Litter on the streets - solid waste detection using VHR images
Creators: name: Ulloa-Torrealba, Yrneh Zarit
name: Schmitt, Andreas
name: Wurm, Michael
name: Taubenböck, Hannes
Publisher: name: Taylor & Francis
Year: 2023
Subjects: subject: Space Science
subject: Medicine
subject: Biotechnology
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Statistics
subject: FOS: Mathematics
subject: Science Policy

description: 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, high-resolution 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 “Non-garbage”. 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. The best segmentation using simple linear iterative clustering (SLIC) was achieved with the parameter values 8,000 segments and 0.3 compactness. The following supervised classification of the segmented images using Random Forest yielded an average overall accuracy of 80.18%.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”.The average reflectance values of the classes “Sure” and “non-Garbage” overlapped. Removing the building rooftops from the orthotiles reduced the overlap of the classes mentioned above. This allowed better identification of the class “Sure”.Moreover, rooftop removal helped improve the accuracy of the classifier, from 59.51% to 90.18% to 71.53% to 95.76% in study areas with and without rooftops, respectively. The best segmentation using simple linear iterative clustering (SLIC) was achieved with the parameter values 8,000 segments and 0.3 compactness. The following supervised classification of the segmented images using Random Forest yielded an average overall accuracy of 80.18%. 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”. The average reflectance values of the classes “Sure” and “non-Garbage” overlapped. Removing the building rooftops from the orthotiles reduced the overlap of the classes mentioned above. This allowed better identification of the class “Sure”. Moreover, rooftop removal helped improve the accuracy of the classifier, from 59.51% to 90.18% to 71.53% to 95.76% in study areas with and without rooftops, respectively.