Edge Detection Research Articles

Segmentation Method of Concrete Small Cracks Based on UAV Images

Introduction:: Cracks are one of the major problems in modern concrete buildings, especially in locations that are difficult to map manually, such as bridges and high-rise buildings. Accurate analysis of unmanned aerial vehicle (UAV) images has become the key to determining whether a building needs maintenance. Methods:: Traditional image processing methods are easily interfered by high-frequency background. Neural network methods need fine datasets, which increase labor costs. Therefore, this paper proposes a segmentation algorithm based on UNet3+ network. After obtaining the UAV image, the rough location of the crack can be obtained by only rough labeling. And then, the sample balance can be carried out by clipping the target area. The UNet3+ network is used to train the processed datasets and extract the region of interest to ignore the non-target texture. Finally, the region of interest is further segmented by color clustering and edge detection methods. Results:: The proposed method can detect the cracks accurately. In all test images, the relative errors are less than 13%. Especially in test images whose width is less than 0.2mm, the maximum absolute error is only 0.0237mm, which is completely acceptable in actual production. The proposed method has higher practicability in the detection of concrete crack images taken by UAV. The results show that the proposed method outperforms the cutting-edge method published in the journal "Sensor", when the background is complex. Conclusion:: The proposed method can segment and detect cracks effectively, which can remove the high-frequency interference region from the images.

Scale Mutualized Perception for Vessel Border Detection in Intravascular Ultrasound Images.

Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent interference. Early methods usually rely on the hand-crafted features, thus not robust to this interference. The existing deep learning methods are also ineffective to solve this challenge, because these methods aggregate multi-scale features in the top-down way. This aggregation may bring in interference from the non-adjacent scale. Besides, they only combine the features in all scales, and thus may weaken their complementary information. We propose the scale mutualized perception to solve this challenge by considering the adjacent scales mutually to preserve their complementary information. First, the adjacent small scales contain certain semantics to locate different vessel tissues. Then, they can also perceive the global context to assist the representation of the local context in the adjacent large scale, and vice versa. It helps to distinguish the objects with similar local features. Second, the adjacent large scales provide detailed information to refine the vessel boundaries. The experiments show the effectiveness of our method in 153 IVUS sequences, and its superiority to ten state-of-the-art methods.

A Systematic Literature Review: Performance Comparison of Edge Detection Operators in Medical Images

Medical images play a crucial role in the diagnosis of diseases. To make the diagnosis more accurate, the image should usually be enhanced first using image processing methods such as segmentation and edge detection stages. However, the complexity and noise that may arise in these images pose challenges in edge detection. Therefore, to portray the characteristics of edge detection operators, this research presents a systematic literature review of the performance of various edge detection operators in medical images, focusing on literature published between 2019 and 2023. After the selection process, 41 papers out of the initial 112 collected papers were chosen for further review. The study evaluates edge detection operators e.g., Canny, Sobel, Prewitt, Roberts, and Laplacian of Gaussian (LOG) on medical images such as X-rays, MRI, CT scans, ultrasound, Pap smears, and others. In the analysis, the accuracy, computational time, and response to noise of each operator are compared. The results indicate that despite longer computational times, Canny emerges as the most accurate operator, especially in Pap smear and CT scan images. The LOG operator offers high accuracy in MRI images with more efficient computational time. Evaluation of operator reliability against noise confirms the superiority of Canny. Furthermore, the future potential of edge detection in medical services is also reviewed. For instance, Canny, known for accurate and noise-resistant edges, enhances detection in complex CT-Scan and X-ray images. Meanwhile, LOG, handling artifacts with lower computational time, improves edge clarity in medical images. Potential applications include enhanced diagnosis, efficient patient monitoring, and improved image clarity in future medical services.

Integrating OpenCV and Pandas for Enhanced Image Filtering and Color Detection

Image filtering and color detection in OpenCV is performed using functions like cv2.filter2D() for filtering, and cv2.cvtColor() for color space conversion and detection. These functions can be combined with other image processing techniques such as thresholding, erosion and dilation, and edge detection to perform more advanced tasks. The process typically starts with loading an image into Python using OpenCV's imread() function. From there, the image may be converted to a different color space using cvtColor() to make it easier to detect certain colors or features. Various image filters can then be applied to enhance the image and reduce noise or other unwanted features. This can include techniques such as blurring, sharpening, or edge detection, depending on the desired outcome. Once the image has been filtered, color detection can be performed using thresholding, which involves setting a threshold value and creating a binary image based on whether each pixel falls above or below the threshold. This can help to identify specific colors or features in the picture. Pandas are particularly useful for working with image data as it provides a flexible and efficient data structure called a DataFrame. This makes it easy to perform data manipulation tasks such as selecting, filtering, and grouping data, which can be useful for image processing tasks. Meanwhile, OpenCV provides a comprehensive set of tools for image filtering and color detection. These tools include a variety of filters, such as Gaussian, Median, and Bilateral filters, as well as thresholding techniques, such as binary and adaptive thresholding, which can be used to detect specific colors or features in an image. Keywords:- Image Processing, OpenCV, Color Detection, Image Filtering, cv2.filter2D, cv2.cvtColor, Thresholding, Erosion and Dilation, Edge Detection, Pandas DataFrame, Gaussian Filter, Median Filter, Bilateral Filter, Adaptive Thresholding, Feature Detection..

Design of A Cataract Detection System based on The Convolutional Neural Network

Cataract, a condition characterized by clouding of the eye's lens, leads to decreased vision and potentially blindness. In Indonesia, it is the predominant cause of blindness, accounting for 81.2% of cases. Given the rising life expectancy, the incidence of degenerative diseases like cataracts is expected to increase. This research aims to develop a cataract detection system capable of classifying eye images as either indicative of cataracts or normal. Utilizing Convolutional Neural Networks (CNN) and RGB-based image processing—including edge detection techniques such as Canny and Prewitt—the system identifies eye contours. This facilitates image segmentation to ascertain the eye's condition. Therefore, image collection and processing models play a crucial role in this study. The research findings indicate that the system functions effectively, with a 98% success rate in accurately processing normal eye images through the CNN model without detecting cataracts. When tested using grayscale imaging, cataract-afflicted eyes—characterized by red spots in the images—were also successfully identified by the CNN model. These test results demonstrate that the designed cataract detection system can accurately classify images into normal or cataract-afflicted eyes with high precision. This system shows promise for use in early cataract detection, potentially helping to reduce the prevalence of cataract-related blindness in Indonesia.

Multi-Scale Earthquake Damaged Building Feature Set

Earthquake disasters are marked by their unpredictability and potential for extreme destructiveness. Accurate information on building damage, captured in post-earthquake remote sensing images, is critical for an effective post-disaster emergency response. The foundational features within these images are essential for the accurate extraction of building damage data following seismic events. Presently, the availability of publicly accessible datasets tailored specifically to earthquake-damaged buildings is limited, and existing collections of post-earthquake building damage characteristics are insufficient. To address this gap and foster research advancement in this domain, this paper introduces a new, large-scale, publicly available dataset named the Major Earthquake Damage Building Feature Set (MEDBFS). This dataset comprises image data sourced from five significant global earthquakes and captured by various optical remote sensing satellites, featuring diverse scale characteristics and multiple spatial resolutions. It includes over 7000 images of buildings pre- and post-disaster, each subjected to stringent quality control and expert validation. The images are categorized into three primary groups: intact/slightly damaged, severely damaged, and completely collapsed. This paper develops a comprehensive feature set encompassing five dimensions: spectral, texture, edge detection, building index, and temporal sequencing, resulting in 16 distinct classes of feature images. This dataset is poised to significantly enhance the capabilities for data-driven identification and analysis of earthquake-induced building damage, thereby supporting the advancement of scientific and technological efforts for emergency earthquake response.

Deep learning model for detection of hotspots using infrared thermographic images of electrical installations

Hotspots in electrical power equipment or installations are a major issue whenever it occurs within the power system. Factors responsible for this phenomenon are many, sometimes inter-related and other times they are isolated. Electrical hotspots caused by poor connections are common. Deep learning models have become popular for diagnosing anomalies in physical and biological systems, by the instrumentality of feature extraction of images in convolutional neural networks. In this work, a VGG-16 deep neural network model is applied for identifying electrical hotspots by means of transfer learning. This model was achieved by first augmenting the acquired infrared thermographic images, using the pre-trained ImageNet weights of the VGG-16 algorithm with additional global average pooling in place of conventional fully connected layers and a softmax layer at the output. With the categorical cross-entropy loss function, the model was implemented using the Adam optimizer at learning rate of 0.0001 as well as some variants of the Adam optimization algorithm. On evaluation, with a test IRT image dataset, and a comparison with similar works, the research showed that a better accuracy of 99.98% in identification of electrical hotspots was achieved. The model shows good score in performance metrics like accuracy, precision, recall, and F1-score. The obtained results proved the potential of deep learning using computer vision parameters for infrared thermographic identification of electrical hotspots in power system installations. Also, there is need for careful selection of the IR sensor’s thermal range during image acquisition, and suitable choice of color palette would make for easy hotspot isolation, reduce the pixel to pixel temperature differential across any of the images, and easily highlight the critical region of interest with high pixel values. However, it makes edge detection difficult for human visual perception which computer vision-based deep learning model could overcome.

Pengolahan Citra Digital EKG Rumah Sakit Tk.IV Samarinda

In the hospital itself, especially in the MCU Poly Radiology Installation, it is a unit that performs services such as Medical Check Up, making health certificates, and carrying out routine checks at an institution. ECG examination is one of the services most frequently performed routinely by patients. Examinations carried out include medical check-ups, monitoring of long-term therapy, patient to assessment before surgery, individual screening for high-risk jobs, examinations before participating in sporting events, and overall examinations as a condition of employment. This study carried out the process of withdrawing information or object descriptions or identifying objects contained in images by compressing or reducing image data. By using EKG data from TK.IV Samarinda Hospital by carrying out processing stages such as converting analog data to digital, cropping, and applying filters to EKG images. After the filter is applied to the image, it is found that after 5 types of image processing (RGB Image, Grayscale Image, Binary Image, Thresholding Image, and Edge Detection Image) on the results of the ECG signal it can be concluded that the processed signal retains the features of the existing signal and information. . Of the five types of processing methods, the clearest result is conversion to Edge Detection imagery.

High-robust compressive multimode fiber imaging based on observation vector feedback correction

Multimode fiber (MMF) imaging technology has promising applications in endoscopic systems, and the compressive multimode fiber imaging (CMMFI) scheme has garnered considerable attention due to its simple optical setup and fast imaging speed. However, the imaging quality of this scheme is significantly impacted by detector noise and environmental interference. This paper introduces a sampling method and reconstruction algorithm that utilizes edge detection and region growing techniques for feedback-based correction of the observation vector data acquired from the detector, aiming to reduce the interference of noise in the system. Simulation and experimental tests were conducted to validate this method, and the results underwent statistical analysis. The findings demonstrate that the proposed method improves imaging quality of the low-cost CMMFI without the need for increasing the number of samples. Moreover, it exhibits robustness against noise, further enhancing the reliability of the imaging process. Overall, this research represents a valuable contribution to the advancement of robust endoscopy based on multimode fibers.

Advancing potential field data analysis: the Modified Horizontal Gradient Amplitude method (MHGA)

Enhancing the detection accuracy of the edges of the geological features within the subsurface remains a significant objective in geophysical data interpretation. Despite numerous advancements, approaches stemming from the directional gradients of gravity and magnetic fields still grapple with challenges such as low-resolution outcomes and susceptibility to noise contamination. In this study, we introduce a novel filtering framework based on the total horizontal gradient and its derivatives, designed to yield more precise and coherent edges free from false boundaries or disruptive artifacts. Validation using synthetic Bishop complex magnetic and gravity datasets, alongside Tuangiao aeromagnetic data from Vietnam, substantiates the robustness and applicability of our modified approach. Furthermore, recognizing the inherent susceptibility of edge detection filters to noise contamination resulting from directional derivatives, we employ the recently developed modified non-local means (MNLM) algorithm to alleviate noise effects prior to the analysis of noisy synthetic and real datasets. Our findings confirm the efficacy of the proposed method in reducing false artifacts and identifying edges with heightened precision, positioning MHGA as a valuable alternative for processing potential field data.

Метод виділення контурів для вирішення завдань моніторингу транспортної інфраструктури

Introduction. Technologies for monitoring transport infrastructure have been rapidly evolving in recent years, absorbing innovations and the latest developments. The main direction of development and use for this technology has been the implementation of continuous monitoring and control of different aspects of transport infrastructure to ensure its safety and allow efficient and timely management. Computer vision has been playing the main role in the evolution of these technologies and has made unmanned aerial vehicles (UAVs) the most cost-efficient remote monitoring tool. The purpose of the paper. Among the main tasks in the field are monitoring traffic and the conditions of road surfaces and markings. Fast and accurate monitoring systems enable quick responses and minimize negative consequences for citizens. Despite the active development of computer vision algorithms, there is no universal algorithm that suits all scenarios. Algorithms depend on the task, conditions, and even UAV trajectory; even a slight change in the visual scene can cause suboptimal results. Lately, significant progress has been made in the development of edge detection algorithms. However, they do not consider the specifics of the task of monitoring road markings. The algorithm should consider the characteristics of the objects of interest – their geometric and color features, and the presence of many other objects in the images. The goal of this paper is to present method crafted specifically for the task of monitoring transport infrastructure. Methods. Computer vision, threshold filtering, Sobel operator, noise removal, probabilistic Hough transform, histograms. Results. The main features of the task of monitoring transport infrastructure using visual data obtained from surveillance cameras or unmanned aerial vehicles have been analyzed. Developed an algorithm for boundary extraction of point clusters using histograms. A method for edge detection in images has been developed, which addresses the shortcomings of known methods and is specifically enhanced for transport infrastructure monitoring. The method leverages the narrow specialization of the task to improve the obtained results. The foundation of the method is based on the features of the HSL color model, filtering in the saturation and lightness channels using gradients obtained from the Sobel operator, segment detection based on the probabilistic Hough transform, and a developed algorithm for boundary extraction of point clusters using histograms. Conclusion. The proposed method can be used in automated and semi-automated decision-making systems, UAV design bureaus, UAV manufacturing enterprises, and information-analytical centers to develop unmanned aviation systems and aerial monitoring technologies to enhance human safety and the economic development of the state. The use of automatic remote monitoring data processing methods allows for faster acquisition of necessary results and improves the efficiency of using geospatial data. Keywords: Computer vision, object detection, edge detection, image filtering, transportation infrastructure, information technology, monitoring.

A Novel Method of Magnetic Sources Edge Detection Based on Gradient Tensor

The edge detection method based on the magnetic gradient tensor data plays an important role in magnetic exploration because it is free from geomagnetic interference and contains more abundant information. This paper proposes a new anomaly edge detection method using the magnetic gradient tensor components. The model is established to compare with other methods, such as directional total horizontal derivative (THDz), analytical signal (AS), tilt angle, theta map, and so on, under conditions of vertical magnetization, oblique magnetization, and noise interference. Through the study of the anomaly distribution of the rectangular model, it is observed that the edge detection method proposed in this paper is nearly impervious to noise interference, exhibits strong anti-interference capabilities, delivers a high-quality boundary identification effect, and provides greater accuracy in anomaly edges with minimal error. When multiple anomalous bodies are present, the edge detection results are less susceptible to interference from each other, resulting in higher resolution. The efficiency of the algorithm is demonstrated by real magnetic data from some study areas in Jiangxi Province, China. The experimental results show that the proposed method is more precise and accurate than the total horizontal derivative, analytical signal, tilt angle, and theta map methods.

Research on an Edge Detection Method Applied to Fire Localization on Storage Racks in a Warehouse

When a fire occurs on storage racks in a warehouse, it is not advisable to find the location of the fire point accurately, because there are a large number of goods on the storage rack, and many interference factors such as light will disturb the precise location of the fire. In response to the above problems, and thanks to the high-speed growth of deep learning technology, we propose an edge detection method and apply it in fire locations successfully. We adopt VGG-16 as our backbone and introduce an attention module to suppress background information and eliminate interference. We test the proposed method on our collected dataset, and the results show that our proposed model can extract the shelf edges more completely and locate the fire point accurately. In terms of detection speed, our method can achieve 0.188 s per image, which meets the requirements of real-time detection. Our approach lays a good foundation for the precise extinguishing of fire that occurs on storage racks.

SFMnet: Edge detection of HABs based on spatial feature mapping encoder-decoder network

Achieving high-accuracy real-time recognition of marine red tide algae images is crucial in implementing red tide algae early warning systems. However, due to uneven lighting and the deposition of unknown suspended particles in seawater, real-time collected harmful algal blooms (HABs) microscopic images in marine engineering suffer from poor clarity and blurred cell contours, leading to lower accuracy of current edge detection models for microscopic images. Firstly, to enhance the quality of degraded images in the original HABs dataset, we design an image enhancement algorithm based on underwater image restoration models, which involves precise depth estimation of seawater scenes and fusion of transmission maps. This algorithm effectively eliminates chromatic aberration and enhances image edge details and contrast. Secondly, we propose a deep-learning model based on the VGG16 network. This model integrates multi-scale features through the Spatial Feature Mapping module to improve edge detection accuracy. Experimental results using real-time HABs microscopic image data show that the proposed enhancement method achieves the best results regarding contrast, image enhancement metrics, and clarity gradient. Moreover, the edge detection method achieves an ODS of 0.636 and an OIS of 0.701. The results demonstrate that performing edge detection after the image enhancement preprocess significantly improves detection accuracy.

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Objective: An essential aspect of computer vision is content-based image retrieval (CBIR), which enables users to search for images based on their visual content instead of created annotations. Advances in technology have resulted in a significant rise in the complexity of multimedia content and the emergence of new research fields centered on similar multimedia material retrieval. The efficacy of retrieval is impacted by the limits of the present CBIR systems, which result from overlooked algorithms and computing restrictions. Methods: This research introduces a novel approach employing the Siamese Edge Attention Layered Convonet (SEAL Convonet) for Image Retrieval. We utilize the CBIR image dataset through Gaussian smoothing to enhance image quality for data preprocessing and the Canny Edge Detector (CED) for edge detection, following pre-processing. The Histogram of Oriented Gradients (HOG) is used for feature extraction to extract complex textures and patterns from the images. Findings: This approach is implemented and tested through simulations as well as the results indicate a substantial positive deviation in the performance and retrieval of the images compared to existing methods. The performance metrics are accuracy (97 %), precision (94 %), recall (91 %), F1-Score (97 %), False Positive Rate (FNR) (0.0013), Matthew's correlation coefficient (MCC) (0.85), and False Negative Rate (FPR) (0.0036) show the measurements of this proposed model. Application: The state of the art in this work is researching the influence of optimizers on the accuracy process, as indicated by the findings. Keywords: CBIR, Gaussian smoothing, Canny Edge Detector, Histogram of oriented gradients (HOG), Siamese Edge Attention Layered convonet (SEAL Convonet), Database

Measurement of Si pixel sensor alignment for the ALICE ITS detector

Abstract A Large Ion Collider Experiment (ALICE) experiment is one of the four experiments at the Large Hadron Collider (LHC) designed to investigate the status of matter under very high energy densities produced during heavy-ion collisions. The ALICE inner tracking system (ITS) consists of seven concentric cylindrical layers of monolithic silicon pixel sensors known as ALICE pixel detector (ALPIDE). The sensors are used to reconstruct the paths of charged particles generated in the collisions. The sensor alignment of the detector must be adjusted to a high precision standard. The adjustment objective is to obtain a detector that can undertake high-resolution measurements. This paper introduces a method for measuring the reference markers utilized in sensor alignment determination. Markers engraved at the chip corners have been detected using the Hough transform, Canny edge detection, and template matching techniques. The distances between two markers are measured to determine the accuracy of the pixel sensor alignment before and after assembly. The proposed methods exhibit an accuracy exceeding 99% and demonstrate high speed analysis. The average processing times for detecting the circle and cross markers are 105.9 ms/image and 113.8 ms/image, respectively. The sensor alignment of the detector must be adjusted to a high precision standard. However, recent studies have shown deviations of up to 5 μ m above the desired value in the measured sensor position. Such deviations do not represent a major issue, nevertheless it is important to measure them in order to speed-up and make more accurate the recursive track-based alignment procedure used to reconstruct the position of each pixel sensor in the tracking detector. The proposed method offers a promising solution to deliver precise and rapid measurements for a large number of examined objects.

Image interpolation with spiking neural network based pixel similarity

Image interpolation is an important topic in the field of image processing. It is defined as the process of transforming low-resolution images into high-resolution ones using image processing methods. Recent studies on interpolation have shown that researchers are focusing on successful interpolation techniques that preserve edge information. Therefore, the edge detection phase plays a vital role in interpolation studies. However, these approaches typically rely on gradient-based linear computations for edge detection. On the other hand, non-linear structures that effectively simulate the human visual system have gained attention. In this study, a non-linear method was developed to detect edge information using a pixel similarity approach. Pixel similarity-based edge detection approach offers both lower computational complexity and more successful interpolation results compared to gradient-based approaches. 1D cubic interpolation was applied to the pixels identified as edges based on pixel similarity, while bicubic interpolation was applied to the remaining pixels. The algorithm was tested on 12 commonly used images and compared with various interpolation techniques. The results were evaluated using metrics such as SSIM and PSNR, as well as visual assessment. The experimental findings clearly demonstrated that the proposed method outperformed other approaches. Additionally, the method offers significant advantages, such as not requiring any parameters and having competitive computational cost.

A New Class of Edge Filter Based on a Cross-correlation-like Equation Derived from Fractional Calculus Principles

In this paper, we propose a new sliding window edge-oriented filter that computes the output pixels using a cross-correlation-like equation derived from the principles of fractional calculus (FC); thus, we call it the “fractional cross-correlation filter” (FCCF). We assessed the performance of this filter utilizing exclusively edge-preservation-oriented metrics such as the gradient conduction mean square error (GCMSE), the edge-based structural similarity (EBSSIM), and the multi-scale structural similarity (MS-SSIM); we conducted a statistical assessment of the performance of the filter based on those metrics by using the Berkeley segmentation dataset benchmark as a test corpus. Experimental data reveal that our approach achieves higher performance compared to conventional edge filters for all the metrics considered in this study. This is supported by the statistical analysis we carried out; specifically, the FCCF demonstrates a consistent enhancement in edge detection. We also conducted additional experiments for determining the main filter parameters, which we found to be optimal for a broad spectrum of images. The results underscore the FCCF’s potential to make significant contributions to the advancement of image processing techniques since many practical applications such as medical imaging, image enhancement, and computer vision rely heavily on edge detection filters.

Transcribing Handwritten Medical Prescription using Convolutional Neural Network AlexNet Architecture and Canny Edge Detection

Misinterpreted medical prescriptions had led to casualties due to the illegible cursive handwriting of medical practitioners. Many studies focused on this problem. However, the accuracy was unsatisfactory and needed improvement. The study evaluated the performance of the Canny edge detection with other preprocessing methods, including RGB to Grayscale Conversion, Binarization, and Inversion, which was used to process the images of cursive handwritten medical prescriptions using Alexnet Convolutional Recurrent Neural Network (ACoRNN). The CRNN model developed by previous researchers was used as the basis for comparison, and the researchers created a faster and more accurate model. The best combination of preprocessing methods for ACoRNN was with RGB to Grayscale Conversion, Binarization, Canny edge detection, and Inversion. The researchers’ model had faster preprocessing and testing time and achieved 90.76% average accuracy through five trials.

Climate-induced vulnerability and conservation strategies for coastal heritage sites in the southern Mediterranean Sea using integrated remote sensing and flood modeling

Coastal heritage sites along the southern Mediterranean Sea confront immediate and severe threats from climate change, intensified by rising sea levels and ongoing shoreline erosion. This study presents a comprehensive approach to monitor and safeguard these significant locations, addressing the challenges posed by climate change. Leveraging high-resolution satellite data sources such as Pleiades imagery and Landsat data, shoreline detection was optimized using the Lee Filter and Canny edge detection techniques. Flood modeling and sea-level rise projections were incorporated to comprehensively assess dynamic coastal landscapes. Advanced methods, including the Prophet model, were employed for temperature predictions, showcasing good predictive accuracy. The results provide crucial insights into climate resilience and temperature forecasting amid rising temperatures associated with climate change. Alarming statistics include an annual erosion rate of −4.47 m/year and critical vulnerability to sea-level rise scenarios. In the extreme 4-m sea-level rise scenario, approximately 35 % of the total surface area (approximately 116.79 m2) is projected to be at risk of submersion, emphasizing the severe susceptibility of these sites to climate-induced sea-level rise. Even in the less severe 2-m scenario, around 24 % of the total area (approximately 80.79 m2) could potentially face submersion, posing a substantial threat to the ancient fortifications of the El Borj heritage site. This study underscores the critical role of preserving coastal heritage in addressing the challenges posed by climate change.