Remote sensing and deep learning techniques for impact assessment of Shaheen cyclone at Al Batinah governorate of Oman

Yaseen Al-Mulla*, Krishna Parimi, Mohammed Bait-Suwailam

*المؤلف المقابل لهذا العمل

نتاج البحث: Conference contribution


The Shaheen cyclone triggered coastal areas of Al-Batinah Governorate of the Sultanate of Oman and caused devastating impacts on vegetation areas, infrastructure and properties that resulted in severe damages and human casualties. A comprehensive evaluation of the cyclone is essential to identify the most impacted areas in the Governorate especially in its four regions Al-Musanaah, Al-Suwaiq, Al-Khaboura and Saham. An advanced techniques and very high resolution datasets have been used to study, analyze and mapping the effects caused by the shaheen Cyclone. The systematic approach included investigating changes before and after the cyclone of various parameters such as vegetation coverage, detection of buildup damages in agriculture lands, detailed study on coastline changes and inundations in agriculture areas & urban community. Both pre-classification and post classification change detection techniques were used to assess the impact of the cyclone. Using very high resolution datasets and application of latest techniques of Geographical information system and remote sensing like vegetation indices, deep learning models, spatial analysis and advanced object based detection methods were used to analyze the damages caused by the cyclone. Agricultural land change detection and its coverage calculation was studied and mapped. All individual vegetation parcels within the study area were analyzed and delineated. Date palm trees classification and counting was conducted and mapped. Inundations in agriculture lands and urban buildings in the agriculture areas were identified and mapped. The changes in the coastline and marine features were studied and mapped using latest object based classification. The outcome of this study was helpful in identifying the most affected areas and providing tempo-geospatially damage assessment that assist the humanitarian aid as well as paving the road for future hazard mitigation and new protection strategies.

اللغة الأصليةEnglish
عنوان منشور المضيفRemote Sensing for Agriculture, Ecosystems, and Hydrology XXIV
المحررونChristopher M. U. Neale, Antonino Maltese
رقم المعيار الدولي للكتب (الإلكتروني)9781510655270
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2022
الحدثRemote Sensing for Agriculture, Ecosystems, and Hydrology XXIV 2022 - Berlin, Germany
المدة: سبتمبر ٥ ٢٠٢٢سبتمبر ٧ ٢٠٢٢

سلسلة المنشورات

الاسمProceedings of SPIE - The International Society for Optical Engineering
مستوى الصوت12262
رقم المعيار الدولي للدوريات (المطبوع)0277-786X
رقم المعيار الدولي للدوريات (الإلكتروني)1996-756X


ConferenceRemote Sensing for Agriculture, Ecosystems, and Hydrology XXIV 2022

ASJC Scopus subject areas

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