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

Healthcare utilization and unmet needs of patients with antisynthetase syndrome: An international patient survey

Antisynthease syndrome (ASSD) is a rare, complex and understudied autoimmune disease. Internet-based studies can overcome barriers of traditional on-site research and are therefore very appealing for rare diseases. The aim of this study was to investigate patient-reported symptoms, diagnostic delay, symptoms, medical care, health status, working status, disease knowledge and willingness to participate in research of ASSD patients by conducting an international web-based survey. The multilingual questionnaire was created by an international group of rheumatologists and patients and distributed online. 236 participants from 22 countries completed the survey. 184/236 (78.0%) were female, mean age (SD) was 49.6 years (11.3) and most common antisynthetase antibody was Jo-1 (169/236, 71.6%). 79/236 (33.5%) reported to work full-time. Median diagnostic delay was one year. The most common symptom at disease onset was fatigue 159/236 (67.4%), followed by myalgia 130/236 (55.1%). The complete triad of myositis, arthritis and lung involvement verified by a clinician was present in 42/236 (17.8%) at disease onset and in 88/236 (37.3%) during the disease course. 36/236 (15.3%) reported to have been diagnosed with fibromyalgia and 40/236 (16.3%) with depression. The most reported immunosuppressive treatments were oral corticosteroids 179/236 (75.9%), followed by rituximab 85/236 (36.0%). 73/236 (30.9%) had received physiotherapy treatment. 71/236 (30.1%) reported to know useful online information sources related to ASSD. 223/236 (94.5%) were willing to share health data for research purposes once a year. Our results reiterate that internet-based research is invaluable for cooperating with patients to foster knowledge in rare diseases