ObliCity: A Benchmark and Baseline for Roof-to-Ground Projection Displacement Correction

2026-07-27

AI Summary

This study addresses the spatial distortion in oblique-view remote sensing imagery caused by geometric projection displacement between building rooftops and their ground footprints. To this end, it formulates roof-to-footprint offset vector (RFOV) extraction as an independent learning task, decoupling geometric correction from semantic segmentation. The authors introduce ObliCity, the first large-scale oblique urban dataset, which integrates high-resolution drone and satellite imagery, and propose DragRoof—a framework based on ordinary differential equations (ODEs) that simulates a continuous, annotation-inspired dragging process to adaptively learn geometrically consistent offset fields. Experiments demonstrate that the proposed method achieves state-of-the-art RFOV extraction accuracy on ObliCity with fewer inference steps, significantly outperforming existing approaches in both direction and magnitude estimation.

Abstract

Oblique-view urban remote sensing imagery inevitably exhibits geometric projection displacements between building roofs and footprints, leading to significant distortions in spatial structure. Existing approaches either ignore these deformations or handle them implicitly within segmentation-based frameworks, where progress is dominated by general segmentation advances rather than improvements in geometric correction. In this work, we explicitly define roof-to-footprint offset vector (RFOV) extraction as an independent learning task that decouples geometric alignment from semantic segmentation. To support this task, we introduce the Oblique City dataset (ObliCity), the first large-scale benchmark that integrates high-resolution UAV imagery and globally distributed satellite data, covering diverse city morphologies and camera perspectives. Methodologically, we reformulate DragOSM into DragRoof, an ODE-based framework inspired by human annotation behavior. By simulating the continuous process of dragging roofs toward their footprints, DragRoof learns deterministic, geometry-consistent offset fields and adaptively determines convergence through an end token. Extensive experiments on ObliCity demonstrate that DragRoof achieves state-of-the-art RFOV extraction performance, requiring fewer inference steps while delivering superior directional and length accuracy. Our dataset and model establish a principled foundation for studying projection displacement correction in oblique remote sensing imagery. The source code and dataset will be avaliable at https://github.com/likaiucas/DragRoof.

Problem

• roof-to-ground projection displacement
• geometric correction
• oblique remote sensing
• building footprint alignment
• spatial distortion

Innovation

• roof-to-footprint offset vector
• oblique remote sensing
• geometric displacement correction
• ODE-based modeling
• DragRoof