A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data
[EN] Although plastic pollution is one of the most noteworthy environmental issues nowadays, there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which is needed to prevent its negative effects and to plan mitigation actions. Unmanned Aerial Vehicles (UAVs) can...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Ajuntament de Barcelona |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/18484 |
| Acceso en línea: | https://www.mdpi.com/2072-4292/12/9/1515 https://hdl.handle.net/10612/18484 |
| Access Level: | acceso abierto |
| Palabra clave: | Cartografía Geodesia Ingeniería forestal Deep learning Mapping plastic Automatic detection AI Remote Sensing UAV Segmentatio |
| Sumario: | [EN] Although plastic pollution is one of the most noteworthy environmental issues nowadays, there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which is needed to prevent its negative effects and to plan mitigation actions. Unmanned Aerial Vehicles (UAVs) can provide suitable data for mapping floating plastic, but most of the methods require visual interpretation and manual labeling. The main goals of this paper are to determine the suitability of deep learning algorithms for automatic floating plastic extraction from UAV orthophotos, testing the possibility of differentiating plastic types, and exploring the relationship between spatial resolution and detectable plastic size, in order to define a methodology for UAV surveys to map floating plastic. Two study areas and three datasets were used to train and validate the models. An end-to-end semantic segmentation algorithm based on U-Net architecture using the ResUNet50 provided the highest accuracy to map different plastic materials (F1-score: Oriented Polystyrene (OPS): 0.86; Nylon: 0.88; Polyethylene terephthalate (PET): 0.92; plastic (in general): 0.78), showing its ability to identify plastic types. The classification accuracy decreased with the decrease in spatial resolution, performing best on 4 mm resolution images for all kinds of plastic. The model provided reliable estimates of the area and volume of the plastics, which is crucial information for a cleaning campaign. |
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