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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


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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

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Keywords

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

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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
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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
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