Polygonal Building Segmentation by Frame Field Learning (2004.14875v2)

Abstract: While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation model for extracting buildings from remote sensing images. We train a deep neural network that aligns a predicted frame field to ground truth contours. This additional objective improves segmentation quality by leveraging multi-task learning and provides structural information that later facilitates polygonization; we also introduce a polygonization algorithm that utilizes the frame field along with the raster segmentation. Our code is available at https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning.

• The paper presents a novel framework that integrates frame field learning into CNNs to improve polygonal building extraction from remote sensing imagery.
• The method employs a multi-task learning strategy with additional loss functions to align frame fields with building contours.
• Experimental results show enhanced segmentation accuracy and computational efficiency, facilitating precise building footprint extraction for GIS.

An Academic Overview of "Polygonal Building Extraction by Frame Field Learning"

The paper under review, titled "Polygonal Building Extraction by Frame Field Learning," addresses the challenge of translating raster outputs from state-of-the-art image segmentation models into vector polygon formats required by geographic information systems (GIS). This research integrates a frame field output into a deep learning framework for building extraction from remote sensing images, aiming to improve the subsequent polygonization process.

Approach and Methodology

The proposed method augments a deep convolutional neural network (CNN) with a frame field output to generate vector representations (polygons) directly from raster data—a critical necessity for GIS applications. The research introduces an innovative multi-task learning approach wherein a deep neural network is trained to align predicted frame fields with the ground truth contours of buildings. This model leverages additional loss functions to ensure that the predicted frame field maintains alignment with building edges, facilitating more accurate and complex polygon generation.

The authors implement a new polygonization algorithm based on the frame field, termed the Active Skeleton Model (ASM). This method extends the traditional Active Contours Model (ACM) by fitting a skeleton graph to the frame field. The approach efficiently reuses shared walls between adjoining buildings and is inherently suitable for handling polygons with non-trivial topologies, such as buildings with courtyards or holes.

Key Contributions

The paper’s contributions are manifold:

1. Frame Field Learning: By introducing a frame field aligned to building edges, segmentation quality is enhanced, resulting in sharper corners and improved structural representation.
2. Loss Regularization: Coupling losses enforce consistency between segmentation output and frame field, leveraging multi-task learning.
3. Frame Field Polygonization: The authors propose a fast, GPU-parallelizable polygonization method that utilizes the frame field, optimizing both complexity and computational efficiency.

Experimental Results

The paper presents robust experimental results across several datasets, including the CrowdAI Mapping Challenge, Inria Aerial Image Labeling dataset, and a proprietary dataset. The proposed model exhibits superior performance in extracting building outlines with increased accuracy, as evidenced by metrics such as mean max tangent angle error and MS COCO Average Precision (AP) and Average Recall (AR) scores.

Implications and Future Work

The implications of this paper are significant for GIS and remote sensing disciplines, offering an effective bridge between raster-based deep learning outputs and vector-based geospatial data applications. The proposed framework demonstrates the potential for substantial improvements in building footprint extraction, crucial for urban planning, disaster management, and environmental monitoring.

Looking ahead, the paper hints at the expansion of frame field learning to multi-class segmentation tasks wherein shared geometric features can be extracted across diverse classes. Additionally, future work may explore the application of this methodology to other high-resolution remote sensing tasks beyond building extraction.

In summary, this paper contributes to the field by significantly refining the process of extracting vector polygon representations from raster image data—a pivotal concern for the practical use of deep learning in remote sensing and GIS. The work provides a scalable, computationally efficient solution that enhances the fidelity of geospatial analyses, setting the stage for future innovations in AI-driven geographic information processing.