Normal 0 21 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normal Tablo"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} Coastal management requires rapid, up-to-date, and correct information. Thus, the determination of coastal movements and its directions has primary importance for coastal managers. For monitoring the change of shorelines, remote sensing data, very high resolution aerial images and orthophoto maps are utilized for detections of change on shorelines. It is possible to monitor coastal changes by extracting the coastline from orthophoto maps. Along the Baltic Sea and Riga Gulf, Latvian coastline length is 496 km. It is rich of coastal resources and natural biodiversity. Around 120 km of coastline are affected by significant coastal changes caused by climate change, storms, erosion, human activities and other reasons and they must be monitored. In this study, an object-oriented approach has been proposed to detect shoreline and detect the changes by using 1:5000 scaled orthophoto maps of Riga-Latvia (3bands, R, G, and NIR) in the years of 2007 and 2013. As many of the authors have mentioned, object-oriented classification method can be more successful than the pixel-based methods especially for high resolution images to avoid mix-classification. In the presented study the eCognition object-oriented fuzzy image processing software has been used. The results were compared to the results derived from manual digitizing. Extracted and manually digitized shorelines have been divided in 5 m segments in x axis. The y coordinates of the new nodes were taken from the original “.dxf” file or computed by interpolation. Thus, the RMS errors of selected points were calculated

The inspection and maintenance of urban sewer infrastructure remain critical challenges for megacities, where conventional manual inspection approaches are labor-intensive, time-consuming, and prone to human error. Although deep learning has been increasingly applied to sewer inspection, the field lacks both a publicly available large-scale dataset and a systematic evaluation of CNN and transformer-based models on real sewer footage. The primary aim of this study is to systematically evaluate and compare state-of-the-art deep learning architectures for automated sewer defect detection using a newly introduced dataset. We present the Istanbul Sewer Defect Dataset (ISWDS), comprising 13,491 expert-annotated images collected from Istanbul’s wastewater network and covering eight defect categories that account for approximately 90% of reported failures. The scientific novelty of this work lies in both the introduction of the ISWDS and the first systematic benchmarking of YOLO (v8/11/12) and RT-DETR (v1/v2) architectures under identical protocols on real sewer inspection footage. Experimental results demonstrate that RT-DETR v2 achieves the best performance (F1: 79.03%, Recall: 81.10%), significantly outperforming the best YOLO variant. While transformer-based architectures excel in detecting partially occluded defects and complex operational conditions, YOLO models provide computational efficiency advantages for resource-constrained deployments. Furthermore, a QGIS-based inspection tool integrating the best-performing models was developed to enable real-time video analysis and automated reporting. Overall, this study highlights the trade-offs between accuracy and efficiency, demonstrating that RT-DETR v2 is most suitable for server-based processing. In contrast, compact YOLO variants are more appropriate for edge deployment.