview:

This dataset provides comprehensive information on waste management and recycling practices in various cities across India. It includes key data related to waste generation, recycling rates, population density, municipal efficiency, landfill details, and more. The data spans multiple years (2019–2023) and covers a range of waste types, including plastic, organic waste, electronic waste (e-waste), construction waste, and hazardous waste.

Purpose:

The dataset aims to: - Promote efficient waste management practices across Indian cities. - Analyze trends in recycling and waste disposal methods. - Provide insights for improving municipal management systems. - Support research and development in sustainability, environmental science, and urban planning.

Columns:

1. City/District: The name of the Indian city or district.
2. Waste Type: Type of waste generated, e.g., Plastic, Organic, E-Waste, Construction, Hazardous.
3. Waste Generated (Tons/Day): Amount of waste generated in tons per day.
4. Recycling Rate (%): The percentage of waste that is recycled.
5. Population Density (People/km²): The number of people per square kilometer in the city.
6. Municipal Efficiency Score (1-10): A score indicating how effectively the municipality manages waste (e.g., waste segregation, collection, disposal).
7. Disposal Method: The method used for waste disposal (e.g., Landfill, Recycling, Incineration, Composting).
8. Cost of Waste Management (₹/Ton): The cost of managing one ton of waste in Indian Rupees.
9. Awareness Campaigns Count: The number of awareness campaigns organized by the municipality in that year related to waste management.
10. Landfill Name: The name of the landfill site used by the city.
11. Landfill Location (Lat, Long): The geographical location (latitude and longitude) of the landfill.
12. Landfill Capacity (Tons): The total waste capacity (in tons) that the landfill can hold.
13. Year: The year of the data entry, ranging from 2019 to 2023.

Applications:

• Urban Planning: The dataset can be used to analyze and optimize waste management infrastructure in urban areas.
• Sustainability Research: It can help in studying the progress of recycling and waste reduction strategies.
• Policy Making: Government bodies can use this data to craft policies aimed at improving waste management and recycling rates.
• Machine Learning/AI: The dataset can be used to build models for predicting waste generation trends, recycling outcomes, and municipal efficiency.

Sources:

• The data is simulated for this dataset based on average waste management practices observed in Indian cities.
• Real-world data could come from municipal corporations, environmental agencies, and government reports on waste management.

These facilities collect and separate non-putrescible recyclables from the solid waste stream or process previously separated non-putrescible recyclables. This layer provides information about facilities authorized by DEC.Service layer is updated annually and was last updated 9/25.For background information, see Recyclables Handling And Recovery FacilitiesFor layer information or to download layer, see Recyclables Handling and Recovery FacilitiesDownload the metadata to learn more information about how the data was created and details about the attributes. Metadata Link