Scalable Building Height Estimation From Street Scene Images

Building height estimation plays an essential role in many applications such as 3-D city rendering, urban planning, and navigation. Recently, a new building height estimation method was proposed using street scene images and 2-D maps, which is more scalable than traditional methods that use high-resolution optical images, radar, or light detection and ranging (LiDAR) data, which are proprietary or expensive to obtain. The method needs to detect building rooflines to compute building height via the pinhole camera model. We observe that this method has limitations in handling complex street scene images where buildings occlude each other or are blocked by other objects such as trees since rooflines can be difficult to locate. To address these limitations, we propose a robust building height estimation method that computes building height simultaneously from street scene images with an orientation along the street and images facing the building with an upward-looking view. We first detect roofline candidates from both types of images. Then, we use a deep neural network called RoofNet to classify and filter these candidates and select the best candidate via an entropy-based ranking algorithm. When the true roofline is identified, we compute building height via the pinhole camera model. Experimental results show that the proposed RoofNet model yields a higher accuracy on building corner and roofline candidate filtering compared with state-of-the-art open-set classifiers. Our overall building height estimation method outperforms the baseline by up to 11.9% in accuracy and achieves 92.8% in height estimation error within 4 m on the collected dataset.