EfficientNetB5 for Landscape Classification: Leveraging Deep Learning for Accurate Scene Analysis

Landscape classification is an important task which consists of varied applications in the analysation of the environmental, urban planning and the management of resources. The efficiency of a fine-tuned EfficientNetB5 model in precisely classifying photos into five landscape types: coast, desert, forest, glacier, and mountain is investigated here. Making use of deep learning's capacity to extract intricate features from unprocessed data, the method uses the fine-tuned EfficientNetB5 architecture, well-known for its unusual scaling method balancing depth, width, and resolution, hence providing both strong and computationally economical results. 25,000 images evenly spaced over the five landscape classes make up the teaching and assessment collection. Although different visually similar classifications, such forest and mountain, presented challenges, nevertheless, our fine-tuned model attained an overall accuracy of 92%. The outcomes underline both possibilities for additional improvement, especially in addressing misclassifications in complex terrain types, and the possibility of the model for pragmatic uses in landscape classification. Most categories show significant accuracy and recall.