As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, and better tools for flood risk communication could increase the support for flood-resilient infrastructure development. Our work aims to enable more visual communication of large-scale climate impacts via visualizing the output of coastal flood models as satellite imagery. We propose the first deep learning pipeline to ensure physical-consistency in synthetic visual satellite imagery. We advanced a state-of-the-art GAN called pix2pixHD, such that it produces imagery that is physically-consistent with the output of an expert-validated storm surge model (NOAA SLOSH). By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism. We envision our work to be the first step towards a global visualization of how the climate challenge will shape our landscape. Continuing on this path, we show that the proposed pipeline generalizes to visualize reforestation. We also publish a dataset of over 25k labelled image-triplets to study image-to-image translation in Earth observation.

Knowledge graphs are concurrently published and edited in the Web of data. Hence they may overlap, which makes key the task that consists in matching their content. This task encompasses the identification, within and across knowledge graphs, of nodes that are equivalent, more specific, or weakly related. In this article, we propose to match nodes of a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings. We experimented this approach on a biomedical knowledge graph and particularly investigated the interplay between formal semantics and GCN models with the two following main focuses. Firstly, we applied various inference rules associated with domain knowledge, independently or combined, before learning node embeddings, and we measured the improvements in matching results. Secondly, while our GCN model is agnostic to the exact alignment relations (e.g., equivalence, weak similarity), we observed that distances in the embedding space are coherent with the "strength" of these different relations (e.g., smaller distances for equivalences), somehow corresponding to their rediscovery by the model.