Coastal surface change detection by aerial image segmentation using convolutional neural networks
The conservation of ecosystems in coastal areas must be considered within a sustainable framework with anthropogenic activities. It is relevant to quantify the impact generated by external agents, hence the objective of this work is to implement a method for monitoring the degradation of the beach s...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | México |
| Institución: | UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO |
| Repositorio: | PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI |
| Idioma: | español |
| OAI Identifier: | oai:repository.uaeh.edu.mx:article/9290 |
| Acceso en línea: | https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/9290 |
| Access Level: | acceso abierto |
| Palabra clave: | Convolutional neural networks Semantic segmentation Time series Protected coastal areas Redes neuronales convolucionales Segmentación semántica Series temporales Zonas costeras protegidas |
| Sumario: | The conservation of ecosystems in coastal areas must be considered within a sustainable framework with anthropogenic activities. It is relevant to quantify the impact generated by external agents, hence the objective of this work is to implement a method for monitoring the degradation of the beach surface and adjacent coastal vegetation. Captures of aerial images of protected coastal areas have been collected, obtained periodically by means of a drone vehicle. A dataset was integrated that includes all seasonal phases and distinguishes 5 classes for monitoring: Mangrove (mangle), creeping vegetation (vegetación rastrera), sand (arena), sea (mar) and hill-plain (cerro-planicie). For semantic segmentation different convolutional neural network (CNN) architectures were implemented and compared using transfer learning. The results have been robust in the classification, reaching an overall accuracy of 93.9% and between 89.9-95.8% in individual classes. In the Intersection over Union, IoU metric, the range was between 86.6-92.7%. In change detection, time series are used for class monitoring. This method has been applied to the case study of Ensenada Grande beach in the Parque Nacional Archipiélago Espíritu Santo. |
|---|