The identification and annotation of buildings has long been a tedious and expensive part of high-precision vector map production. The deep learning techniques such as fully convolution network (FCN) have largely promoted the accuracy of automatic building segmentation from remote sensing images. However, compared with the deep-learning-based building segmentation methods that greatly benefit from data-driven feature learning, the building boundary vector representation generation techniques mainly rely on handcrafted features and high human intervention. These techniques continue to employ manual design and ignore the opportunity of using the rich feature information that can be learned from training data to directly generate vectorized boundary descriptions. Aiming to address this problem, we introduce PolygonCNN, a learnable end-to-end vector shape modeling framework for generating building outlines from aerial images. The framework first performs an FCN-like segmentation to extract initial building contours. Then, by encoding the vertices of the building polygons along with the pooled image features extracted from segmentation step, a modified PointNet is proposed to learn shape priors and predict a polygon vertex deformation to generate refined building vector results. Additionally, we propose 1) a simplify-and-densify sampling strategy to generate homogeneously sampled polygon with well-kept geometric signals for shape prior learning; and 2) a novel loss function for estimating shape similarity between building polygons with vastly different vertex numbers. The experiments on over 10,000 building samples verify that PolygonCNN can generate building vectors with higher vertex-based F1-score than the state-of-the-art method, and simultaneously well maintains the building segmentation accuracy achieved by the FCN-like model.

建筑物的识别和标注长期以来一直是高精度矢量地图制作中乏味且昂贵的部分。诸如全卷积网络（FCN）之类的深度学习技术极大地提高了根据遥感图像进行自动建筑物分割的准确性。但是，与基于深度学习的建筑物分割方法相比，该方法大大受益于数据驱动的特征学习，建筑物边界矢量表示生成技术主要依靠手工特征和高度的人为干预。这些技术继续采用手动设计，并且忽略了使用可以从训练数据中学习的丰富特征信息直接生成矢量化边界描述的机会。为了解决这个问题，我们介绍了PolygonCNN，一个可学习的端到端矢量形状建模框架，用于从航空影像生成建筑物轮廓。该框架首先执行类似FCN的分段，以提取初始建筑物轮廓。然后，通过对建筑物多边形的顶点以及从分割步骤中提取的合并图像特征进行编码，提出了一种改进的PointNet来学习形状先验并预测多边形顶点变形以生成精炼的建筑物矢量结果。此外，我们提出1）简化和密集采样策略，以生成具有良好形状信息的均匀采样的多边形，以进行形状先验学习；2）一种新颖的损失函数，用于估计顶点数量相差很大的建筑物多边形之间的形状相似性。超过10个实验