A Deep Learning-Based Approach for Mangrove Monitoring

TL;DR

This paper evaluates deep learning models for mangrove segmentation using a new open-source satellite image dataset, demonstrating the superior performance of the Mamba model across various metrics.

Contribution

Introduces MagSet-2, a comprehensive mangrove dataset, and benchmarks multiple deep learning architectures, highlighting the effectiveness of the Mamba model.

Findings

Mamba model outperforms other architectures in all metrics.

MagSet-2 dataset includes global mangrove annotations and Sentinel-2 images.

Deep learning models show promise for ecological monitoring.

Abstract

Mangroves are dynamic coastal ecosystems that are crucial to environmental health, economic stability, and climate resilience. The monitoring and preservation of mangroves are of global importance, with remote sensing technologies playing a pivotal role in these efforts. The integration of cutting-edge artificial intelligence with satellite data opens new avenues for ecological monitoring, potentially revolutionizing conservation strategies at a time when the protection of natural resources is more crucial than ever. The objective of this work is to provide a comprehensive evaluation of recent deep-learning models on the task of mangrove segmentation. We first introduce and make available a novel open-source dataset, MagSet-2, incorporating mangrove annotations from the Global Mangrove Watch and satellite images from Sentinel-2, from mangrove positions all over the world. We then benchmark three architectural groups, namely convolutional, transformer, and mamba models, using the created dataset. The experimental outcomes further validate the deep learning community's interest in the Mamba model, which surpasses other architectures in all metrics.