Buildings' segmentation is a fundamental task in the field of earth observation and aerial imagery analysis. Most existing deep learning-based methods in the literature can be applied to a fixed or narrow-range spatial resolution imagery. In practical scenarios, users deal with a broad spectrum of image resolutions. Thus, a given aerial image often needs to be re-sampled to match the spatial resolution of the dataset used to train the deep learning model, which results in a degradation in segmentation performance. To overcome this challenge, we propose, in this manuscript, Scale-invariant Neural Network (Sci-Net) architecture that segments buildings from wide-range spatial resolution aerial images. Specifically, our approach leverages UNet hierarchical representation and Dense Atrous Spatial Pyramid Pooling to extract fine-grained multi-scale representations. Sci-Net significantly outperforms state of the art models on the Open Cities AI and the Multi-Scale Building datasets with a steady improvement margin across different spatial resolutions.

翻译：建筑物分割是地球观测与航空影像分析领域的一项基础任务。现有文献中大多数基于深度学习方法仅适用于固定或窄范围空间分辨率的影像。在实际场景中，用户需处理宽泛的图像分辨率范围，因此给定的航空图像常需重采样以匹配训练深度学习模型所用数据集的空间分辨率，这会导致分割性能下降。为克服这一挑战，本文提出尺度不变神经网络（Sci-Net）架构，该架构能从宽范围空间分辨率的航空图像中分割建筑物。具体而言，我们的方法利用UNet层次化表示与密集空洞空间金字塔池化来提取精细的多尺度表征。在Open Cities AI与多尺度建筑物数据集上，Sci-Net显著优于现有最先进模型，并在不同空间分辨率下均展现稳定性能提升幅度。