Coastal surface change detection by aerial image segmentation using convolutional neural networks
Abstract
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.
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