Accurate Detection of Illegal Dumping Sites Using High Resolution Aerial Photography and Deep Learning

Padubidri, Chirag; Kamilaris, Andreas; Karatsiolis, Savvas

doi:10.1109/percomworkshops53856.2022.9767451

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…The precondition for reaping the benefits of CV for landfill discovery is the availability of high-quality datasets for training, validating, and testing predictive models. At present no such datasets exist for the landfill discovery task and this gap penalizes the research towards scalable and accurate detection methods [2][3][4][5][6] .…”

Section: Background and Summarymentioning

confidence: 99%

AerialWaste dataset for landfill discovery in aerial and satellite images

Torres

Fraternali

2023

Sci Data

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Illegal landfills are sites where garbage is dumped violating waste management laws. Aerial images enable the use of photo interpretation for territory scanning and landfill detection but this practice is hindered by the manual nature of this task which also requires expert knowledge. Deep Learning methods can help capture the analysts’ expertise and build automated landfill discovery tools. However, this goal requires public high-quality datasets for model training and testing. At present no such datasets exist and this gap penalizes the research toward scalable and accurate landfill discovery methods. We present a dataset for landfill detection featuring airborne, WorldView-3, and GoogleEarth images annotated by professional photo interpreters. It comprises 3,478 positive and 6,956 negative examples. Most positive instances are characterized by metadata: the type of waste, its storage mode, the type of the site, and the evidence and severity of the illicit. The dataset has been technically validated by building an accurate landfill detector and is accompanied by a visualization and annotation tool.

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Section: Background and Summarymentioning

confidence: 99%

AerialWaste dataset for landfill discovery in aerial and satellite images

Torres

Fraternali

2023

Sci Data

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“…The previous approaches for monitoring solid waste dumps relied on manual inspections and video surveillance, both of which have many drawbacks [8]. These approaches have limited detection range and cannot comprehensively cover monitoring areas, especially in remote areas such as suburbs, mountains, riverbanks, and road edges [9,10]. Inasmuch, many scholars have applied remote sensing technology to relevant research, such as Gill et al [11], who used thermal infrared remote sensing technology to measure land surface temperature (LST) and outline the most likely dumping areas within landfill sites.…”

Section: Introductionmentioning

confidence: 99%

A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery

Liu,

Zhao,

Zhang

et al. 2024

Applied Sciences

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The development of global urbanization has brought about a significant amount of solid waste. These untreated wastes may be dumped in any corner, causing serious pollution to the environment. Thus, it is necessary to accurately obtain their distribution locations and detailed edge information. In this study, a practical deep learning network for recognizing solid waste piles over extensive areas using unmanned aerial vehicle (UAV) imagery has been proposed and verified. Firstly, a high-resolution dataset serving to solid waste detection was created based on UAV aerial data. Then, a dual-branch solid waste semantic segmentation model was constructed to address the characteristics of the integration of solid waste distribution with the environment and the irregular edge morphology. The Context feature branch is responsible for extracting high-level semantic features, while the Spatial feature branch is designed to capture fine-grained spatial details. After information fusion, the model obtained more comprehensive feature representation and segmentation ability. The effectiveness of the improvement was verified through ablation experiments and compared with 13 commonly used semantic segmentation models, demonstrating the advantages of the method in solid waste segmentation tasks, with an overall accuracy of over 94%, and a recall rate of 88.6%—much better than the best performing baselines. Finally, a spatial distribution map of solid waste over Jiaxing district, China was generated by the model inference, which assisted the environmental protection department in completing environmental management. The proposed method provides a feasible approach for the accurately monitoring of solid waste, so as to provide policy support for environmental protection.

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“…The cited studies use spatial data and remote-sensing approaches with GIS. Padubidri et al [21] propose a methodology for identifying illegal waste through aerial orthophotography using deep learning. Their results suggest a deficit in available information for training and testing and the need to train the model with a classifier using synthetic images.…”

Section: Introductionmentioning

confidence: 99%

Detection of Construction and Demolition Illegal Waste Using Photointerpretation of DEM Models of LiDAR Data

Sánchez-Fernández,

Arenas-García,

Gutiérrez Gallego

2023

Land

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Illegal waste is a global problem with negative impacts on human health and the environment. This article focuses on detection using remote sensing of sites of demolition and construction waste. We hypothesise that construction and demolition waste represent a human modification of terrain and, as a result, will be sensible to detection using visualisation models of terrain, specifically DEM (digital elevation model). To this effect, we start with a DEM of 0.25 m per pixel developed using data from the second iteration of the PNOA LiDAR project by the Spanish National Geographic Institute (IGN). We evaluate seven modelling tools of the Relief Visualisation Toolbox (RVT) for the visual detection of waste. The study area includes the city of Mérida (Extremadura, Spain). Our fieldwork identified 494 points of illegal waste in this area. These points were classified according to five categories in relation to land use, and we established a total of 14 areas with a surface area of 450 m by 450 m. Our results suggest that three of the seven models employed allow us to differentiate with clarity what is anthropic from the natural terrain and, in some scenarios, the location of construction and demolition waste. The LD model was the one with the best results, allowing an increase in the number of locations of illegal dumping of CDW in the study area.

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Accurate Detection of Illegal Dumping Sites Using High Resolution Aerial Photography and Deep Learning

Cited by 9 publications

References 17 publications

AerialWaste dataset for landfill discovery in aerial and satellite images

AerialWaste dataset for landfill discovery in aerial and satellite images

A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery

Detection of Construction and Demolition Illegal Waste Using Photointerpretation of DEM Models of LiDAR Data

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