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University of Banja Luka , Banja Luka , Bosnia and Herzegovina
In the modern world, automatic classification of buildings from orthophoto images has become critical task in various domains, including urban planning, geographic mapping and infrastructure change monitoring. Traditional building detection methods rely on manual procedures or classical image processing algorithms, which often lead to limited accuracy, especially in complex urban environments where buildings vary in shape, size and spatial arrangement. The development of deep neural networks over the past decade has opened new opportunities for automating and improving this process. However, a significant challenge still remains in achieving an optimal balance between detection accuracy and model efficiency.
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