This paper introduces a multi-modal network that learns to retrieve by content vertical aerial images of French urban and rural territories taken about 15 years apart. This means it should be invariant against a big range of changes as the (nat-ural) landscape evolves over time. It leverages the original images and semantically segmented and labeled regions. The core of the method is a Siamese network that learns to extract features from corresponding image pairs across time. These descriptors are discriminative enough, such that a simple kNN classifier on top, suffices as final geo-matching criteria. The method outperformed SOTA "off-the-shelf" image descrip-tors GEM and ResNet50 on the new aerial images dataset.