Building extraction, which is a fundamental task in the community of remote sensing image analysis, has been extensively applied in various applications related to smart cities. Due to the complicated background information in urban areas, how to extract building footprints from high-resolution aerial images is challenging. The recent achievements of deep learning have shed light on building extraction and other remote sensing domain tasks. However, the heavy consumption of computational resources and the design of the neural architectures became the biggest bottleneck of utilizing deep learning techniques to improve the performance. In this work, we developed a Neural Architecture Search (NAS) algorithm, dubbed BuildingNAS, for building extraction from high-resolution aerial images. In particular, we built an efficient candidate operation set upon Separable Factorized Residual Blocks as our cell-level search space. Different from previous NAS in semantic segmentation tasks, we employed the hierarchical search space and proposed the Single-Path Sampling strategy to eliminate excessive GPU memory comsumption in searching process. In addition, we proposed an entropy regularized objective for the optimization of architecture parameters. As the result, the larger batch size can be adopted in the whole pipeline to accelerate the searching process, and make the resulted architecture more stable and accurate. We evaluated our proposed algorithm in WHUBuilding Dataset, the derived network achieved mIoU of 86.95% with only 2.05G FLOPs and 3.10 M parameters. The comparison results demonstrate that the network discovered by our algorithm can achieve great efficiency-accuracy trade-off.

建筑物提取是遥感图像分析领域的一项基本任务，已广泛应用于与智能城市相关的各种应用中。由于城市地区的背景信息复杂，如何从高分辨率的航空影像中提取建筑足迹是一项挑战。深度学习的最新成就为建筑物提取和其他遥感领域任务提供了启示。但是，计算资源的大量消耗和神经体系结构的设计成为利用深度学习技术提高性能的最大瓶颈。在这项工作中，我们开发了一种神经建筑搜索（NAS）算法，称为BuildingNAS，用于从高分辨率的航空影像中提取建筑物。尤其是，我们在可分解因子残差块作为单元级搜索空间上建立了有效的候选操作集。与以前的NAS在语义分割任务上不同，我们采用了分层搜索空间并提出了单路径采样策略，以消除搜索过程中过多的GPU内存消耗。此外，我们提出了一个熵正则化目标，以优化建筑参数。结果，可以在整个管道中采用较大的批处理大小，以加快搜索过程，并使生成的体系结构更加稳定和准确。我们在WHUBuilding Dataset中评估了我们提出的算法，导出的网络仅使用2.05G FLOP和3.10 M参数实现了86.95％的mIoU。