PolyR-CNN: R-CNN for end-to-end polygonal building outline extraction

TL;DR

PolyR-CNN is an efficient end-to-end framework for extracting polygonal building outlines from remote sensing images, achieving high accuracy and speed by leveraging RoI features and a novel vertex proposal scheme.

Contribution

The paper introduces PolyR-CNN, a fully integrated model that predicts building polygons directly from images, simplifying architecture and improving computational efficiency over existing methods.

Findings

Achieves 79.2 AP on CrowdAI dataset, outperforming state-of-the-art.

Operates 2.5 times faster and is four times lighter than PolyWorld.

Maintains 71.1 AP with ResNet-50 backbone, with fourfold speed increase.

Abstract

Polygonal building outline extraction has been a research focus in recent years. Most existing methods have addressed this challenging task by decomposing it into several subtasks and employing carefully designed architectures. Despite their accuracy, such pipelines often introduce inefficiencies during training and inference. This paper presents an end-to-end framework, denoted as PolyR-CNN, which offers an efficient and fully integrated approach to predict vectorized building polygons and bounding boxes directly from remotely sensed images. Notably, PolyR-CNN leverages solely the features of the Region of Interest (RoI) for the prediction, thereby mitigating the necessity for complex designs. Furthermore, we propose a novel scheme with PolyR-CNN to extract detailed outline information from polygon vertex coordinates, termed vertex proposal feature, to guide the RoI features to predict more regular buildings. PolyR-CNN demonstrates the capacity to deal with buildings with holes through a simple post-processing method on the Inria dataset. Comprehensive experiments conducted on the CrowdAI dataset show that PolyR-CNN achieves competitive accuracy compared to state-of-the-art methods while significantly improving computational efficiency, i.e., achieving 79.2 Average Precision (AP), exhibiting a 15.9 AP gain and operating 2.5 times faster and four times lighter than the well-established end-to-end method PolyWorld. Replacing the backbone with a simple ResNet-50, PolyR-CNN maintains a 71.1 AP while running four times faster than PolyWorld.