Drones for Flood Monitoring, Mapping and Detection: A Bibliometric Review

Iqbal, Umair; Riaz, Muhammad Zain Bin; Zhao, Jiahong; Barthélemy, Johan; Perez, Pascal

doi:10.3390/drones7010032

Cited by 29 publications

(16 citation statements)

References 79 publications

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“…The stereo camera [14] is used to analyze the depth or distance to get a full reading of the surroundings and the victims [1]. By mounting these optical technologies on the drone creates it very efficient to observe and work with the rescue operations [2]. The tracking system works by GPS location marking with faster communication systems to map the location in the database for better rescue and prompt response.…”

Section: Methodsmentioning

confidence: 99%

Drones in Human Aid and Disaster Relief using Thermal Imaging Technology

Thiyagarajan S,

Arunmozhiselvam B,

Akash G

et al. 2024

Int Res J Adv Engg Mgt

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The rapidly evolving landscape of medical imaging necessitates innovative approaches to enhance diagnostic accuracy, particularly in liver and tumor segmentation. Current methodologies often grapple with limitations such as the need for extensive labeled datasets and the challenge of generalizing across diverse pathological presentations. Addressing these constraints, this study introduces a novel deep learning model that amalgamates the prowess of U-Net architecture, Generative Adversarial Networks (GANs), and Monte Carlo Dropout techniques, each contributing uniquely to overcome the existing barriers. Predominantly, the traditional segmentation models are hindered by their dependency on large, annotated datasets, which are not only scarce but also labor-intensive to produce. Further, these models frequently struggle to maintain consistent performance across varying imaging conditions, a pivotal requirement in medical diagnostics. To surmount these challenges, our proposed model employs a fine-tuning methodology using a U-Net architecture pre-trained on extensive medical image datasets like NIH Chest X-ray or MIMIC-CXR. This strategy leverages the pre-existing knowledge within the network, significantly enhancing the model's ability to discern and adapt to the specific characteristics inherent in liver and tumor images & samples. In a parallel vein, the model harnesses the capabilities of GANs for data augmentation, generating synthetic yet realistic medical images. This innovative use of GANs addresses the issue of dataset variability, equipping the model to generalize more effectively across a spectrum of liver and tumor appearances, ultimately bolstering its robustness. This approach is particularly advantageous in handling cases with diverse lighting, angles, or pathological conditions, traditionally a stumbling block for segmentation models. Moreover, the incorporation of Monte Carlo Dropout for uncertainty estimation marks a significant stride in the realm of clinical applicability. This technique yields pixel-wise uncertainty maps, offering invaluable insights into the model's confidence levels in its predictions. Such transparency is crucial in clinical settings, where understanding the model's certainty can guide more informed and cautious decision-making, particularly in scenarios where the cost of erroneous segmentation is high for different scenarios. Empirical evaluation of this composite model on Kaggle datasets has demonstrated its superior performance, evidenced by improvements in precision (3.9%), accuracy (4.9%), recall (4.5%), AUC (8.5%), and a reduction in delay (3.5%) compared to existing methods. The culmination of these advancements not only enhances the efficacy of liver and tumor segmentation but also paves the way for broader applications in medical imaging diagnostics.

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Section: Methodsmentioning

confidence: 99%

Drones in Human Aid and Disaster Relief using Thermal Imaging Technology

Thiyagarajan S,

Arunmozhiselvam B,

Akash G

et al. 2024

Int Res J Adv Engg Mgt

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“…In contrast, remote sensing can provide direct, independent information on floods. Satellite imagery and aerial photography from planes and drones have been used to monitor past and real‐time environmental and hydrological changes (Iqbal et al., 2023; Manfreda et al., 2018; Yang et al., 2020). However, the main advantage of satellites is the large area of coverage, including remote regions.…”

Section: Introductionmentioning

confidence: 99%

SatVITS‐Flood: Satellite Vegetation Index Time Series Flood Detection Model for Hyperarid Regions

Burstein,

Grodek,

Enzel

et al. 2023

Water Resources Research

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We present the satellite vegetation index time series model for detecting historical floods in ungauged hyperarid regions (SatVITS‐Flood). SatVITS‐Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time‐series metrics: (a) trend change detection from the Breaks For Additive Season and Trend and (b) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing‐season integral anomaly (TFA‐GSIanom). The two metrics complement each other by capturing changes in perennial plant species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index, the modified soil‐adjusted vegetation index, and the normalized difference water index, acquired from MODIS, Landsat, and Advanced Very High‐Resolution Radiometer. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS‐Flood in three regions on different continents with 40‐year‐long, systematic, reliable gauge data. Our results indicate that SatVITS‐Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall = 0.69 and F1 = 0.68; p < 0.01), and the flood volume and duration with NSE of 0.79 (RMSE = 15.4 × 106 m3 event−1), and R2 of 0.69 (RMSE = 5.7 days), respectively. SatVITS‐Flood proved useful for detecting historical floods and may provide valuable long‐term hydrological information in poorly documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

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“…However, only coarse-grained river ice changes can be captured due to the long image-shooting interval. The second is unmanned aerial vehicle imaging (UAV imagery) [7][8][9], which has the advantage of its relatively low cost and ability to capture hourly data for river ice changes occurring anywhere. However, it is challenging to capture the long-term data for river ice changes.…”

Section: Introductionmentioning

confidence: 99%

River Ice Regime Recognition Based on Deep Learning: Ice Concentration, Area, and Velocity

Yang,

Zong,

Zhu

et al. 2023

Water

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The real-time derivation of the concentration, area, and velocity of river surface ice based on camera imagery is essential for predicting the potential risks related to ice blockages in water routes. The key lies in the continuous tracking and velocity measuring of river ice, and reliable ice motion detection is a prerequisite for the dynamic perception of tracking targets. Previous studies did not utilize motion tracking for measuring ice velocity, and particle image velocimetry and feature point matching were used. This study aimed to use deep learning methods to address the challenging problems of deriving the ice concentration, area, and velocity based on camera imagery, and the focus was on measuring the ice velocity and drawing trajectories using the particle video tracking algorithm. We built a dataset named IPC_RI_IDS and collected information during the ice cover break-up process in the Nenjiang River (China). Our suggested approach was divided into four steps: (1) image preprocessing, where the camera image was calibrated to real-world coordinates; (2) determining the ice and water pixels in the camera image using the lightweight semantic segmentation network and then calculating the ice concentration and area; (3) enhancing and optimizing motion detection using the semantic segmentation results; and (4) adapting the particle video tracking algorithm to measure ice velocity using the proposed tracking points generation strategy. Finally, we analyzed the surface ice data in the study area and attempted to predict the stage of the ice break-up process to provide support for the real-time short-term forecasts of ice floods.

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Drones for Flood Monitoring, Mapping and Detection: A Bibliometric Review

Cited by 29 publications

References 79 publications

Drones in Human Aid and Disaster Relief using Thermal Imaging Technology

Drones in Human Aid and Disaster Relief using Thermal Imaging Technology

SatVITS‐Flood: Satellite Vegetation Index Time Series Flood Detection Model for Hyperarid Regions

River Ice Regime Recognition Based on Deep Learning: Ice Concentration, Area, and Velocity

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