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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference 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.

Original languageEnglish
Title of host publicationRemote Sensing for Agriculture, Ecosystems, and Hydrology XXIV
EditorsChristopher M. U. Neale, Antonino Maltese
ISBN (Electronic)9781510655270
Publication statusPublished - 2022
EventRemote Sensing for Agriculture, Ecosystems, and Hydrology XXIV 2022 - Berlin, Germany
Duration: Sept 5 2022Sept 7 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceRemote Sensing for Agriculture, Ecosystems, and Hydrology XXIV 2022


  • Cyclones
  • Deep learning
  • Impact assessment
  • Remote sensing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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