Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model

TL;DR

This paper introduces a promptable framework and Offset-Building Model for more accurate and generalizable building footprint extraction from aerial images, significantly reducing offset errors and improving roof segmentation performance.

Contribution

The paper proposes a novel prompt-driven framework and Offset-Building Model that enhance offset prediction and generalization in building footprint extraction from aerial images.

Findings

Offset errors reduced by 16.6%

Roof IoU improved by 10.8%

Offset vector loss decreased by 6.5%

Abstract

More accurate extraction of invisible building footprints from very-high-resolution (VHR) aerial images relies on roof segmentation and roof-to-footprint offset extraction. Existing methods based on instance segmentation suffer from poor generalization when extended to large-scale data production and fail to achieve low-cost human interaction. This prompt paradigm inspires us to design a promptable framework for roof and offset extraction, and transforms end-to-end algorithms into promptable methods. Within this framework, we propose a novel Offset-Building Model (OBM). Based on prompt prediction, we first discover a common pattern of predicting offsets and tailored Distance-NMS (DNMS) algorithms for offset optimization. To rigorously evaluate the algorithm's capabilities, we introduce a prompt-based evaluation method, where our model reduces offset errors by 16.6\% and improves roof Intersection over Union (IoU) by 10.8\% compared to other models. Leveraging the common patterns in predicting offsets, DNMS algorithms enable models to further reduce offset vector loss by 6.5\%. To further validate the generalization of models, we tested them using a newly proposed test set, Huizhou test set, with over 7,000 manually annotated instance samples. Our algorithms and dataset will be available at https://github.com/likaiucas/OBM.