Automated Extraction of Building Footprints from High-Resolution Cartosat-2 Imagery for Urban Planning

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The proposed method achieved building footprint extraction accuracies of 83.10%, 92.38%, and 89.52% across three test regions.
Extraction demonstrated a strong F1-score of 0.9267, indicating balanced precision and recall for building footprint detection.
Automated extraction utilizes machine learning and image processing techniques for efficient urban analysis and planning.
This methodology offers significant improvements over traditional GIS methods, suggesting a shift towards more automated approaches in urban development.

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Automated extraction of building footprints improves urban planning in Gandhinagar City, suggesting enhanced scalability and accuracy.

Patel et al. (2025) studied this question.

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