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.

翻译：通过卫星图像准确预测公路网络需要全球对网络地形学的了解。我们提议采用一个基于图表的框架,利用强化学习(RL)方法模拟增加图表边缘序列,从而捕捉这种高层次的信息。特别是,考虑到与卫星图像有关的部分生成的图表,一个RL代理提出尽量扩大累积奖励的修改。与通常更局限于常用代谢损失的标准监督技术相反,这些奖励可以基于各种复杂、可能不连续的、具有兴趣的尺度。这产生更大的权力和灵活性,以解析依赖问题的知识。几个基准数据集的经验性结果表明,在使用基于树木的搜索时,其性能得到了提高,对图形表层学的高度推理也有所增加。我们进一步强调我们的方法在重大封闭下具有优势,为此采用了新的合成基准数据集。