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Conference Papers Year : 2021

Learning embeddings for cross-time geographic areas represented as graphs

Abstract

Geographic entities from the vertical aerial images can be viewed as discrete objects and represented as nodes in a graph, linked to each other by edges capturing their spatial relationships. Over time, the natural and man made landscape may evolve and thus also their graph representations. This paper addresses the challenging problem of the retrieval and fuzzy matching of graphs to localize near-identical geographical areas across time. Several use-case scenarios are proposed for the end-to-end learning of a graph embedding using Graph Neural Networks (GNN), along with an effective baseline without learning. The results demonstrate the efficiency of our approach, that enables efficient similarity reasoning for novel hand-engineered cross-time graph data. Code and data processing scripts are available online 1 .
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Dates and versions

emse-03548148 , version 1 (29-01-2022)

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Margarita Khokhlova, Nathalie Abadie, Valérie Gouet-Brunet, Liming Chen. Learning embeddings for cross-time geographic areas represented as graphs. SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, Mar 2021, Virtual Event Republic of Korea, South Korea. pp.559-568, ⟨10.1145/3412841.3441936⟩. ⟨emse-03548148⟩

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