Estimating building height from satellite imagery poses significant challenges, especially when monocular images are employed, resulting in a loss of essential 3D information during imaging. This loss of spatial depth further complicates the height estimation process. We addressed this issue by using shadow length as an additional cue to compensate for the loss of building height estimation using single-view imagery. We proposed a novel method that first localized a building and its shadow in the given satellite image. After localization, the shadow length is estimated using a regression model. To estimate the final height of each building, we utilize the principles of photogrammetry, specifically considering the relationship between the solar elevation angle, the vertical edge length of the building, and the length of the building's shadow. For the localization of buildings in our model, we utilized a modified YOLOv7 detector, and to regress the shadow length for each building we utilized the ResNet18 as backbone architecture. Finally, we estimated the associated building height using solar elevation with shadow length through analytical formulation. We evaluated our method on 42 different cities and the results showed that the proposed framework surpasses the state-of-the-art methods with a suitable margin.

翻译：利用卫星影像估算建筑物高度面临重大挑战，尤其在采用单目图像时，成像过程中会损失关键的三维信息。这种空间深度信息的缺失进一步增加了高度估算的难度。我们通过利用阴影长度作为附加线索，以补偿单视角影像在建筑物高度估算中的信息损失，从而应对这一问题。我们提出了一种新颖的方法，首先在给定的卫星影像中定位建筑物及其阴影。定位完成后，使用回归模型估算阴影长度。为估算每栋建筑物的最终高度，我们运用摄影测量学原理，特别考虑了太阳高度角、建筑物垂直边缘长度与建筑物阴影长度之间的关系。在我们的模型中，为定位建筑物，我们采用了改进的YOLOv7检测器；为回归每栋建筑物的阴影长度，我们以ResNet18作为主干网络架构。最后，我们通过解析公式，结合太阳高度角与阴影长度估算了相应的建筑物高度。我们在42个不同城市的数据集上评估了所提方法，结果表明该框架以显著优势超越了现有最优方法。