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Building Extraction at Amodal-Instance- Segmentation Level: Datasets and Framework
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DOI：10.1109/TGRS.2023.3345867.png)
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Abstract
En 中文
This article presents two amodal instance segmentation (AIS) datasets in the field of remote sensing: the multiview building dataset for the Zurich region (MVB-Zurich) and the multisize building dataset for the Dortmund region (MSB-Dortmund). Additionally, a new AIS network framework, named FE-RSBL-AmodalNet, is proposed, which is based on feature enhancement and remote sensing boundary loss. Instance segmentation has emerged as a popular approach for building extraction in recent years. However, a limitation of using such algorithms for building extraction is the inability to predict the invisible areas. Consequently, the extracted building contours are incomplete, which hampers certain applications relying on accurate building extraction. To address this limitation, AIS has emerged as a promising research field. Unfortunately, there is currently a lack of datasets available for developing AIS algorithms in the field of remote sensing, which poses a barrier to the widespread application of AIS in this domain. This article introduces two AIS datasets specifically designed for remote-sensing buildings. The datasets consist of images captured from tilted views using a tilt photography system, resulting in a significant presence of occluded areas within the images. Moreover, the MVB-Zurich dataset comprises aerial images captured from five different viewpoints, while the MSB-Dortmund dataset encompasses diverse buildings, including garages and residences, with varying sizes. The multiview and multisize attributes of these datasets offer enhanced research opportunities. Furthermore, a new AIS framework, specifically designed for remote sensing buildings, was proposed with the aim of accurately predicting complete building contours.
Keywords:
Artificial intelligence
Remote sensing
Buildings
Instance segmentation
Feature extraction
Annotations
Semantics
Amodal instance segmentation (AIS)
building extraction
dataset
multi size
multi view
occlusion
Journal
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