---
title: "Waste Bin Fill Level Dataset"
description: "This waste bin dataset features 8,000 photos of full and overflowing outdoor containers, organized into 1,000 scenes (8 photos per scene: 4 close-up and 4…"
url: "https://unidata.pro/datasets/waste-bin-fill-level/"
date_modified: "2026-11-12T13:53:21+03:00"
language: "en-US"
---
This waste bin dataset features 8,000 photos of full and overflowing outdoor containers, organized into 1,000 scenes (8 photos per scene: 4 close-up and 4 distant shots from different camera angles). It covers streets, courtyards, parks, and other urban locations during daytime, evening, and nighttime. Each scene is annotated with location, bin type, fill level, lid presence, and waste position.

## List of Datasets

- [Graffiti Drawing Detection Video Dataset](https://unidata.pro/datasets/graffiti-drawing-detection-video/)
- [Fall Detection Dataset](https://unidata.pro/datasets/fall-detection/)
- [Abandoned Object Detection Video Dataset](https://unidata.pro/datasets/abandoned-object-detection-video/)
- [Fight Detection Video Dataset](https://unidata.pro/datasets/fight-detection-video/)

## Dataset Structure

### The Numbers Section

**Numbered list:** - **Number:** 8,000 — **Text:** Images

### Tooltips Section

**Tooltip items:**

- **Name:** Computer Vision
- **Name:** Deep Learning
- **Name:** Public Safety
- **Name:** Object Detection
- **Name:** Smart Cities

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Photos of full and overflowing outdoor waste bins from various urban locations and times of day, annotated with fill level and bin details, suitable for training overflow monitoring model |
| Data types | Image |
| Tasks | Public Safety, Computer Vision |
| Total number of files | 8,000 |
| Number of locations | 1,000 |
| Photos per scene | 8 in every scene (4 close, 4 far) |
| Labeling | Metadata (scene id, photo id, location type, camera distance, camera angle, time of day, lighting source, container type, bin type, fill level, presence of lid, location of waste, resolution, size bytes) |

**Media Slider:**

- **Image in the slider:** ![Waste Bin Fill Level Dataset](https://unidata.pro/wp-content/uploads/2026/11/waste-bin-dataset-sample.webp)
- **Image in the slider:** ![Waste Bin Fill Level Dataset](https://unidata.pro/wp-content/uploads/2026/11/waste-bin-dataset-sample2.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/u/3/folders/1CvUgPJfx471pm_MHOxNvlWgEPpnx0fm3)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Image extension | JPG |
| Total size | about 15.0 GB |
| File size | 0.16–1.40 MB, median 0.62 MB |
| Resolution | 1080x1440, 1440x1080 |

**Source and data collection methodology:** Source and collection methodology. Data was captured via digital cameras.

### Statistics - Charts

**Charts with Titles:**

- **Shortcode:** [ays_chart id='95'] — **caption above the graph:** Location Type (by scene)
- **Shortcode:** [ays_chart id='96'] — **caption above the graph:** Fill Level (by scene)
- **Shortcode:** [ays_chart id='97'] — **caption above the graph:** Time of Day (by scene)
- **Shortcode:** [ays_chart id='98'] — **caption above the graph:** Lighting Source (by scene)
- **Shortcode:** [ays_chart id='100'] — **caption above the graph:** Bin Material (by scene)
- **Shortcode:** [ays_chart id='99'] — **caption above the graph:** Container Type (by scene)
- **Shortcode:** [ays_chart id='101'] — **caption above the graph:** Waste Location (by scene)
- **Shortcode:** [ays_chart id='102'] — **caption above the graph:** Camera Angle (by photo)
- **Shortcode:** [ays_chart id='103'] — **caption above the graph:** Resolution (by photo)

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Facility & Property Management — **Title:** Bin level monitoring for shared trash areas — **Text:** This waste bin dataset can help property managers train computer vision models that check trash areas behind apartment blocks, offices and shopping centers. A bin level monitoring system built on it might show when containers are full, so staff book extra pickups when needed and avoid paying for unnecessary collections.
- **Industry:** Hospitality & Food Service — **Title:** Fill level monitoring for restaurant waste — **Text:** Restaurants, hotels and food courts can use the dataset to train detection models for fill level monitoring of outdoor bins. Such models might warn managers before containers overflow, helping them prevent odors, pests and health inspection issues. The annotated images can support pickup planning for sorted plastic waste and recycling.
- **Industry:** Events & Venue Management — **Title:** Overflow detection at stadiums and festivals — **Text:** Event organizers can apply the dataset to build a waste monitoring system for concerts, sports matches and festivals, where trash bins fill within hours. Trained models might detect overflowing containers from venue cameras and alert cleaning teams on site. This helps keep walkways free of litter and lowers cleanup costs.
- **Industry:** Waste Tech & IoT Development — **Title:** Training vision models for smart waste products — **Text:** Startups building smart bins, sensor networks and route optimization software can use this waste dataset as training data for object detection and segmentation. It might improve model performance in real outdoor conditions and pair well with waste classification algorithms, helping teams bring products for automating waste management to market faster.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** Can I request a sample of the dataset before purchasing? — **Answer:** Yes, you can request a sample of the waste bin fill level dataset before purchasing the complete collection. Free samples are available for trial and testing, allowing you to evaluate image quality, fill-level labels, camera angles, and metadata structure.
- **Question:** What is this waste bin fill dataset can be used for? — **Answer:** The dataset is designed for training and evaluating computer vision models that detect full and overflowing outdoor waste containers. It supports waste monitoring systems, bin level monitoring systems, overflow detection, urban sanitation analytics, and automated waste management applications.
- **Question:** What types of annotations are provided in the dataset? — **Answer:** The dataset includes metadata annotations for scene ID, photo ID, location type, camera distance, camera angle, time of day, lighting source, container type, bin type, fill level, lid presence, and waste position. Additional technical metadata includes image resolution and file size, supporting organized dataset analysis and model evaluation.
- **Question:** What waste-bin fill levels are represented in the dataset? — **Answer:** The dataset focuses on full and overflowing outdoor waste containers, with fill-level information included in the metadata. These annotations can support the development of models that identify bins requiring collection or maintenance.
- **Question:** Which urban environments are represented in the waste bin fill level dataset? — **Answer:** The dataset covers outdoor urban environments such as streets, courtyards, parks, and other public locations. This environmental variation makes it relevant to waste monitoring systems designed for different urban sanitation settings.
- **Question:** Does the dataset include photographs captured at different times of day? — **Answer:** Yes, the dataset includes scenes captured during daytime, evening, and nighttime conditions. Time-of-day and lighting-source metadata can help researchers evaluate how illumination changes affect waste-bin recognition and overflow detection.
- **Question:** Is the waste bin dataset real-world or synthetic? — **Answer:** The dataset consists of photographs captured using digital cameras rather than computer-generated images. It documents outdoor waste containers in urban environments, providing photographic data for real-world waste monitoring and fill-level detection research.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model: free samples are provided for trial and testing, while complete datasets are available exclusively through purchase. This allows users to assess dataset suitability before obtaining the complete collection.
- **Question:** How are Unidata datasets stored? — **Answer:** Unidata stores all datasets securely on AWS cloud infrastructure, ensuring high availability and scalability. Our storage and management practices are aligned with ISO 27001 and ISO 27701 standards, which guarantee compliance with internationally recognized information security and privacy management requirements.
- **Question:** How long does it take to receive the dataset? — **Answer:** Once you submit a request, Unidata will reach out to you to review the details and complete the necessary documents. After signing and payment, the dataset will be delivered within 3–10 days.

[Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)