Global population is projected to increase by about 70% by 2050, with a growing proportion of people living in urban areas. This trend highlights the importance of accurately assessing urban expansion. Automatic building detection from remotely sensed imagery using deep learning (DL) has demonstrated considerable potential for applications, including sustainable urban planning and infrastructure monitoring. However, the inherent black-box nature of DL models limits their transparency and reduces trust in model-driven decisions. Although various Explainable Artificial Intelligence (XAI) approaches have been proposed to highlight image regions influencing model predictions, qualitative visual inspection alone is insufficient for reliably evaluating the credibility of these explanations. This study evaluates several XAI techniques for building footprint extraction using a U-Net model trained on a refined Massachusetts Buildings Dataset. The segmentation model achieved precision, recall, F1-score, IoU, and overall accuracy values of 89.68%, 85.69%, 87.53%, 79.03%, and 94.35%, respectively. To investigate the model’s decision-making process, three explanation methods, namely Saliency, GradientSHAP, and GuidedGradCAM, were applied. The quality of the generated explanations was then quantitatively assessed using 16 evaluation metrics. Beyond single-image analysis, a dataset-level evaluation was conducted using 547 image patches containing building coverage greater than 20%. The results indicate that GuidedGradCAM produces more consistent and reliable explanations. Furthermore, dataset-level analysis using dense-building samples provides a statistically more robust representation of overall model behaviour compared to evaluations based on individual images. These findings highlight the importance of quantitative assessment in validating the interpretability of DL models for building footprint extraction.