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Intelligent Sensing Technologies, Algorithms, and Drone Applications for Environmental and Aerospace Challenges

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Dr. Justyna Tomaszewska

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Guest Editor

Faculty of Aviation, Polish Air Force University, Dywizjonu 303 nr 35, 08-521 Dęblin, Poland
Interests: aerospace systems; aviation safety; reliability engineering; aircraft maintenance; logistical support systems
Special Issues, Collections and Topics in MDPI journals

Dr. Paweł Gołda

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Faculty of Aviation, Polish Air Force University, 08-521 Dęblin, Poland
Interests: air traffic management

Special Issue Information

Dear Colleagues,

The integration of intelligent sensing technologies, advanced algorithms, and unmanned aerial systems (UAS) is driving a transformative shift in the environmental and aerospace domains. These innovations are facilitating high-resolution data acquisition, real-time analysis, and actionable insights to address critical challenges such as environmental conservation, climate monitoring, disaster response, and aerospace exploration.

We are pleased to announce a Special Issue focusing on "Intelligent Sensing Technologies, Algorithms, and Drone Applications for Environmental and Aerospace Challenges", which seeks to bring together groundbreaking research at the intersection of intelligent sensing, algorithm development, and drone technologies. Contributions are invited that explore the following:

Novel sensing technologies tailored for environmental and aerospace applications;
Machine learning and optimization algorithms for advanced sensor data processing;
Drone applications in environmental monitoring, resource management, and aerospace operations;
Autonomous navigation, sensor fusion, and real-time environmental data analysis;
Innovative use cases of drones in remote and challenging environments.

This Special Issue highlights interdisciplinary advancements, promoting innovative solutions to global environmental and aerospace challenges. By fostering collaboration across domains, we hope to pave the way for sustainable technological progress and enhanced situational awareness in these critical areas.

We warmly invite you to submit your original research articles and reviews to showcase the state of the art in this dynamic and rapidly evolving field.

Dr. Justyna Tomaszewska
Dr. Pawel Golda
Guest Editors

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27 pages, 13961 KiB

Open AccessArticle

An Approach for Detecting Mangrove Areas and Mapping Species Using Multispectral Drone Imagery and Deep Learning

by Xingyu Chen, Xiuyu Zhang, Changwei Zhuang, Xuejiao Dai, Lingling Kong, Zixia Xie and Xibang Hu

Sensors 2025, 25(8), 2540; https://doi.org/10.3390/s25082540 - 17 Apr 2025

Viewed by 220

Abstract

Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity and maintaining marine ecological balance. It is crucial to develop more efficient, intelligent, and accurate monitoring methods for mangroves to understand better and protect mangrove ecosystems. This study promotes a novel model, MangroveNet, for integrating multi-scale spectral and spatial information and detecting mangrove area. In addition, we also present an improved model, AttCloudNet+, to identify the distribution of mangrove species based on high-resolution multispectral drone images. These models incorporate spectral and spatial attention mechanisms and have been shown to effectively address the limitations of traditional methods, which have been prone to inaccuracy and low efficiency in mangrove species identification. In this study, we compare the results from MangroveNet with SegNet, UNet, and DeepUNet, etc. The findings demonstrate that the MangroveNet exhibits superior generalization learning capabilities and more accurate extraction outcomes than other deep learning models. The accuracy, F1_Score, mIoU, and precision of MangroveNet were 99.13%, 98.84%, 98.11%, and 99.14%, respectively. In terms of identifying mangrove species, the prediction results from AttCloudNet+ were compared with those obtained from traditional supervised and unsupervised classifications and various machine learning and deep learning methods. These include K-means clustering, ISODATA cluster analysis, Random Forest (RF), Support Vector Machines (SVM), and others. The comparison demonstrates that the mangrove species identification results obtained using AttCloudNet+ exhibit the most optimal performance in terms of the Kappa coefficient and the overall accuracy (OA) index, reaching 0.81 and 0.87, respectively. The two comparison results confirm the effectiveness of the two models developed in this study for identifying mangroves and their species. Overall, we provide an efficient solution based on deep learning with a dual attention mechanism in the acceptable real-time monitoring of mangroves and their species using high-resolution multispectral drone imagery. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies, Algorithms, and Drone Applications for Environmental and Aerospace Challenges)

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