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

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 an...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-15
Main Authors: Li, Kai, Deng, Yupeng, Kong, Yunlong, Liu, Diyou, Chen, Jingbo, Meng, Yu, Ma, Junxian, Wang, Chenhao
Format: Article
Language:English
Subjects:
Algorithms
Building footprint extraction (BFE)
Buildings
Data mining
Data models
Error reduction
Feature extraction
Image processing
Image resolution
Image segmentation
Instance segmentation
nonmaximum suppression (NMS)
Offsets
Prediction algorithms
Predictions
Production
Remote sensing
roof segmentation
roof-to-footprint offset extraction
segment anything model (SAM)
Test sets
Three-dimensional displays
Training
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Description
Summary: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 (VL) 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 .
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3487652