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Sensors
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Sensing in Harsh Environments: Power, Communication and Material Challenges
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Sensing in Harsh Environments: Power, Communication and Material Challenges

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 1775

Special Issue Editor

Prof. Dr. Chong Li

E-Mail Website
Guest Editor
James Watt School of Engineering, University of Glasgow, Glasgow, UK
Interests: sensors; semiconductor devices; wireless power transfer and energy harvesting

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the key challenges and recent advancements in sensing technologies designed for harsh environments, such as extreme temperatures, high pressures, corrosive conditions, and remote locations. Sensing systems play a vital role in applications like environmental monitoring, space exploration, industrial automation, and defence operations. However, these environments demand innovative solutions in three critical areas: power management, communication, and material resilience.

Papers in this issue will explore sustainable power solutions, including energy harvesting techniques like solar, thermal, wireless, and kinetic energy, aimed at enabling long-term sensor operation in isolated regions. Another major focus will be communication technologies, such as low-power wide-area networks (LPWAN), satellite links, IoTs, and mesh networks, which allow reliable data transmission in areas with limited infrastructure.

The issue will also examine material innovations, including corrosion-resistant alloys, self-healing polymers, and advanced encapsulation techniques to enhance sensor durability in extreme conditions. Contributions are expected to present breakthroughs in material science, communication protocols, and energy systems that can overcome the limitations of current sensing technologies.

This Special Issue aims to bring together interdisciplinary research that will advance the development of resilient, efficient sensing systems for extreme environments, benefiting numerous sectors reliant on robust, remote data collection.

Prof. Dr. Chong Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensors
  • power supply
  • communications
  • materials
  • harsh environments

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

14 pages, 6691 KiB  
Article
Remote Sensing Extraction of Damaged Buildings in the Shigatse Earthquake, 2025: A Hybrid YOLO-E and SAM2 Approach
by Zhimin Wu, Chenyao Qu, Wei Wang, Zelang Miao and Huihui Feng
Sensors 2025, 25(14), 4375; https://doi.org/10.3390/s25144375 - 12 Jul 2025
Viewed by 257
Abstract
In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment [...] Read more.
In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone. Full article
(This article belongs to the Special Issue Sensing in Harsh Environments: Power, Communication and Material Challenges)
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22 pages, 3865 KiB  
Article
Enhanced U-Net++ for Improved Semantic Segmentation in Landslide Detection
by Meng Tang, Yuelin He, Muhammed Aslam, Edore Akpokodje and Syeda Fizzah Jilani
Sensors 2025, 25(9), 2670; https://doi.org/10.3390/s25092670 - 23 Apr 2025
Viewed by 1178
Abstract
Landslide detection and segmentation are critical for disaster risk assessment and management. However, achieving accurate segmentation remains challenging due to the complex nature of landslide terrains and the limited availability of high-quality labeled datasets. This paper proposes an enhanced U-Net++ model for semantic [...] Read more.
Landslide detection and segmentation are critical for disaster risk assessment and management. However, achieving accurate segmentation remains challenging due to the complex nature of landslide terrains and the limited availability of high-quality labeled datasets. This paper proposes an enhanced U-Net++ model for semantic segmentation of landslides in the Wenchuan region using the CAS Landslide Dataset. The proposed model integrates multi-scale feature extraction and attention mechanisms to enhance segmentation accuracy and robustness. The experimental results demonstrate that ASK-UNet++ outperforms traditional methods, achieving a mean intersection over union (mIoU) of 97.53%, a Dice coefficient of 98.27%, and an overall accuracy of 96.04%. These findings highlight the potential of the proposed approach for improving landslide monitoring and disaster response strategies. Full article
(This article belongs to the Special Issue Sensing in Harsh Environments: Power, Communication and Material Challenges)
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