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Coastal landscape classification using convolutional neural network and remote sensing data in Vietnam.

Tuan Linh Giang¹, Quang Thanh Bui², Thi Dieu Linh Nguyen²

¹VNU Institute of Vietnamese Studies and Development Sciences, Vietnam National University, Hanoi, 336 Nguyen Trai, 10000, Hanoi, Viet Nam; VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, 10000, Hanoi, Viet Nam.

Journal of Environmental Management

|February 26, 2023

Summary

This study introduces a convolutional neural network (CvNet) model for precise coastal landscape classification using remote sensing data. The CvNet model achieved 98% accuracy in identifying nine distinct coastal types in Vietnam.

Keywords:

Artificial intelligence Coast Landsat Machine learning Optical satellite imagery

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Area of Science:

Earth and Environmental Sciences
Geomorphology
Remote Sensing

Background:

Traditional methods for coastal landscape categorization are often qualitative and lack precision.
Advancements in remote sensing and GIS tools enable more accurate identification of coastal features using multi-source data.

Purpose of the Study:

To classify coastal landscapes in Vietnam using convolutional neural network (CvNet) models.
To leverage multi-source remote sensing data (ALOS, NOAA, Landsat) for improved coastal landscape identification.

Main Methods:

Utilized 900 coastal landscape samples from Vietnam for training and optimizing CvNet models.
Employed multi-temporal Landsat satellite images, ALOS, and NOAA data as input for the CvNet models.
Tested three CvNet models with different optimizers on 1150 cut-lines for classification.

Main Results:

Successfully identified nine distinct coastal landscapes: deltas, alluvial, sand dunes (mature and young), cliff, lagoon, tectonic, karst, and transitional.
Achieved high classification accuracies of approximately 98% with low loss function values.
Found heterogeneous distribution of most identified coastal landscapes along Vietnam's coast, excluding Dalmatian, karst, and delta types.

Conclusions:

The CvNet model demonstrates significant potential for accurate coastal landscape classification.
Further evaluation of natural components is recommended for comprehensive coastal analysis.
The CvNet model's adaptability suggests its utility for classifying diverse tropical coastal landscapes at national and global scales.