Rebuild City Buildings from Off-Nadir Aerial Images with Offset-Building Model

Accurate measurement of the offset from roof-to-footprint in very-high-resolution remote sensing imagery is crucial for urban information extraction tasks. With the help of deep learning, existing methods typically rely on two-stage CNN models to extract regions of interest on building feature maps. At the first stage, a Region Proposal Network (RPN) is applied to extract thousands of ROIs (Region of Interests) which will post-imported into a Region-based Convolutional Neural Networks (RCNN) to extract wanted information. However, because of inflexible RPN, these methods often lack effective user interaction, encounter difficulties in instance correspondence, and struggle to keep up with the advancements in general artificial intelligence. This paper introduces an interactive Transformer model combined with a prompt encoder to precisely extract building segmentation as well as the offset vectors from roofs to footprints. In our model, a powerful module, namely ROAM, was tailored for common problems in predicting roof-to-footprint offsets. We tested our model's feasibility on the publicly available BONAI dataset, achieving a significant reduction in Prompt-Instance-Level offset errors ranging from 14.6% to 16.3%. Additionally, we developed a Distance-NMS algorithm tailored for large-scale building offsets, significantly enhancing the accuracy of predicted building offset angles and lengths in a straightforward and efficient manner. To further validate the model's robustness, we created a new test set using 0.5m remote sensing imagery from Huizhou, China, for inference testing. Our code, training methods, and the updated dataset will be accessable at https://github.com/likaiucas.