Using Unmanned Aerial Vehicle Remote Sensing and a Monitoring Information System to Enhance the Management of Unauthorized Structures

He, Yuanrong; Ma, Weiwei; Ma, Zelong; Fu, Wenjie; Chen, Chihcheng; Yang, Cheng–Fu

doi:10.3390/app9224954

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…By inspecting target areas at a very fine scale, UAV platforms are foreseen as potential tools to detect and counteract the illegal dumping of solid/liquid waste in the environment [158,159], for actively monitoring the operations in existing landfills [160], and for general prevention of soil contamination [161]. Preventing the diffusion of unauthorized constructions or their illegal demolition is another important application field for UAV-based monitoring systems [162]. From the surveyed literature, it emerges that these monitoring problems can be addressed using statistical change detection approaches operating on a temporal series of images or video sequences.…”

Section: Uav For Land Monitoringmentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

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Section: Uav For Land Monitoringmentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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“…Moreover, drones are making high-altitude inspections safer, obviating the need for risk-prone traditional methods. By enabling precise surveying, drones are reducing both the duration and costs associated with construction processes [7][8][9]. Semantic segmentation, an advanced computer vision technique, enhances the efficacy of image analysis, especially for images captured by drones.…”

Section: Introductionmentioning

confidence: 99%

Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation

Youm,

2023

Applied Sciences

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Images captured by drones are increasingly used in various fields, including geographic information management. This study evaluates a procedure that incorporates active learning semantic segmentation for verifying the building registration ledger. Several semantic segmentation techniques were evaluated to extract building information, with ResNet identified as the most effective method for accurately recognizing building roofs. Using active learning, the training data were refined by removing instances with low similarity, leading to improved network performance of the model. The procedure was demonstrated to identify discrepancies between the building information system and the inferred label images, as well as to detect labeling errors on a training dataset. Through this research, the geographic information system dataset is enhanced with minimal human oversight, offering significant potential for urban planning and building detection advancements.

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“…Building change detection (BCD) is the procedure of extracting the dynamic changes in buildings in the same area based on remote sensing images acquired at different times. This process is of great importance to many fields, such as land resource utilization [1], urban expansion [2] and illegal construction management [3,4]. As more and more highquality sensors are launched (e.g., IKONOS, QuickBird, GeoEye-1), many remote sensing images become accessible, which extensively drives the development of intelligent change detection technology.…”

Section: Introductionmentioning

confidence: 99%

MAEANet: Multiscale Attention and Edge-Aware Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images

Yang

Huang

et al. 2022

Remote Sensing

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In recent years, using deep learning for large area building change detection has proven to be very efficient. However, the current methods for pixel-wise building change detection still have some limitations, such as a lack of robustness to false-positive changes and confusion about the boundary of dense buildings. To address these problems, a novel deep learning method called multiscale attention and edge-aware Siamese network (MAEANet) is proposed. The principal idea is to integrate both multiscale discriminative and edge structure information to improve the quality of prediction results. To effectively extract multiscale discriminative features, we design a contour channel attention module (CCAM) that highlights the edge of the changed region and combine it with the classical convolutional block attention module (CBAM) to construct multiscale attention (MA) module, which mainly contains channel, spatial and contour attention mechanisms. Meanwhile, to consider the structure information of buildings, we introduce the edge-aware (EA) module, which combines discriminative features with edge structure features to alleviate edge confusion in dense buildings. We conducted the experiments using LEVIR-CD and BCDD datasets. The proposed MA and EA modules can improve the F1-Score of the basic architecture by 1.13% on the LEVIR CD and by 1.39% on the BCDD with an accepted computation overhead. The experimental results demonstrate that the proposed MAEANet is effective and outperforms other state-of-the-art methods concerning metrics and visualization.

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Using Unmanned Aerial Vehicle Remote Sensing and a Monitoring Information System to Enhance the Management of Unauthorized Structures

Cited by 8 publications

References 22 publications

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation

MAEANet: Multiscale Attention and Edge-Aware Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images

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