Sara A. Al-Emadi — accepted AI-conference papers

2026

Analysing Satellite Imagery Classification Under Spatial Domain Shift Across Geographic Regions (Abstract Reprint)

AAAI 2026technical

Deep learning models are designed based on the i.i.d. assumption; consequently, they experience a significant performance drop due to the distribution shifts when deployed in real environments. Domain Generalisation (DG) aims to bridge the distribution shift between the source and target domains by

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2025

Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery

Sara A. Al-Emadi, Yin Yang, Ferda Ofli

CVPR 2025poster

Object detectors have achieved remarkable performance in many applications; however, these deep learning models are typically designed under the i.i.d. assumption, meaning they are trained and evaluated on data sampled from the same (source) distribution. In real-world deployment, however, target di…

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