In the context of road transportation, detecting road surface irregularities, particularly potholes, is of paramount importance due to their implications for driving comfort, transportation costs, and potential accidents. This study presents the development of a system for pothole detection using vibration sensors and the Global Positioning System (GPS) integrated within smartphones, without the need for additional onboard devices in vehicles incurring extra costs. In the realm of vibration-based road anomaly detection, a novel approach employing convolutional neural networks (CNNs) is introduced, breaking new ground in this field. An iOS-based application was designed for the acquisition and transmission of road vibration data using the built-in three-axis accelerometer and gyroscope of smartphones. Analog road data were transformed into pixel-based visuals, and various CNN models with different layer configurations were developed. The CNN models achieved a commendable accuracy rate of 93.24% and a low loss value of 0.2948 during validation, demonstrating their effectiveness in pothole detection. To evaluate the performance further, a two-stage validation process was conducted. In the first stage, the potholes along predefined routes were classified based on the labeled results generated by the CNN model. In the second stage, observations and detections during the field study were used to identify road potholes along the same routes. Supported by the field study results, the proposed method successfully detected road potholes with an accuracy ranging from 80% to 87%, depending on the specific route.

The stream threshold is a user-defined and important parameter that directly affects the drainage network and basin boundaries that would be obtained as a result of hydrological analysis. There have been several approaches developed for the stream threshold. According to the approaches treated in this study, the stream threshold is determined based on (1) the flow accumulation statistics: (a) 1% of the maximum flow accumulation value and (b) the mean flow accumulation value; (2) the flow accumulation values at the cells including the beginning points of the line features representing the existing streams; and (3) the adjacency and direction relationships between the cells including the beginning points of the lines in the drainage network to be derived. In this paper, a new approach as an integrant of the last approach was introduced (not only the adjacency and direction relationships between the cells including the beginning points, but also the adjacency and direction relationships amongst all of the cells, as well as including the lines considered). An experiment was conducted in accordance with the purpose of comparing and evaluating these approaches. According to the experimental results, it was found that the last two approaches differed from the first three approaches in terms of providing clues to the user regarding the quality and the quantity of the drainage network to be derived.

Within the contemporary urban development discourse, the paradigm of smart cities has gained prominence over the past two decades. Ensuring sustainability in smart cities requires coherent orchestration of processes that span design, construction, operations, and management. Central to this orchestration are technologies such as Building Information Modeling (BIM), which provides detailed architectural data, and Geographic Information Systems (GIS), which provide comprehensive geographic intelligence. However, a significant challenge remains: data degradation during BIM-GIS integration. This data inconsistency, exacerbated by the different data structures of BIM and GIS, is a barrier to true interoperability. One promising solution to this conundrum is the use of Semantic Web technologies. In this study, we leverage Semantic Linked Data and geometric conversion tools to develop an algorithm that mitigates the loss of semantic information during the BIM-to-GIS conversion process. The effectiveness of this approach is underscored by a 95% accuracy rate of the converted semantic information.

Multi-representation databases (MRDBs) are used in several geographical information system applications for different purposes. MRDBs are mainly obtained through model and cartographic generalizations. Simplification is the essential operator of cartographic generalization, and streams and lakes are essential features in hydrography. In this study, a new algorithm was developed for the simplification of streams and lakes. In this algorithm, deviation angles and error bands are used to determine the characteristic vertices and the planimetric accuracy of the features, respectively. The algorithm was tested using a high-resolution national hydrography dataset of Pomme de Terre, a sub-basin in the USA. To assess the performance of the new algorithm, the Bend Simplify and Douglas-Peucker algorithms, the medium-resolution hydrography dataset of the sub-basin, and Töpfer’s radical law were used. For quantitative analysis, the vertex numbers, the lengths, and the sinuosity values were computed. Consequently, it was shown that the new algorithm was able to meet the main requirements (i.e., accuracy, legibility and aesthetics, and storage).

Road-matching processes establish links between multi-sourced road lines representing the same entities in the real world. Several road-matching methods have been developed in the last three decades. The main issue related to this process is selecting the most appropriate method. This selection depends on the data and requires a pre-process (i.e., accuracy assessment). This paper presents a new matching method for roads composed of different patterns. The proposed method matches road lines incrementally (i.e., from the most similar matching to the least similar). In the experimental testing, three road networks in Istanbul, Turkey, which are composed of tree, cellular, and hybrid patterns, provided by the municipality (authority), OpenStreetMap (volunteered), TomTom (private), and Basarsoft (private) were used. The similarity scores were determined using Hausdorff distance, orientation, sinuosity, mean perpendicular distance, mean length of triangle edges, and modified degree of connectivity. While the first four stages determined certain matches with regards to the scores, the last stage determined them with a criterion for overlapping areas among the buffers of the candidates. The results were evaluated with manual matching. According to the precision, recall, and F-value, the proposed method gives satisfactory results on different types of road patterns.