BDWaste: A comprehensive image dataset of digestible and indigestible waste in Bangladesh

Published: 17 November 2023| Version 1 | DOI: 10.17632/96g5pgfnfw.1
, Arfan uddin A,


The dataset describes about two significant classes of waste, which are defined as “waste management." Two classes include biodegradable and non-biodegradable, which contain a total of 21 distinct classes of waste. Biodegradable contains potato peel, lemon peel, rice, paper, fish scale, malt shell, banana peel, coffee cup, sugarcane husk, and egg shell. Non-biodegradable materials include polythene, plastic, wire, glass, gloves, chip packets, empty medicine packets, masks, and bottles. The dataset holds an overall total of 2625 real images of waste. Each image, both outdoor and indoor, used natural light to avoid shadows with an appropriate background. The device used to capture the images is an Android. The whole dataset practices the primary concepts of waste management, gives an overview of improper waste management, provides guidance on recycling, and also serves as valuable resources on waste management for future development purposes.


Steps to reproduce

To complete the dataset step, Step 1. Waste Selection: Selected waste that is easily found in the local area The waste is divided into two significant categories: digestive and indigestive. The set contains a total of 21 distinct classes of waste.Digestive classes are: 1. potato peel; 2. lemon peel; 3. rice. 4. sugarcane husk, 5. fish ash, 6. Malta shell; 7. paper; 8. banana peel; 9. Egg shell; 10. coffee cup; 11. mango peelIndigestive classes are: 1. Polythene 2. Cane 3. Glasses   4. Plastic  5. Wire  6. gloves 7. Chip packet 8. empty medicine packet 9. Mask; 10. Bottle. Step 2. Image Capturing: Capturing an image of selected waste in two verities: indoors (with a visible surface) and outdoors (that can usually be seen). For clicking the images, the devices used are Samsung A51 (1080*2400 pixels, 48 MP), HTC 10 (1440*2560 pixels, 12 MP), Redmi Note 9 (1080*2340 pixels, 48 MP), and Redmi Note 11 (1080*2400 pixels, 50 MP). The picture was collected from the roads, local areas, and waste dumping sites in the Uttara, Mirpur, Gazipur, and Azampur areas of Dhaka.Step 3. Data Storing: Data is stored in a specific file for future use. The dataset contains a total of 2624 images, of which 1390 are in the digestive and 1234 are in the indigestive categories. Step 4. Data Cleaning: Clean the data by removing blurred, noisy, invisible, and dark images from stored data. Step 5. Data Augmentation: Processing the data for representing a formative dataset and adjusting the image's size, angle, shape, and brightness Step 6. Final dataset: Store the output obtained from augmentation in an understandable and clear dataset.


Uttara University, Mawlana Bhashani Science and Technology University


Sustainable Development, Sustainability, Machine Learning, Internet of Things, Digestive System, Image Classification, Hazardous Waste, Waste, Municipal Waste, Sustainable Build Environment, Agricultural Waste, Sustainable Lifestyle, Biodegradable Material, Food Waste, Environmentalism, Deep Learning, Image Analysis, IoT Application, Non-Recyclable Waste