We propose a framework to interpret deep convolutional models for visual place classification. Given a deep place classification model, our proposed method produces visual explanations and saliency maps that reveal the understanding of images by the model. To evaluate the interpretability, t-SNE algorithm is used for mapping and visualization of these latent visual explanations. Moreover, we use pre-trained semantic segmentation networks to label all objects appearing in the visual explanations for our discriminative models. This work has two main goals. The first one is to investigate the consistency of visual explanations by different models. The second goal is to investigate whether visual explanations are meaningful and interpretable or not in an unsupervised manner. We find that varying the CNN architecture leads to variations in the discriminative visual explanations, but these visual explanations are interpretable.