论文标题
多边形建筑物通过框架现场学习
Polygonal Building Segmentation by Frame Field Learning
论文作者
论文摘要
尽管最先进的图像分割模型通常以栅格格式输出分割，但地理信息系统中的应用通常需要向量多边形。为了帮助弥合深网络输出与下游任务中使用的格式之间的差距，我们将框架字段输出添加到深层分割模型中，以从遥感图像中提取建筑物。我们训练一个深层的神经网络，该网络将预测的框架场与地面真相轮廓保持一致。这个额外的目标通过利用多任务学习来提高细分质量，并提供后来促进多代化的结构信息。我们还引入了一种多边形算法，该算法利用框架场以及栅格分割。我们的代码可在https://github.com/lydorn/polygonization-by-frame-field-learning上找到。
While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation model for extracting buildings from remote sensing images. We train a deep neural network that aligns a predicted frame field to ground truth contours. This additional objective improves segmentation quality by leveraging multi-task learning and provides structural information that later facilitates polygonization; we also introduce a polygonization algorithm that utilizes the frame field along with the raster segmentation. Our code is available at https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning.
