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Litter on the streets - solid waste detection using VHR images

Ulloa-Torrealba, Yrneh Zarit and Schmitt, Andreas and Wurm, Michael and Taubenböck, Hannes (2023) Litter on the streets - solid waste detection using VHR images. European Journal of Remote Sensing, 56 (1), pp. 1-19. Taylor & Francis. doi: 10.1080/22797254.2023.2176006. ISSN 2279-7254.

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Official URL: https://www.tandfonline.com/doi/full/10.1080/22797254.2023.2176006


Failures in urban areas’ solid waste management lead to clandestine garbage dumping and pollution. This affects sanitation and public human hygiene, deteriorates quality of life, and contributes to deprivation. This study aimed to test a combination of machine learning, highresolution earth observation and GIS data to detect diverse categories of residual waste on the streets, such as sacks and construction debris. We conceptualised five different classes of solid waste from image interpretation: “Sure”, “Half-sure”, “Not-sure”, “Dispersed”, and “Nongarbage”. We tested a combination of k-means-based segmentation and supervised random forest to investigate the capabilities of automatic classification of these waste classes. The model can detect the presence of solid waste on the streets and achieved an accuracy of up from 73.95%–95.76% for the class “Sure”. Moreover, a building extraction using an EfficientNet deep-learning-based semantic segmentation allowed masking the rooftops. This improved the accuracy of the classes “Sure” and “Non-garbage”. The systematic evaluation of all parameters considered in this model provides a robust and reliable method of solid waste detection for decision-makers. These results highlight areas where insufficient waste management affects the citizens of a given city.

Item URL in elib:https://elib.dlr.de/196330/
Document Type:Article
Title:Litter on the streets - solid waste detection using VHR images
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Ulloa-Torrealba, Yrneh ZaritHochschule MünchenUNSPECIFIEDUNSPECIFIED
Schmitt, AndreasHochschule MünchenUNSPECIFIEDUNSPECIFIED
Wurm, MichaelUNSPECIFIEDhttps://orcid.org/0000-0001-5967-1894UNSPECIFIED
Taubenböck, HannesUNSPECIFIEDhttps://orcid.org/0000-0003-4360-9126UNSPECIFIED
Date:20 February 2023
Journal or Publication Title:European Journal of Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:pp. 1-19
Publisher:Taylor & Francis
Keywords:Solid waste; sanitation; deprivation; remote sensing; machine learning; superpixels
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research, R - Geoscientific remote sensing and GIS methods
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Taubenböck, Prof. Dr. Hannes
Deposited On:18 Sep 2023 09:27
Last Modified:19 Oct 2023 14:54

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