Large-scale integration of remotely sensed and GIS road networks: A full image-vector conflation approach based on optimization and deep learning

IF 7.1 1区 地球科学 Q1 ENVIRONMENTAL STUDIES Computers Environment and Urban Systems Pub Date : 2024-08-20 DOI:10.1016/j.compenvurbsys.2024.102174
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引用次数: 0

Abstract

Road networks play an important role in the sustainable development of human society. Conventionally, there are two sources of road data acquisition: road extraction from Remote Sensing (RS) imagery and GIS based map production. Each method has its limitations. The RS road extraction methods are primarily raster-based and the extracted roads are not directly usable in GIS due to their fragmented and noisy nature, while vector-based methods cannot utilize rich raster information. Further more, the vector and raster data can have discrepancies for various reasons. Efficient road data production requires an image-vector conflation process that can match and combine raster and vector-based road data automatically.

In this study, we propose a full image-vector conflation framework that directly integrates image and vector road data by appropriately transforming extracted roads from imagery and establishing a match relation between these roads and a credible target GIS road dataset. Based on analyzing these match relations, we propose new metrics for measuring the degree of agreement between the raster and vector road data. The proposed framework combines state-of-the-art deep learning methods for image segmentation and optimization-based models for object matching. We prepared a large-scale high-resolution road dataset covering two counties in Kansas, US. Using trained models from one of the two counties, we were able to extract road segments in the other county and match them to the TIGER/Line roads.

Our experiments show that conventional performance metrics for road extraction (e.g. IoU) are insufficient for measuring the degree of agreement between image and vector roads as they are pixel-based and are too sensitive to spatial displacement. Instead, the newly defined vector-based agreement metrics are needed for image-vector conflation purposes. Experiments show that, by the vector-based metrics, nearly 90% of GIS road lengths in the study area were extracted and over 90% of extracted roads matched the target GIS roads. The new framework streamlines raster-vector conflation of roads and can potentially expedite relevant geospatial analyses regarding change detection, disaster monitoring and GIS data production, among others.

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大规模整合遥感和 GIS 道路网络:基于优化和深度学习的全图像矢量混合方法
道路网络在人类社会的可持续发展中发挥着重要作用。传统的道路数据采集方法有两种:从遥感(RS)图像中提取道路数据和基于地理信息系统(GIS)的地图制作。每种方法都有其局限性。遥感图像的道路提取方法主要是基于栅格的,提取的道路因其破碎和噪声大的特点而无法直接用于地理信息系统,而基于矢量的方法则无法利用丰富的栅格信息。此外,由于各种原因,矢量数据和栅格数据可能存在差异。在本研究中,我们提出了一个完整的图像-矢量混合框架,通过对从图像中提取的道路进行适当转换,并在这些道路和可信的目标 GIS 道路数据集之间建立匹配关系,从而直接整合图像和矢量道路数据。在分析这些匹配关系的基础上,我们提出了衡量栅格和矢量道路数据一致性程度的新指标。所提出的框架结合了最先进的图像分割深度学习方法和基于优化的对象匹配模型。我们准备了一个覆盖美国堪萨斯州两个县的大规模高分辨率道路数据集。我们的实验表明,道路提取的传统性能指标(如 IoU)不足以衡量图像与矢量道路之间的一致程度,因为它们是基于像素的,对空间位移过于敏感。相反,新定义的基于矢量的一致性度量则需要用于图像与矢量的混合。实验表明,通过基于矢量的指标,研究区域内近 90% 的 GIS 道路长度被提取出来,超过 90% 的提取道路与目标 GIS 道路相匹配。新框架简化了道路的栅格-矢量混合,有可能加快有关变化检测、灾害监测和 GIS 数据生产等方面的相关地理空间分析。
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来源期刊
CiteScore
13.30
自引率
7.40%
发文量
111
审稿时长
32 days
期刊介绍: Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.
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