Extraction of City Roads Using Luojia 1-01 Nighttime Light Data

Wang, Lu-Yao; Zhang, Hao; Xu, Haiyan; Zhu, Anfeng; Fan, Hong; Wang, Yankun

doi:10.3390/app112110113

Cited by 8 publications

(3 citation statements)

References 48 publications

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“…In addition, in road extraction experiments of Lhasa, Wuhan, and Beijing, we also found that the greater the traffic flow was, the lower the accuracy of these three methods became. This phenomenon is similar to the conclusion drawn from the research on extracting roads by LJ-1 NTL data [32].…”

Section: Discussionsupporting

confidence: 89%

Research on Road Extraction Method Based on Sustainable Development Goals Satellite-1 Nighttime Light Data

et al. 2022

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Road information plays a fundamental role in many applications. However, at present, it is difficult to extract road information from the traditional nighttime light images in view of their low spatial and spectral resolutions. To fill the gap in high-resolution nighttime light (NTL) data, the Sustainable Development Goals Satellite-1(SDGSAT-1) developed by the Chinese Academy of Sciences (CAS) was successfully launched on 5 November 2021. With 40 m spatial resolution, NTL data acquired by the Glimmer Imager Usual (GIU) sensor on the SDGSAT-1 provide a new data source for road extraction. To evaluate the ability of SDGSAT-1 NTL data to extract road information, we proposed a new road extraction method named Band Operation and Marker-based Watershed Segmentation Algorithm (BO-MWSA). Comparing with support vector machine (SVM) and optimum threshold (OT) algorithms, the results showed that: (1) the F1 scores of the roads in the test area extracted by SVM, OT, and BO-MWSA were all over 70%, indicating that SDGSAT-1/GIU data could be used as a data source for road extraction. (2) The F1 score of road extraction by BO-MWSA is 84.65%, which is 11.02% and 9.43% higher than those of SVM and OT, respectively. In addition, the F1 scores of BO-MWSA road extraction in Beijing and Wuhan are both more than 84%, indicating that BO-MWSA is an effective method for road extraction using NTL imagery. (3) In road extraction experiments for Lhasa, Beijing, and Wuhan, the results showed that the greater the traffic flow was, the lower the accuracy of the extracted roads became. Therefore, BO-MWSA is an effective method for road extraction using SDGSAT-1 NTL data.

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Section: Discussionsupporting

confidence: 89%

Research on Road Extraction Method Based on Sustainable Development Goals Satellite-1 Nighttime Light Data

et al. 2022

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“…As one of the basic remote sensing satellite data, night-time light data (NTL) is an important data source for analyzing some urban interior problems (Liu et al, 2015), including population, GDP, and carbon emission, because of its advantage in reflecting urban activities via capturing the NTL brightness in the urban city (Zhao et al, 2015;Zhang et al, 2017;You et al, 2021), Additionally, it has also been shown by relevant studies that urban night-time light is also directly related to urban surface temperature, which also plays an important role in reflecting the urban heat island effect (Liu et al, 2021;Ren et al, 2022). Currently, there are three types of NTL data: DMSP/OLS (Elvidge et al, 2001;Small et al, 2013), NPP/VIIRS (Elvidge et al, 2021a;2021b), and Luojia-01 (Wang et al, 2021). NTL data are mainly used to identify urban centers and delineate urban boundaries; thresholds combined with NTL are used as the main tools, owing to their easier operation steps (Liu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Using Tencent User Location Data to Modify Night-Time Light Data for Delineating Urban Agglomeration Boundaries

Zhu

Chang

et al. 2022

Front. Environ. Sci.

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The study of urban agglomeration boundaries is helpful to understand the internal spatial structure of urban agglomeration, evaluate the development level of urban agglomeration, and thus, assist in the formulation of regional planning and policies. However, previous studies often used only static spatial elements to delineate the boundaries of urban agglomerations, ignoring the spatial connections within urban agglomerations. In this study, night-time light and Tencent user location data were evaluated separately and fused to delineate urban agglomeration boundaries from both static and dynamic spatial perspectives. Additionally, it has been shown in the study results that the accuracy of urban agglomeration boundary delineated by night-time light data is 84.90%, with Kappa coefficient as 0.6348. The accuracy delineated by Tencent user location data is 82.40%, with Kappa coefficient as 0.5637, while the accuracy delineated by data fusion is 92.70%, with Kappa coefficient as 0.7817. Therefore, it can be concluded that the fusion of night-time light and Tencent user location data had the highest accuracy in delineating urban agglomeration boundaries, which verified that the fusion of dynamic spatial elements on a single static spatial element can supplement the spatial connection of urban agglomeration. Our findings enrich the understanding of urban agglomerations, and the accurate delineation of urban agglomerations boundaries can aid urban agglomeration planning and management.

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“…Only a handful of studies have endeavored to employ NTL images for road extraction tasks, achieving some noteworthy results. For instance, Wang [18] harnessed Luojia 1-01 NTL imagery to extract roads in Wuhan through shape optimization and a thresholding approach, attaining a final extraction accuracy of 83.2%. However, the resolution of Luojia 1-01 NTL data is relatively low.…”

Section: Introductionmentioning

confidence: 99%

Road extraction through Yangwang-1 nighttime light data: A case study in Wenzhou, China

Zhu,

Hao,

Gang

et al. 2024

PLoS ONE

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The extraction of roadways from remote sensing imagery constitutes a pivotal task, with far-reaching implications across diverse domains such as urban planning, management of transportation systems, emergency response initiatives, and environmental monitoring endeavors. Satellite images captured during daytime have customarily served as the primary resource for this extraction process. However, the emergence of Nighttime Light (NTL) remote sensing data introduces an innovative dimension to this arena. The exploration of NTL data for road extraction remains in its nascent stage, and this study seeks to bridge this gap. We present a refined U-Net model (CA U-Net) integrated with Cross-Attention Mechanisms, meticulously designed to extract roads from Yangwang-1 NTL images. This model incorporates several enhancements, thereby improving its proficiency in identifying and delineating road networks. Through extensive experimentation conducted in the urban landscape of Wenzhou City, the model delivers highly accurate results, achieving an F1 score of 84.46%. These outcomes significantly surpass the performance benchmarks set by Support Vector Machines (SVM) and the Optimal Threshold (OT) method. This promising development paves the way towards maximizing the utility of NTL data for comprehensive mapping and analysis of road networks. Furthermore, the findings underscore the potential of utilizing Yangwang-1 data as a reliable source for road extraction and reaffirm the viability of deploying deep learning frameworks for road extraction tasks utilizing NTL data.

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Extraction of City Roads Using Luojia 1-01 Nighttime Light Data

Cited by 8 publications

References 48 publications

Research on Road Extraction Method Based on Sustainable Development Goals Satellite-1 Nighttime Light Data

Research on Road Extraction Method Based on Sustainable Development Goals Satellite-1 Nighttime Light Data

Using Tencent User Location Data to Modify Night-Time Light Data for Delineating Urban Agglomeration Boundaries

Road extraction through Yangwang-1 nighttime light data: A case study in Wenzhou, China

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