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Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 36773

Special Issue Editors

Prof. Dr. Qingling Zhang

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen Campus, Shenzhen 510055, China
Interests: urban remote sensing; digital image analysis; big remote sensing data analysis; nightlight remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Hongsheng Zhang

E-Mail Website
Guest Editor
Department of Geography, The University of Hong Kong, Hong Kong 999077, China
Interests: coastal ecosystems and sustainability; coastal remote sensing; land use/land cover change; multi-sensor data fusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Noam Levin

E-Mail Website
Guest Editor
Department of Geography, The Hebrew University of Jerusalem, Mount Scopus, Jerusalem 91905, Israel
Interests: urban remote sensing; nightlight remote sensing; remote sensing image analysis; GIS; spatial analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongchang Sun

E-Mail Website
Guest Editor
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Interests: urban remote sensing; urban sustainability; big earth data process and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the world has also witnessed rapid urbanization, with an increasing urban population that is projected to rise to 80% by 2050. The high density of urban areas makes them especially vulnerable to both the impacts of acute disasters and the effects of the changing climate.  It is thus critical that we address sustainability challenges facing cities by taking steps such as poverty reduction, disaster reduction and prevention, and climate change mitigation, environmental sustainability maintenance, and social inclusion measures. These efforts towards urban resilience not only help individuals, communities, and business cope with multiple stresses, but also allow for the exploitation of opportunities for transformational development, and are the main focus of many global agencies, such as the World Bank, UN, and GEO.

The urban resilience framework is multidimensional in nature, consisting of four core dimensions: leadership and strategy, health and well-being, economy and society, and infrastructure and environment. Remote sensing has been applied to monitor urban infrastructure and environments in various ways. With recent advances in remote sensing in terms of spatial, temporal, and spectral resolutions and data processing algorithms, remote sensing is expected to provide important observations and tools for monitoring, evaluating, and modeling urban resilience. To help global cities persevere through future challenges, while positively adapting and moving towards sustainability, this Special Issue calls for original research papers covering topics including, but not limited to:

  • Urban spatial structure and development;
  • Urban green space;
  • Implementation of new technologies toward resilient cities;
  • Urban transportation systems and development;
  • Urban infrastructure and building health monitoring;
  • Climate impacts on urban areas;
  • Urban flooding: prediction, monitoring, and mitigation;
  • Remote observations for resilient cities;
  • Urban heat island;
  • Urban carbon emissions;
  • Remote-sensing-based urban resilient index; 
  • Urban resilience evaluation and modeling with remote sensing data as well as other data;
  • Case studies;
  • Dedicated hardware and software solutions.

Prof. Dr. Qingling Zhang
Dr. Hongsheng Zhang
Prof. Dr. Noam Levin
Dr. Zhongchang Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban resilience
  • global change
  • change detection
  • multisource data fusion
  • signal processing and data mining
  • artificial intelligence
  • urban science
  • sustainability
  • resilient cities

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Published Papers (10 papers)

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16 pages, 7178 KiB  
Article
Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data
by Neel Chaminda Withanage, Kaifang Shi and Jingwei Shen
Remote Sens. 2023, 15(18), 4632; https://doi.org/10.3390/rs15184632 - 21 Sep 2023
Cited by 4 | Viewed by 1353
Abstract
It is crucial to evaluate the expansion of urban entities to implement sustainable urban planning strategies in China. Thus, this study attempted to extract and evaluate the growth of urban entities 270 prefecture cities in mainland China (2000–2020) using a novel approach based [...] Read more.
It is crucial to evaluate the expansion of urban entities to implement sustainable urban planning strategies in China. Thus, this study attempted to extract and evaluate the growth of urban entities 270 prefecture cities in mainland China (2000–2020) using a novel approach based on consistent night light images. After the urban entities were extracted, a rationality assessment was carried out to compare the derived urban entities with the LandScan population product, Landsat, and road network results. Additionally, the results were compared with other physical extent products, such as the Moderate Resolution Imaging Spectrometer (MODIS) and urban built-up area products (HE) products. According to the findings, the urban entities were basically consistent with the LandScan, road network, and HE and MODIS products. However, the urban entities more accurately reflected the concentration of human activities than did the impervious extents of the MODIS and HE products. At the prefecture levels, the area of urban entities increased from 8082 km2 to 74,417 km2 between 2000 and 2020, showing an average growth rate of 10.8% over those twenty years. As a reliable supplementary resource and guide for urban mapping, this research will inform new research on the K-means algorithm and on variations in NTL data brightness threshold dynamics at regional and global scales. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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17 pages, 5358 KiB  
Article
The Differences and Influence Factors in Extracting Urban Green Space from Various Resolutions of Data: The Perspective of Blocks
by Xiao Wei, Mengjun Hu and Xiao-Jun Wang
Remote Sens. 2023, 15(5), 1261; https://doi.org/10.3390/rs15051261 - 24 Feb 2023
Cited by 6 | Viewed by 1546
Abstract
The appropriate resolution has been confirmed to be crucial to the extraction of urban green space and the related research on ecosystem services. However, the factors affecting the differences between various resolutions of data in certain application scenarios are lacking in attention. To [...] Read more.
The appropriate resolution has been confirmed to be crucial to the extraction of urban green space and the related research on ecosystem services. However, the factors affecting the differences between various resolutions of data in certain application scenarios are lacking in attention. To fill the gap, this paper made an attempt to analyze the differences of various resolutions of data in green space extraction and to explore where the differences are reflected in the actual land unit, as well as the factors affecting the differences. Further, suggestions for reducing errors and application scenarios of different resolutions of data in related research are proposed. Taking a typical area of Nanjing as an example, data taken by DJI drone (0.1 m), GaoFen-1 (2 m) and Sentinel-2A (10 m) were selected for analysis. The results show that: (1) There were minimal differences in the green space ratio of the study area calculated by different resolutions of data on the whole, but when subdivided into each land use type and block, the differences were obvious; (2) The function, area and shape of the block, as well as the patch density and aggregation degree of the internal green space, had a certain impact on the differences. However, the specific impact varied when the block area was different; and (3) For the selection of the data source, the research purpose and application scenarios need to be comprehensively considered, including the function and attributes of the block, the distribution characteristics of green space, the allowable error limits and the budget. The present study highlighted the reasons of differences and hopefully it can provide a reference for the data selection of urban green space in the practical planning and design. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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20 pages, 5096 KiB  
Article
Automatic Extraction of Urban Impervious Surface Based on SAH-Unet
by Ruichun Chang, Dong Hou, Zhe Chen and Ling Chen
Remote Sens. 2023, 15(4), 1042; https://doi.org/10.3390/rs15041042 - 14 Feb 2023
Cited by 7 | Viewed by 2329
Abstract
Increases in the area of impervious surfaces have occurred with urbanization. Such surfaces are an important indicator of urban expansion and the natural environment. The automatic extraction of impervious surface data can provide useful information for urban and regional management and planning and [...] Read more.
Increases in the area of impervious surfaces have occurred with urbanization. Such surfaces are an important indicator of urban expansion and the natural environment. The automatic extraction of impervious surface data can provide useful information for urban and regional management and planning and can contribute to the realization of the United Nations Sustainable Development Goal 11—Sustainable Cities and Communities. This paper uses Google Earth Engine (GEE) high-resolution remote sensing images and OpenStreetMap (OSM) data for Chengdu, a typical city in China, to establish an impervious surface dataset for deep learning. To improve the extraction accuracy, the Small Attention Hybrid Unet (SAH-Unet) model is proposed. It is based on the Unet architecture but with attention modules and a multi-scale feature fusion mechanism. Finally, depthwise-separable convolutions are used to reduce the number of model parameters. The results show that, compared with other classical semantic segmentation networks, the SAH-Unet network has superior precision and accuracy. The final scores on the test set were as follows: Accuracy = 0.9159, MIOU = 0.8467, F-score = 0.9117, Recall = 0.9199, Precision = 0.9042. This study provides support for urban sustainable development by improving the extraction of impervious surface information from remote sensing images. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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28 pages, 9173 KiB  
Article
Patterns of Typical Chinese Urban Agglomerations Based on Complex Spatial Network Analysis
by Sijia Li, Huadong Guo, Zhongchang Sun, Zongqiang Liu, Huiping Jiang and Hongsheng Zhang
Remote Sens. 2023, 15(4), 920; https://doi.org/10.3390/rs15040920 - 7 Feb 2023
Cited by 6 | Viewed by 2342
Abstract
The two prerequisites for monitoring SDG11.A “support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning” are the classification of the urban–rural continuum and the extraction of spatial links. However, the complexity and [...] Read more.
The two prerequisites for monitoring SDG11.A “support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning” are the classification of the urban–rural continuum and the extraction of spatial links. However, the complexity and diversity of urban patch distribution make it difficult to achieve a global rapid assessment. Based on the self-developed high-resolution global impervious surface area 2021 (Hi-GISA 2021) product, this study combined the complex network with remote sensing technology to propose a new method to delineate and evaluate the pattern and inner spatial links of the urban–rural continuum for five typical urban agglomerations in China, including the Beijing–Tianjin–Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), the Greater Bay Area (GBAUA), the Chengdu–Chongqing urban agglomeration (CYUA), and the Middle Reaches of Yangtze River urban agglomeration (MRYRUA). The research results are in good agreement with Chinese government documents. First, the five urban agglomerations are all small-world networks with a low degree of overall polycentricity, and the urbanization degrees of GBAUA and YRDUA are higher than BTHUA, CYUA, and MRYRUA. Second, the imbalanced development of YRDUA is higher than the other regions, and the siphon effects of BTHUA and MRYRUA are more significant than YRDUA, CYUA, and GBAUA. Third, some multi-centers show significant siphon effects. The urbanization degree is highly correlated with the urbanization potential but not positively correlated with the degree of balanced development. The results can provide data, methods, and technical support for monitoring and evaluating SDG11.A. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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23 pages, 21129 KiB  
Article
SegDetector: A Deep Learning Model for Detecting Small and Overlapping Damaged Buildings in Satellite Images
by Zhengbo Yu, Zhe Chen, Zhongchang Sun, Huadong Guo, Bo Leng, Ziqiong He, Jinpei Yang and Shuwen Xing
Remote Sens. 2022, 14(23), 6136; https://doi.org/10.3390/rs14236136 - 3 Dec 2022
Cited by 9 | Viewed by 3345
Abstract
Buildings bear much of the damage from natural disasters, and determining the extent of this damage is of great importance to post-disaster emergency relief. The application of deep learning to satellite remote sensing imagery has become more and more mature in monitoring natural [...] Read more.
Buildings bear much of the damage from natural disasters, and determining the extent of this damage is of great importance to post-disaster emergency relief. The application of deep learning to satellite remote sensing imagery has become more and more mature in monitoring natural disasters, but there are problems such as the small pixel scale of targets and overlapping targets that hinder the effectiveness of the model. Based on the SegFormer semantic segmentation model, this study proposes the SegDetector model for difficult detection of small-scale targets and overlapping targets in target detection tasks. By changing the calculation method of the loss function, the detection of overlapping samples is improved and the time-consuming non-maximum-suppression (NMS) algorithm is discarded, and the horizontal and rotational detection of buildings can be easily and conveniently implemented. In order to verify the effectiveness of the SegDetector model, the xBD dataset, which is a dataset for assessing building damage from satellite imagery, was transformed and tested. The experiment results show that the SegDetector model outperforms the state-of-the-art (SOTA) models such as you-only-look-once (YOLOv3, v4, v5) in the xBD dataset with F1: 0.71, Precision: 0.63, and Recall: 0.81. At the same time, the SegDetector model has a small number of parameters and fast detection capability, making it more practical for deployment. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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19 pages, 5547 KiB  
Article
Analyzing the Spatially Heterogeneous Relationships between Nighttime Light Intensity and Human Activities across Chongqing, China
by Jihao Wu, Yue Tu, Zuoqi Chen and Bailang Yu
Remote Sens. 2022, 14(22), 5695; https://doi.org/10.3390/rs14225695 - 11 Nov 2022
Cited by 12 | Viewed by 2843
Abstract
Nighttime light (NTL) intensity is highly associated with the unique footprint of human activities, reflecting the development of socioeconomic and urbanization. Therefore, better understanding of the relationship between NTL intensity and human activities can help extend the applications of NTL remote sensing data. [...] Read more.
Nighttime light (NTL) intensity is highly associated with the unique footprint of human activities, reflecting the development of socioeconomic and urbanization. Therefore, better understanding of the relationship between NTL intensity and human activities can help extend the applications of NTL remote sensing data. Different from the global effect of human activities on NTL intensity discussed in previous studies, we focused more attention to the local effect caused by the spatial heterogeneity of human activities with the support of the multiscale geographically weighted regression (MGWR) model in this study. In particular, the Suomi National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) NTL data within Chongqing, China were taken as example, and the point of interest (POI) data and road network data were adopted to characterize the intensity of human activity type. Our results show that there is significant spatial variation in the effect of human activities to the NTL intensity, since the accuracy of fitted MGWR (adj.R2: 0.86 and 0.87 in 2018 and 2020, respectively; AICc: 4844.63 and 4623.27 in 2018 and 2020, respectively) is better than that of both the traditional ordinary least squares (OLS) model and the geographically weighted regression (GWR) model. Moreover, we found that almost all human activity features show strong spatial heterogeneity and their contribution to NTL intensity varies widely across different regions. For instance, the contribution of road network density is more homogeneous, while residential areas have an obviously heterogeneous distribution which is associated with house vacancy. In addition, the contributions of the commercial event and business also have a significant spatial heterogeneity distribution, but show a distinct decrement when facing the COVID-19 pandemic. Our study successfully explores the relationship between NTL intensity and human activity features considering the spatial heterogeneity, which aims to provide further insights into the future applications of NTL data. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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22 pages, 11240 KiB  
Article
Evaluation of Urban Spatial Structure from the Perspective of Socioeconomic Benefits Based on 3D Urban Landscape Measurements: A Case Study of Beijing, China
by Yujia Liu, Qingyan Meng, Jichao Zhang, Linlin Zhang, Mona Allam, Xinli Hu and Chengxiang Zhan
Remote Sens. 2022, 14(21), 5511; https://doi.org/10.3390/rs14215511 - 1 Nov 2022
Cited by 4 | Viewed by 3040
Abstract
Urban spatial structures (USS) play an essential role in urbanization. Understanding the impact of USS patterns on their socioeconomic benefits is crucial to evaluating urban structure quality. Previous studies have, primarily, relied on statistical data and have significant temporal consistency and spatial accuracy [...] Read more.
Urban spatial structures (USS) play an essential role in urbanization. Understanding the impact of USS patterns on their socioeconomic benefits is crucial to evaluating urban structure quality. Previous studies have, primarily, relied on statistical data and have significant temporal consistency and spatial accuracy limitations. Moreover, previous evaluation methods mainly determined the weight of indicators based on subjective assessments, such as the Delphi method, without integrating the actual socioeconomic benefits of complex urban systems. By measuring the two-dimensional (2D) urban functional landscape patterns and three-dimensional (3D) building forms of the city and considering the level of urban socioeconomic vitality as revealed by nighttime light intensity (NTLI), this study explores the influence of urban spatial structure on socioeconomic vitality. It provides a new perspective for evaluating the USS level. Furthermore, a comprehensive index, namely the Spatial Structure Socioeconomic Benefit Index (SSSBI), was constructed to quantify the socioeconomic benefits of USS. The results showed that (1) the impact of spatial structure on NTLI differs significantly with the distribution of urban functional landscape patterns and building forms. (2) The combined effect of any two spatial structure factors on NTLI was higher than the effect of each factor separately, indicating that multiple dimensions can improve urban spatial construction. (3) This study quantitatively extracts the characteristics of USS from multiple scales, which helps to find the optimal evaluation scale and build a scientific and objective evaluation model. The results showed that the USS assessment based on the SSSBI index is practical. This study could provide a reference for the government’s urban planning and land-use decisions. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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19 pages, 6753 KiB  
Article
Assessing the 2022 Flood Impacts in Queensland Combining Daytime and Nighttime Optical and Imaging Radar Data
by Noam Levin and Stuart Phinn
Remote Sens. 2022, 14(19), 5009; https://doi.org/10.3390/rs14195009 - 8 Oct 2022
Cited by 13 | Viewed by 13923
Abstract
In the Australian summer season of 2022, exceptional rainfall events occurred in Southeast Queensland and parts of New South Wales, leading to extensive flooding of rural and urban areas. Here, we map the extent of flooding in the city of Brisbane and evaluate [...] Read more.
In the Australian summer season of 2022, exceptional rainfall events occurred in Southeast Queensland and parts of New South Wales, leading to extensive flooding of rural and urban areas. Here, we map the extent of flooding in the city of Brisbane and evaluate the change in electricity usage as a proxy for flood impact using VIIRS nighttime brightness imagery. Scanning a wide range of possible sensors, we used pre-flood and peak-flood PlanetScope imagery to map the inundated areas, using a new spectral index we developed, the Normalized Difference Inundation Index (NDII), which is based on changes in the NIR reflectance due to sediment-laden flood waters. We compared the Capella-Space X-band/HH imaging radar data captured at peak-flood date to the PlanetScope-derived mapping of the inundated areas. We found that in the Capella-Space image, significant flooded areas identified in PlanetScope imagery were omitted. These omission errors may be partly explained by the use of a single-date radar image, by the X-band, which is partly scattered by tree canopy, and by the SAR look angle under which flooded streets may be blocked from the view of the satellite. Using VIIRS nightly imagery, we were able to identify grid cells where electricity usage was impacted due to the floods. These changes in nighttime brightness matched both the inundated areas mapped via PlanetScope data as well as areas corresponding with decreased electricity loads reported by the regional electricity supplier. Altogether we demonstrate that using a variety of optical and radar sensors, as well as nighttime and daytime sensors, enable us to overcome data gaps and better understand the impact of flood events. We also emphasize the importance of high temporal revisit times (at least twice daily) to more accurately monitor flood events. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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16 pages, 5466 KiB  
Article
Subsidence Monitoring and Mechanism Analysis of Anju Airport in Suining Based on InSAR and Numerical Simulation
by Ting Wang, Rui Zhang, Runqing Zhan, Age Shama, Mingjie Liao, Xin Bao, Liu He and Junyu Zhan
Remote Sens. 2022, 14(15), 3759; https://doi.org/10.3390/rs14153759 - 5 Aug 2022
Cited by 6 | Viewed by 2045
Abstract
The mountainous area of southwest China is characterized by significant topography and complex geological conditions, which pose great challenges to the airport’s site selection, construction, and safe operation. Suining Anju Airport, one of the key projects under construction in southwest China, is essential [...] Read more.
The mountainous area of southwest China is characterized by significant topography and complex geological conditions, which pose great challenges to the airport’s site selection, construction, and safe operation. Suining Anju Airport, one of the key projects under construction in southwest China, is essential in alleviating and dredging the air passenger flow in Sichuan Province. Because the overlying quaternary strata’s physical and mechanical properties, thickness, and distribution range are fairly different in the longitudinal and transverse directions, the Anju Airport’s foundation in the hilly area has typical inhomogeneity. Large-scale excavation and filling pose a challenge to the ground stability of the airport. To comprehensively monitor Anju Airport’s uneven ground subsidence during the construction period, this paper selected SAR image data collected by the Sentinel-1A satellite from May 2018 to June 2021 to extract time-series ground subsidence measurements based on the SBAS-InSAR method. Furthermore, based on the simulation of roadbed filling in the airport’s parallel slide fill area, the dynamic evolution analysis of soil stress field and internal subsidence caused by roadbed filling activities was carried out to further reveal the occurrence mechanism of ground subsidence. The monitoring results show that the subsidence centers of Anju Airport are mainly distributed in the filling areas, and the average annual subsidence is −20~−75 mm/yr from May 2018 to June 2021. Comparative analysis with in situ data indicates that the RMSE of InSAR monitoring results was ±6.12 mm. The numerical simulation shows that the subsidence of the airport parallel slide is mainly caused by a load of subgrade filling body and the compression of its weight. The results of this study can provide reference methodology and data support for the construction and future safe operation of Suining Anju Airport. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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14 pages, 4164 KiB  
Technical Note
A Land Cover Background-Adaptive Framework for Large-Scale Road Extraction
by Yu Li, Hao Liang, Guangmin Sun, Zifeng Yuan, Yuanzhi Zhang and Hongsheng Zhang
Remote Sens. 2022, 14(20), 5114; https://doi.org/10.3390/rs14205114 - 13 Oct 2022
Cited by 5 | Viewed by 2105
Abstract
Background: Road network data are crucial in various applications, such as emergency response, urban planning, and transportation management. The recent application of deep neural networks has significantly boosted the efficiency and accuracy of road network extraction based on remote sensing data. However, most [...] Read more.
Background: Road network data are crucial in various applications, such as emergency response, urban planning, and transportation management. The recent application of deep neural networks has significantly boosted the efficiency and accuracy of road network extraction based on remote sensing data. However, most existing methods for road extraction were designed at local or regional scales. Automatic extraction of large-scale road datasets from satellite images remains challenging due to the complex background around the roads, especially the complicated land cover types. To tackle this issue, this paper proposes a land cover background-adaptive framework for large-scale road extraction. Method: A large number of sample image blocks (6820) are selected from six different countries of a wide region as the dataset. OpenStreetMap (OSM) is automatically converted to the ground truth of networks, and Esri 2020 Land Cover Dataset is taken as the background land cover information. A fuzzy C-means clustering algorithm is first applied to cluster the sample images according to the proportion of certain land use types that obviously negatively affect road extraction performance. Then, the specific model is trained on the images clustered as abundant with that certain land use type, while a general model is trained based on the rest of the images. Finally, the road extraction results obtained by those general and specific modes are combined. Results: The dataset selection and algorithm implementation were conducted on the cloud-based geoinformation platform Google Earth Engine (GEE) and Google Colaboratory. Experimental results showed that the proposed framework achieved stronger adaptivity on large-scale road extraction in both visual and statistical analysis. The C-means clustering algorithm applied in this study outperformed other hard clustering algorithms. Significance: The promising potential of the proposed background-adaptive network was demonstrated in the automatic extraction of large-scale road networks from satellite images as well as other object detection tasks. This search demonstrated a new paradigm for the study of large-scale remote sensing applications based on deep neural networks. Full article
(This article belongs to the Special Issue Urban Resilience with Remote Sensing - Observation, Measurement, Evaluation and Applications)
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