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- W4302012306 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." @default.
- W4302012306 created "2022-10-06" @default.
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- W4302012306 date "2022-10-03" @default.
- W4302012306 modified "2023-09-28" @default.
- W4302012306 title "Mastering Spatial Graph Prediction of Road Networks" @default.
- W4302012306 doi "https://doi.org/10.48550/arxiv.2210.00828" @default.
- W4302012306 hasPublicationYear "2022" @default.
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