Mastering Spatial Graph Prediction of Road Networks

• URL: http://arxiv.org/abs/2210.00828v1
• Date: Mon, 3 Oct 2022 11:26:09 GMT
• Title: Mastering Spatial Graph Prediction of Road Networks
• Authors: Sotiris Anagnostidis, Aurelien Lucchi, Thomas Hofmann
• Abstract summary: We propose a graph-based framework that simulates the addition of sequences of graph edges. In particular, given a partially generated graph associated with a satellite image, an RL agent nominates modifications that maximize a cumulative reward.
• Score: 18.321172168775472
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Accurately predicting road networks from satellite images requires a global understanding of the network topology. We propose to capture such high-level information by introducing a graph-based framework that simulates the addition of sequences of graph edges using a reinforcement learning (RL) approach. In particular, given a partially generated graph associated with a satellite image, an RL agent nominates modifications that maximize a cumulative reward. As opposed to standard supervised techniques that tend to be more restricted to commonly used surrogate losses, these rewards can be based on various complex, potentially non-continuous, metrics of interest. This yields more power and flexibility to encode problem-dependent knowledge. Empirical results on several benchmark datasets demonstrate enhanced performance and increased high-level reasoning about the graph topology when using a tree-based search. We further highlight the superiority of our approach under substantial occlusions by introducing a new synthetic benchmark dataset for this task.

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