BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ESGRS - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:ESGRS
X-ORIGINAL-URL:https://www.esgrs.ae
X-WR-CALDESC:Events for ESGRS
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Dubai
BEGIN:STANDARD
TZOFFSETFROM:+0400
TZOFFSETTO:+0400
TZNAME:+04
DTSTART:20200101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Dubai:20210504T100000
DTEND;TZID=Asia/Dubai:20210504T110000
DTSTAMP:20260611T182707
CREATED:20210504T095538Z
LAST-MODIFIED:20240513T045026Z
UID:3460-1620122400-1620126000@www.esgrs.ae
SUMMARY:Semantic Segmentation of Roads in Satellite Imagery
DESCRIPTION:Remote sensing is a field that is constantly growing due to its importance in applications related to urban planning\, risk assessment\, resource exploration\, and disaster management. Some of the most highly researched topics in the area of remote sensing include Land Cover Land Use (LCLU)\, object detection\, scene classification\, and semantic segmentation. Nowadays\, researchers are looking into automating these tasks\, as performing them manually is time and cost inefficient. Therefore\, Deep Learning (DL) algorithms have been utilized for remote sensing and image processing tasks in the recent years. Many studies proved efficiency of DL algorithms\, however\, several challenges remain unsolved. In particular\, semantic segmentation is one of the challenging tasks that is under heavy demand in the field of remote sensing. For this research study\, semantic segmentation is applied to extract roads from satellite images. Road mapping is an essential first step in several applications that include transportation\, traffic management\, and city planning. Extracting roads efficiently and accurately will in turn boost the overall outcome of the aforementioned applications. U-Net is the state-of-the-art Convolutional Neural Networks (CNN) that perform semantic segmentation. This network will be trained using publically available Massachusetts dataset. The accuracy and loss of all experiments will be reported and sample results from the dataset will be demonstrated.\n\n\n    \n    \n    \n\n                \n        \n            \n\n                \n                                \n                    \n                        \n                            \n                                \n                                ENG. MINA AL-SAADRESEARCH ASSISTANT @MBRSC LAB - UNIVERSITY OF DUBAIMina Al-Saad is a Research Assistant at Mohammed Bin Rashid Space Center (MBRSC) Lab at university of Dubai. She received her BSc in Laser and Optoelectronics Engineering from Al-Nahrain University\, Iraq in 2009 and received her MSc in Laser and Optoelectronics Engineering from Al-Nahrain University\, Iraq (2012). Her research interests include: Geographic information system (GIS) and remote sensing\, Autonomous Object Detection\, Semantic Segmentation\, and Satellite Calibration and Validation. \n\n                            \n                        \n                    \n\n                    \n                        \n            \n\n        \n                \n        \n            No Results Found
URL:https://www.esgrs.ae/event/semantic-segmentation-of-roads-in-satellite-imagery/
CATEGORIES:2021,Webinars
ATTACH;FMTTYPE=image/jpeg:https://www.esgrs.ae/wp-content/uploads/2023/07/MicrosoftTeams-image-5.jpg
END:VEVENT
END:VCALENDAR