To maintain the serviceability of buildings, the owners need to be informed about the current condition of the water supply and waste disposal systems. Therefore, timely and accurate detection of corrosion on pipe surface is a crucial task. The conventional manual surveying process performed by human inspectors is notoriously time consuming and labor intensive. Hence, this study proposes an image processing-based method for automating the task of pipe corrosion detection. Image texture including statistical measurement of image colors, gray-level co-occurrence matrix, and gray-level run length is employed to extract features of pipe surface. Support vector machine optimized by differential flower pollination is then used to construct a decision boundary that can recognize corroded and intact pipe surfaces. A dataset consisting of 2000 image samples has been collected and utilized to train and test the proposed hybrid model. Experimental results supported by the Wilcoxon signed-rank test confirm that the proposed method is highly suitable for the task of interest with an accuracy rate of 92.81%. Thus, the model proposed in this study can be a promising tool to assist building maintenance agents during the phase of pipe system survey.

This study introduces an innovative machine learning method to model the spatial variation of land surface temperature (LST) with a focus on the urban center of Da Nang, Vietnam. Light Gradient Boosting Machine (LightGBM), support vector machine, random forest, and Deep Neural Network are employed to establish functional relationships between urban LST and its influencing factors. The machine learning approaches are trained and validated using remote sensing data from 2014, 2019, and 2024. Various explanatory variables representing topographical and spatial characteristics, as well as urban landscapes, are used. Experimental results show that LightGBM outperforms other benchmark methods. In addition, Shapley Additive Explanations are utilized to clarify the impact of the factors affecting LST. The analysis outcomes indicate that while the importance of these variables changes over time, urban density and greenspace density consistently emerge as the most influential factors. LightGBM attained R² values of 0.85, 0.92, and 0.91 for the years 2014, 2019, and 2024, respectively. The findings of this work can be helpful for deeper understanding of urban heat stress dynamics and facilitate urban planning.

During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision‐based approach for automatically distinguishing patched and unpatched potholes. Using two‐dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray‐level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture‐based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic‐Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine‐tune the SVC hyperparameters. To establish the hybrid FBI‐SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed‐rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.

During the phase of periodic survey, sealed crack and crack in asphalt pavement surface should be detected accurately. Moreover, the capability of identifying these two defects can help reduce the false‐positive rate for pavement crack detection. Because crack and sealed crack are both line‐based defects and may resemble each other in shape, this study puts forward an innovative method based on computer vision for detecting sealed crack and crack. This method is an integration of feature extraction based on image processing and metaheuristic optimized machine learning. Image processing is used to compute features that characterize visual appearance and texture of the pavement image. Subsequently, Salp Swarm Algorithm integrated with multiclass support vector machine is employed for pattern recognition. Based on experimental results, the newly developed method has achieved the most desired predictive performance with an accuracy rate = 91.33% for crack detection and 92.83% for sealed crack detection.