Visual pollution real images benchmark dataset on the public roads

The term quality of life (QoL) refers to a wide range of mul-tifaceted concepts that often involve subjective assessments of both positive and negative aspects of life. It is difficult to quantify QoL as the word has varied meanings in different academic areas and may have different connotations in dif-ferent circumstances. The five sectors most commonly associ-ated with QoL, however, are Health, Education, Environmen-tal Quality, Personal Security, Civic Engagement, and Work -Life Balance. An emerging issue that falls under environmen-tal quality is visual pollution (VP) which, as detailed in this study, refers to disruptive presences that limit visual abil-ity in public roads with an emphasis on excavation barriers, potholes, and dilapidated sidewalks. Quantifying VP has al-ways been difficult due to its subjective nature and lack of a consistent set of rules for systematic assessment of visual pollution. This emphasizes the need for research and mod-ule development that will allow government agencies to au- tomatically predict and detect VP. Our dataset was collected from different regions in the Kingdom of Saudi Arabia (KSA) via the Ministry of Municipal and Rural Affairs and Housing (MOMRAH) as a part of a VP campaign to improve Saudi Ara-bia's urban landscape. It consists of 34,460 RGB images sepa-rated into three distinct classes: excavation barriers, potholes, and dilapidated sidewalks. To annotate all images for detec-tion (i.e., bounding box) and classification (i.e., classification label) tasks, the deep active learning strategy (DAL) is used where an initial 1,200 VP images (i.e., 400 images per class) are manually annotated by four experts. Images with more than one object increase the number of training object ROIs which are recorded to be 8,417 for excavation barriers, 25,975 for potholes, and 7,412 for dilapidated sidewalks. The MOM -RAH dataset is publicly published to enrich the research do-main with the new VP image dataset.& COPY; 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )