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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65318

WALL.E: An AI and Cloud-Based Waste Management System with Multi-Scale Object Detection and Geospatial Hotspot Analytics

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65318

Volume:

10 |

6 |

Jun 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65318

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

TA

Tejas Apte

Computer Engineering

Zeal College of Engineering and Research

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OB

Om Bhalerao

Computer Engineering

Zeal College of Engineering and Research

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YK

Yash Khope

Computer Engineering

Zeal College of Engineering and Research

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OD

Om Desai

Computer Engineering

Zeal College of Engineering and Research

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Mentors

PSG

Prof. Savita Ghatte

Computer Engineering

Zeal College of Engineering and Research

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Abstract

Rapid urbanization creates immense pressure on mu-nicipal solid waste management systems. Tradi-tional collection methods depend heavily on man-ual segregation and static reporting protocols, re-sulting in high operational costs, unhygienic condi-tions, and human error [10]. This paper presents the WALL.E Waste Classification System, an end-to-end cloud-based and AI-powered pipeline designed to automate municipal waste management. The pro-posed framework integrates a serverless computer vi-sion architecture using an enhanced Faster R-CNN with a Feature Pyramid Network (FPN) backbone to classify waste into Wet, Dry, E-Waste, and Mixed categories [5]. Furthermore, the system incorporates real-time geolocation tracking via GPS geotagging to perform area-wise spatial analytics, generating local-ized waste density heatmaps and identifying chronic dumping hotspots. Experimental results confirm that splitting operations into data-driven serverless clas-sification models and geographic aggregation mod-els dramatically reduces municipal oversight delays, advancing smart city resource allocation capabilities [13].

KEYWORDS

WALL.E Waste Classification System Wet Dry E-Waste and Mixed categories

Cite This Article

Tejas Apte, Om Bhalerao, Yash Khope, Om Desai, Prof. Savita Ghatte, "WALL.E: An AI and Cloud-Based Waste Management System with Multi-Scale Object Detection and Geospatial Hotspot Analytics," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-6, Jun 2026. https://doi.org/10.55041/IJSREM65318

Full Article/Conclusion

The WALL.E Waste Classification System demon-strates an effective framework for modern smart city sanitation by combining deep learning, cloud sys-tems, and area-wise geographic analytics. By uti-lizing a multi-scale Faster R-CNN and Feature Pyra-mid Network pipeline, the system reliably automates waste categorization. Decoupling computer vision processing from geospatial aggregation allows munic-ipal authorities to replace manual, reactive workflows with data-driven urban planning, identifying chronic waste hotspots and optimizing resource distribution across the city.

References

[1]   Y. Zhang et al., “TAMER: Tree-Aware Trans-former for Handwritten Mathematical Expression Recognition,” AAAI, 2024. [2]   R. Smith, “An Overview of the Tesseract OCR Engine,” ICDAR, 2007. [3]   P. F. Proen¸ca and P. Sim˜oes, “TACO: Trash An-notations in Context for Litter Detection,” arXiv preprint arXiv:2003.06975, 2020. [4]   D. Zeng, S. Zhang, F. Chen, and Y. Wang, “Multi-scale CNN based garbage detection of air-borne hyperspectral data,” IEEE Access, vol. 7, pp. 64568–64577, 2019, doi: 10.1109/AC-CESS.2019.2917539. [5]   C. Cao, B. Wang, W. Zhang, X. Zeng, X. Yan, Z. Feng, Y. Liu, and Z. Wu, “An improved Faster R-CNN for small object detection,” IEEE Access, vol. 7, pp. 106838–106846, 2019, doi: 10.1109/ACCESS.2019.293273. [6]   L. Jiao and J. Zhao, “A Survey on the New Gen-eration of Deep Learning in Image Processing,” IEEE Access, vol. 7, pp. 172231–172268, Dec. 2019, doi: 10.1109/ACCESS.2019.2956508. [7]   W. Ma, X. Wang, and J. Yu, “A lightweight feature fusion single shot multibox detector for garbage detection,” IEEE Access, vol. 8, pp. 207646–207657, 2020, doi: 10.1109/AC-CESS.2020.3038880. [8]   Z. Kang, J. Yang, G. Li, and Z. Zhang, “An Automatic Garbage Classification System Based on Deep Learning,” IEEE Access, vol. 8, pp. 140018–140030, Aug. 2020, doi: 10.1109/AC-CESS.2020.3010496. [9]   N. C. A. Sallang, M. T. Islam, M. S. Islam, and H. Arshad, “A CNN-Based Smart Waste Manage-ment System Using TensorFlow Lite and LoRa-GPS Shield in Internet of Things Environment,” IEEE Access, vol. 9, pp. 153559–153571, Nov. 2021, doi: 10.1109/ACCESS.2021.3128314. [10]   I. Sosunova and J. Porras, “IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review,” IEEE Access, vol. 10, pp. 73325–73348, Jul. 2022, doi: 10.1109/AC-CESS.2022.3188308.

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Publication Date

6/23/2026

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