Single-Image Building Height Estimation Using EfficientNet: A Simplified, Scalable Approach

Abstract

We present a novel approach for estimating building heights using single street-level images. The method employs EfficientNet, a state-of-the-art neural network, to eliminate the need for additional data like street maps. We compare this new method with existing techniques, focusing on accuracy evaluated through metrics like Mean Absolute Error (MAE). The model is pre-trained on the Cityscapes dataset and fine-tuned on images from Toronto's 3D Massing dataset. It demonstrates strong accuracy, with an MAE of 1.21 meters, outperforming traditional methods.