Note: While the D.Waste App provides helpful guidance, it may occasionally make mistakes or be unclear. If you’re unsure or have any questions, please contact recycle@umd.edu for clarification.
Bins and Accepted Materials
Bins Color and Accepted Materials
Remarks:
UMD Waste Signages/ Bins
No signages available for this pilot.
Image source: University of Maryland (UMD)
🚛 Collection Guidelines
- ♻️ Recycling Collection Day: Monday
- 🗑️ Trash Collection Days: Wednesday and Sunday
- 📤 Placement of Items: Place trash and recycling outside your apartment between 7 and 11 AM on pickup day. Do not leave items in the hallway overnight.
- 🛍️ Properly Bundle Trash: Use closed garbage bags for all trash.
- 📦 Properly Bundle Recycling: Break down boxes and, if possible, use one to contain other recyclables.
⚠️ Please install the D.Waste app first. The QR code will only open the rules in the app.
(*If you haven’t installed the app yet, download it below for your device.*)
Scan or Look for on the D.Waste App
📸 You can scan the QR code below using your phone’s camera app or search for directly in the D.Waste app to view institution-specific waste rules and accepted items.
🎥 How-To Videos
✅ How to Opt In to This Campaign
New to DWaste? No worries! This short tutorial walks you through the simple steps to opt in to the KU campaign. You’ll learn how to register, activate your participation, and start contributing to a cleaner campus while earning reward points. Whether you're a student, faculty, or staff getting started only takes a minute!
🎁 How to Redeem Rewards
Your sustainable actions earn you points, which you can redeem through our amazing partner network! This video shows you how to explore available partners, choose your preferred reward (like discounts, gifts, or eco-friendly products), and complete the redemption process. Supporting sustainability has never been this rewarding or this easy!
📊 Research & Dataset Outcomes
Insights gathered from this pilot directly contributed to the creation of the WasteSense Dataset, a first-of-its-kind dataset developed in Nepal. It features high-quality, annotated images captured near campus waste stations, designed to support AI research in waste management.
The accompanying research study based on this dataset is published through Kathmandu University and can be found on their official site here.
Edge Device Experiments
An edge device was installed near waste bins in a co-working space at the
Herbert Rabin Technology Advancement Building to collect real-time waste
stream data and evaluate device performance in a live environment.
We thank
MTech Ventures
for supporting this
initiative and helping place the device near the bins. The deployment ran
for one month, from November 19, 2025 to
December 19, 2025, capturing data to assess
waste patterns and detect contamination.
The full case study can be found here.