Automated Wildlife Species Detection and Classification in Oman’s Natural Reserves Using Few-Shot Learning

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dc.contributor.authorEthar Tamimi
dc.contributor.authorMalak Abdul Hamid Al Hinai
dc.contributor.authorIman Said Al Hajri
dc.contributor.authorAbdelhamid Abdessalem
dc.date.accessioned2025-10-04T09:14:00Z
dc.date.available2025-10-04T09:14:00Z
dc.date.issued2025-06-01
dc.description.abstractBackground: Wildlife monitoring is a critical component of biodiversity conservation, especially in regions like Oman, where ecological challenges and human-induced threats endanger various species. Camera traps have become invaluable tools for capturing wildlife activity, producing large volumes of image data over time. However, manually analyzing these images is labor-intensive, time-consuming, and prone to human error. Accurate species detection and recognition are essential for informing effective conservation strategies, yet traditional methods of reviewing images present limitations.
dc.identifier.urihttps://dspace.alquds.edu/handle/20.500.12213/10195
dc.language.isoen
dc.publisherDeanship of Research - Al-Quds University
dc.titleAutomated Wildlife Species Detection and Classification in Oman’s Natural Reserves Using Few-Shot Learning
dc.typeArticle
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