Automatic detection of shoreline change on coastal Ramsar wetlands of Turkey

This research focuses on the shoreline change rate analysis by automatic image analysis techniques using multi-temporal Landsat images and Digital Shoreline Analysis System (DSAS) along the coastal Ramsar wetlands of Turkey. Five wetlands were selected for analysis: Yumurtalik Ramsar, the Goksu Ramsar, Kizilirmak and Yesilirmak wetlands and Gediz wetlands. Accretion or erosion processes were observed on multi-temporal satellite images along the areas of interest. Landsat images were geometrically and radiometrically corrected for the quantitative coastline delineation analysis. DSAS (Digital Shoreline Analysis System) was used as a reliable statistical approach for the rate of coastline change. For the detection of coastal change in Aegean part (Gediz wetland) of the study, zonal change detection method was used. As a result of the analysis, in some parts of research area remarkable shoreline changes (more than 765 m withdrawal and -20.68 m/yr erosion in Yumurtalik, 650 m withdrawal and -25.99 m/yr erosion in Goksu, 660 m withdrawal and -16.10 m/yr erosion in Kizilirmak and 640 m withdrawal and -4.91 m/yr erosion in Yesilirmak) were observed for three periods (1989, 1999 and 2009). Wetland in Gediz delta which is 35.57 km2 was converted to sea or salt pan for the period 1975 and 2009. © 2011 Elsevier Ltd. All rights reserved

AN ANALYSIS OF NEIGHBOURHOOD TYPES FOR POINTNET++ IN SEMANTIC SEGMENTATION OF AIRBORNE LASER SCANNING DATA

The objective of the study is to conduct a comprehensive examination of how different neighbourhood types, namely spherical, cylindrical, and k-nearest neighbour (kNN), influence the feature extraction capabilities of the PointNet++ architecture in the semantic segmentation of Airborne Laser Scanning (ALS) point clouds. Two datasets are utilized for semantic segmentation analysis: the Dayton Annotated LiDAR Earth Scan (DALES) and the ISPRS 3D Semantic Labelling Benchmark datasets. In the experiments, the kNN method exhibited approximately 1% higher accuracy in weighted mean F1 and intersection over union (IoU) metrics compared to the spherical and cylindrical neighbourhood types on the DALES dataset. However, in the generalization experiment conducted on the ISPRS dataset, the spherical neighbourhood achieved the best results in these metrics, outperforming the cylindrical neighbourhood by a small margin. Notably, the kNN method was the least accurate, with a decrease in accuracy of approximately 1% in both weighted mean IoU and F1 scores. These findings suggest that the features extracted from spherical and cylindrical neighbourhood types are more generalizable compared to those from the kNN method