Estimating building footprint maps from geospatial data is of paramount importance in urban planning, development, disaster management, and various other applications. Deep learning methodologies have gained prominence in building segmentation maps, offering the promise of precise footprint extraction without extensive post-processing. However, these methods face challenges in generalization and label efficiency, particularly in remote sensing, where obtaining accurate labels can be both expensive and time-consuming. To address these challenges, we propose terrain-aware self-supervised learning, tailored to remote sensing, using digital elevation models from LiDAR data. We propose to learn a model to differentiate between bare Earth and superimposed structures enabling the network to implicitly learn domain-relevant features without the need for extensive pixel-level annotations. We test the effectiveness of our approach by evaluating building segmentation performance on test datasets with varying label fractions. Remarkably, with only 1% of the labels (equivalent to 25 labeled examples), our method improves over ImageNet pre-training, showing the advantage of leveraging unlabeled data for feature extraction in the domain of remote sensing. The performance improvement is more pronounced in few-shot scenarios and gradually closes the gap with ImageNet pre-training as the label fraction increases. We test on a dataset characterized by substantial distribution shifts and labeling errors to demonstrate the generalizability of our approach. When compared to other baselines, including ImageNet pretraining and more complex architectures, our approach consistently performs better, demonstrating the efficiency and effectiveness of self-supervised terrain-aware feature learning.

翻译：从地理空间数据中估算建筑物足迹地图对于城市规划、开发、灾害管理及众多其他应用至关重要。深度学习方法在建筑物分割图中已占据重要地位，能够在不依赖大量后处理的情况下实现精确足迹提取。然而，这些方法在泛化能力和标注效率方面面临挑战，尤其在遥感领域，获取精确标注既昂贵又耗时。为解决这些问题，我们提出一种面向遥感的地形感知自监督学习方法，该方法利用LiDAR数据的数字高程模型。我们旨在训练模型区分裸地与叠加结构，使网络能够无需大量像素级标注而隐式学习领域相关特征。通过在不同标注比例测试数据集上评估建筑物分割性能，我们验证了该方法的有效性。值得注意的是，仅使用1%的标注（相当于25个标注样本），我们的方法便优于ImageNet预训练，凸显了在遥感领域利用无标注数据进行特征提取的优势。性能提升在少样本场景下更为显著，并随标注比例增加逐渐缩小与ImageNet预训练的差距。我们采用具有显著分布偏移和标注误差的数据集进行测试，以证明方法的泛化能力。与包括ImageNet预训练及更复杂架构在内的其他基线方法相比，我们的方法持续表现更优，证明了自监督地形感知特征学习的高效性与有效性。