Towards sustainable coastal management: aerial imagery and deep learning for high-resolution Sargassum mapping

View article
Environmental Science

Main article text

 

Introduction

Theoretical framework

Semantic segmentation

Materials and Methods

Pix2pix settings

Dataset

Fβ-score metric

Resampling

Results and discussion

General performance of the pix2pix model

Sargassum mapping

Conclusions

Supplemental Information

Code for the Pix2Pix Neural Network.

https://cocodataset.org

https://www.cvlibs.net/datasets/kitti/

https://www.cityscapes-dataset.com/

https://www.dronelink.com/

https://sammo.icmyl.unam.mx/

https://www.opendronemap.org/webodm/

DOI: 10.7717/peerj.18192/supp-1

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Javier Arellano-Verdejo conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Hugo E. Lazcano-Hernandez conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The data is available at figshare: Arellano-Verdejo, Javier (2024). Aerial Segmented Sargassum Dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.25320148.v4

The code is available in the Supplemental File.

Funding

The authors received no funding for this work.

831 Visitors 796 Views 44 Downloads