Urban Area Extraction Research Articles

LiDAR-Based Urban Three-Dimensional Rail Area Extraction for Improved Train Collision Warnings.

The intrusion of objects into track areas is a significant issue affecting the safety of urban rail transit systems. In recent years, obstacle detection technology based on LiDAR has been developed to identify potential issues, in which accurately extracting the track area is critical for segmentation and collision avoidance. However, because of the sparsity limitations inherent in LiDAR data, existing methods can only segment track regions over short distances, which are often insufficient given the speed and braking distance of urban rail trains. As such, a new approach is developed in this study to indirectly extract track areas by detecting references parallel to the rails (e.g., tunnel walls, protective walls, and sound barriers). Reference point selection and curve fitting are then applied to generate a reference curve on either side of the track. A centerline is then extrapolated from the two curves and expanded to produce a 2D track area with the given size specifications. Finally, the 3D track area is acquired by detecting the ground and removing points that are either too high or too low. The proposed technique was evaluated using a variety of scenes, including tunnels, elevated sections, and level urban rail transit lines. The results showed this method could successfully extract track regions from LiDAR data over significantly longer distances than conventional algorithms.

Analysis of Spatial and Temporal Changes and Drivers of Urban Sprawl in Xinjiang Based on Integrated DMSP-OLS and NPP-VIIRS Data

The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution.

Urban area extraction based on polarimetric features using DP-UNet

Urban area extraction based on polarimetric features using DP-UNet

Comparative Analysis of Urban Area Extraction Using Different Classification Methods: A Case Study of Ghorahi Sub-Metropolitan City, Nepal

Comparative Analysis of Urban Area Extraction Using Different Classification Methods: A Case Study of Ghorahi Sub-Metropolitan City, Nepal

An commercial area extraction approach using time series nighttime light remote sensing data——Take Wuhan city as a case

The degree of nighttime illumination in a region is closely related to its economic development, therefore, urban commercial areas can be located based on the intensity of the nighttime light. Aiming at the problem of discontinuous and incomparable brightness of nighttime remote sensing images in different periods, a method of urban commercial area extraction based on temporal nighttime remote sensing data is proposed. Firstly, multi- nighttime remote sensing image correction was carried out based on the invariant target region method, and stable time series NPP/VIIRS data set was generated. Secondly, the impervious water surface was extracted by three index synthesis method to obtain the candidate urban commercial area using Landsat-8 image. Finally, multi-scale segmentation and unsupervised classification are carried out on the temporal nighttime remote sensing data set of the candidate area to realize the extraction of urban commercial area. Taking Wuhan city as an example, the method in this paper is used to extract and verify the urban commercial area. The effectiveness and accuracy of the method are verified according to the commercial planning map of Wuhan city and previous studies.

Satellite Monitoring of the Urban Expansion in the Pearl River–Xijiang Economic Belt and the Progress towards SDG11.3.1

Quantitative analysis of the spatiotemporal pattern of urban expansion and forecasting of the progress towards SDG11.3.1 are of great significance for the promotion of sustainable urban development. This study employed the spatiotemporal normalized threshold method to extract urban built-up areas in the Pearl River–Xijiang Economic Belt based on night-time light data and investigated the intricate patterns of urban expansion from 2000 to 2020. Then, the historical trends of the SDG11.3.1 indicators within the economic belt were evaluated, and future urban built-up areas were predicted based on the SSP1 scenario. The results indicate the following: (1) Built-up area extraction has an overall accuracy that exceeds 97% and G-mean values that all surpass 82%, indicating the high accuracy of the method. (2) The Pearl River–Xijiang Economic Belt demonstrates evident urban expansion trends, albeit with uneven development. The urban area of the economic belt has expanded from 1020.29 km2 to 3826.87 km2, the expansion direction of each city is different, and the center of gravity of the economic belt has moved to the southeast. (3) During the period from 2008 to 2020, the entire economic belt experienced a situation where the urban expansion rate was lower than the population growth rate, and there was an imbalance in urban development (LCRPGR = 0.33). However, looking ahead to the period from 2020 to 2030, the average LCRPGR for the entire economic belt shows a significant upward trend, approaching the ideal state of sustainable development (LCRPGR ≈ 1).

Automatic built-up area extraction by feature-level fusion of Luojia 1–01 nighttime light and Sentinel satellite imageries in Google Earth Engine

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales.

Extraction of Urban Built-Up Areas Using Nighttime Light (NTL) and Multi-Source Data: A Case Study in Dalian City, China

The rapid urban development associated with China’s reform and opening up has been the source of many urban problems. To understand these issues, it is necessary to have a deep understanding of the distribution of urban spatial structure. Taking the six districts of Dalian as an example, in this study, we integrated the enhanced vegetation index, points of interest, and surface temperature data into night light data. Furthermore, herein, we analyze the kernel density of the points of interest and construct three indices using image geometric mean: a human settlement index (HSI), a HSI-POI (HP) index, and a HSI-POI-LST (HPL) index. Using a support vector machine to identify the land type in Dalian’s built-up area, 1000 sampling points were created for verification. Then, the threshold boundary corresponding to the highest overall accuracy of each index and kappa coefficient was selected. The relevant conclusions are as follows: As compared with the other three types of data, the HPL index constructed in this study exhibited natural and social attributes, and the built-up area extracted using this method had the highest accuracy, a high image spatial resolution, and was able to overcome the omission issues observed when using one or two data sources. In addition, this method produces richer spatial details of the actual built-up area and provides more choices for assessing small-scale urban built-up areas in future research.

Assessing the Spatiotemporal Dynamics of Vegetation Coverage in Urban Built-Up Areas

As the main carbon sink in the carbon cycle process, vegetation is an important support for achieving “carbon peaking” and “carbon neutrality. How does the vegetation coverage of urban built-up areas change in the process of urbanization in China? Taking advantage of Landsat remote sensing data, we extract urban built-up areas, calculate the Normalized Difference Vegetation Index (NDVI), estimate fractional vegetation cover (FVC), and analyze the temporal and spatial dynamics of vegetation coverage in built-up areas of provincial capitals from the national and individual city levels in China. Major conclusions are as follows: (1) The FVC in the urban built-up areas has increased by 7.97%, and the urban green space has gradually changed from the “green core” distribution model to the “green vein” distribution mode. (2) The disparities in FVC of built-up areas of provincial capitals are notable, presumably due to a variety of factors that include the natural geographic environment, economic development level, built-up area expansion, land type conversion, afforestation of greening policy, etc. (3) Not just simply raise or lower, the FVC curves in the built-up areas of provincial capital cities present oscillating patterns with gradually weakening amplitude. Our study is expected to provide scientific references of an important theoretical basis for urban ecological construction and practical support for promoting the harmonious development of urban residents and urban environments in China.

Spatiotemporal Evolution of West Africa’s Urban Landscape Characteristics Applying Harmonized DMSP-OLS and NPP-VIIRS Nighttime Light (NTL) Data

Investigating urban expansion patterns aids in the management of urbanization and in ameliorating the socioeconomic and environmental issues associated with economic transformation and sustainable development. Applying Harmonized Defense Meteorological Satellite Program-Operational Line-scan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership-Visible Infrared Imagery Radiometer Suite (NPP-VIIRS) Nighttime Light (NTL) data, this paper investigated the characteristics of urban landscape in West Africa. Using the harmonized NTL data, spatial comparison and empirical threshold methods were employed to detect urban changes from 1993 to 2018. We examined the rate of urban change and calculated the direction of the urban expansion of West Africa using the center-of-gravity method for urban areas. In addition, we used the landscape expansion index method to assess the processes and stages of urban growth in West Africa. The accuracy of urban area extraction based on NTL data were R 2 = 0.8314 in 2000, R 2 = 0.8809 in 2006, R 2 = 0.9051 in 2012 for the DMSP-OLS and the simulated NPP-VIIRS was R 2 = 0.8426 in 2018, by using Google Earth images as validation. The results indicated that there was a high rate and acceleration of urban landscapes in West Africa, with rates of 0.016 0, 0.017 3, 0.018 9, and 0.068 6, and accelerations of 0.31, 0.42, 0.54, and 0.90 for the periods of 1998-2003, 2003-2008, 2008-2013, and 2013-2018, respectively. The expansion direction of urban agglomeration in West Africa during 1993-2018 was mainly from the coast to inland. However, cities located in the Sahel Region of Africa and in the middle zone expanded from north to south. Finally, the results showed that the urban landscape of West Africa was mainly in a scattered and disordered 'diffusion' process, whereas only a few cities located in coastal areas experiencing the process of 'coalescence' according to urban growth phase theory. This study provides urban planners with relevant insights for the urban expansion characteristics of West Africa.

An STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data.

A change in an urban built-up area can reflect the process of urbanization and the development of a city. At present, multi-source remote sensing data extraction of built-up areas based on the human settlement index (HSI) has achieved relatively good results but the existence of noise, such as light spillover in the night-time light remote sensing data, seriously affects the accuracy of the HSI. In this paper, a high-precision human settlement index (STP-HSI) method based on spatio-temporal remote sensing and point-of-interest (POI) data is presented to improve the classification accuracy in urban built-up areas extractions. First, to correct light spillover, a new night-time light index the fuzzy c-means spatio-temporal point (FCM-STP) based on fuzzy c-means clustering is proposed, which integrates the spatio-temporal characteristics and uses night light video imaging data from Luojia-1 and POI data. Then, based on the FCM-STP index, the HSI is updated to the STP-HSI index. Finally, a random forest algorithm is used to extract the urban built-up areas, and the random forest feature database is composed of normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and STP-HSI index features and texture features. To develop and evaluate the accuracy of the new method for built-up areas extraction with multi-source data, three test sites located in the cities of China (Guangzhou, Xiamen and Nanjing) are used. The experimental results show that our method outperforms the single-source multi-spectral (Landsat 8) data extraction results, the overall accuracy is improved by up to 7.52%, and the kappa coefficient is improved by up to 14%. Compared with the HSI index, the maximum contribution rates of the STP-HSI increased by 25.74%. These experimental results show that the method in this paper is feasible.

Extraction of Urban Built-Up Areas Based on Data Fusion: A Case Study of Zhengzhou, China

Urban built-up areas are not only the spatial carriers of urban activities but also the direct embodiment of urban expansion. Therefore, it is of great practical significance to accurately extract urban built-up areas to judge the process of urbanization. Previous studies that only used single-source nighttime light (NTL) data to extract urban built-up areas can no longer meet the needs of rapid urbanization development. Therefore, in this study, spatial location big data were first fused with NTL data, which effectively improved the accuracy of urban built-up area extraction. Then, a wavelet transform was used to fuse the data, and multiresolution segmentation was used to extract the urban built-up areas of Zhengzhou. The study results showed that the precision and kappa coefficient of urban built-up area extraction by single-source NTL data were 85.95% and 0.7089, respectively, while the precision and kappa coefficient of urban built-up area extraction by the fused data are 96.15% and 0.8454, respectively. Therefore, after data fusion of the NTL data and spatial location big data, the fused data compensated for the deficiency of single-source NTL data in extracting urban built-up areas and significantly improved the extraction accuracy. The data fusion method proposed in this study could extract urban built-up areas more conveniently and accurately, which has important practical value for urbanization monitoring and subsequent urban planning and construction.

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

The importance of small urban green areas has increased in the context of rapid urbanization and the densification of the urban tissue. The analysis of these areas through remote sensing has been limited due to the low spatial resolution of freely available satellite images. We propose a timeseries analysis on 3 m resolution Planet images, using GEOBIA and vegetation indices, with the aim of extracting and assessing the quality of small urban green areas in two different climatic and biogeographical regions: temperate (Bucharest, Romania) and mediterranean (Athens, Greece). Our results have shown high accuracy (over 91%) regarding the extraction of small urban green areas in both cities across all the analyzed images. The timeseries analysis showed consistency with respect to location for around 55% of the identified surfaces throughout the entire period. The vegetation indices registered higher values in the temperate region due to the vegetation characteristics and city plan of the two cities. For the same reasons, the increase in the vegetation density and quality, as a result of the distance from the city center, and the decrease in the density of built-up areas, is more obvious in Athens. The proposed method provides valuable insights into the distribution and quality of small urban green areas at the city level and can represent the basis for many analyses, which is currently limited by poor spatial resolution.

A New Approach to Monitoring Urban Built-Up Areas in Kunming and Yuxi from 2012 to 2021: Promoting Healthy Urban Development and Efficient Governance.

With the rapid expansion of urban built-up areas in recent years, accurate and long time series monitoring of urban built-up areas is of great significance for healthy urban development and efficient governance. As the basic carrier of urban activities, the accurate monitoring of urban built-up areas can also assist in the formulation of urban planning. Previous studies on urban built-up areas mainly focus on the analysis of a single time section, which makes the extraction results exist with a certain degree of contingency. In this study, a U-net is used to extract and monitor urban built-up areas in the Kunming and Yuxi area from 2012 to 2021 based on nighttime light data and POI_NTL (Point of Interest_Nighttime light) data. The results show that the highest accuracy of single nighttime light (NTL) data extraction was 89.31%, and that of POI_NTL data extraction was 95.31%, which indicates that data fusion effectively improves the accuracy of built-up area extraction. Additionally, the comparative analysis of the results of built-up areas and the actual development of the city shows that NTL data is more susceptible to urban emergencies in the extraction of urban built-up areas, and POI (Point of interest) data is subject to the level of technology and service available in the region, while the combination of the two can avoid the occasional impact of single data as much as possible. This study deeply analyzes the results of extracting urban built-up areas from different data in different periods and obtains the feasible method for the long time sequence monitoring of urban built-up areas, which has important theoretical and practical significance for the formulation of long-term urban planning and the current high-quality urban development.

Shannon Entropy-based urban spatial fragmentation to ensure sustainable development of the urban coastal city: A case study of Semarang, Indonesia

This study examines the extent to which the Shannon Entropy Index (H) can be used to ensure urban growth sustainability by measuring the spatial dispersion pattern of a built-up area. The Shannon Entropy Index was calculated based on the proximities to the city center (HCC) and the main road (HMR), which divide the study area into 17 zones. Data from Landsat 5- TM Imagery 2011 and Landsat 8- OLI Imagery 2016 and 2021 were used to extract urban built-up areas through image classification using the Support Vector Machine (SVM) Algorithm and measure their growth over time. The results show the normalized Shannon Entropy Index for the proximities to the city center (HCC′) and the main road (HMR′). HCC′ was found to be 0.93 in 2021 and increased by 0.44 during 2011–2021, while HMR′ was recorded to be 0.68 in 2021 and increased by 0.27 during 2011–2021, indicating that urban development in Semarang City has become increasingly fragmented. Among the 17 existing zones, the peri-urban areas of southeast part zones in Semarang (III-BP and IV-AP) were found to have the largest relative Shannon Entropy Index (H') of 0.98 and 0.96, respectively, indicating that this area is developing irregularly and spreading out compared to the city center, which is unsustainable built-up area growth. We call for an integrated space utilization control strategy between the city center and the peri-urban area. Our study demonstrates the capacity of using the Shannon Entropy Index to detect potential areas of unsustainable development in a coastal city with diverse development dynamics, which can also be applied in other coastal cities to prevent unsustainable development.

A GloVe Model for Urban Functional Area Identification Considering Nonlinear Spatial Relationships between Points of Interest

As cities continue to grow, the functions of urban areas change and problems arise from previously constructed urban planning schemes. Hence, the actual distribution of urban functional areas needs to be confirmed. POI data, as a representation of urban facilities, can be used to mine the spatial correlation within the city. Therefore it has been widely used for urban functional area extraction. Previous studies are mostly devoted to mining POI linear location relationships and do not comprehensively mine POI spatial information, such as spatial interaction information. This results in less accurate modeling of the relationship between POI-based and urban function types. In addition, they all use Euclidean distance for proximity assessment, which is not realistic. This paper proposes an urban functional area identification method that considers the nonlinear spatial relationship between POIs. First, POI adjacency is determined according to road network constraints, which forms the basis of a co-occurrence matrix. Then, a Global Vectors (GloVe) model is used to train POI category vectors and the feature vectors for each basic research unit are obtained using weighted averages. This is followed by clustering analysis, which is realized by a K-Means++ algorithm. Lastly, the functional areas are labeled according to the POI category ratio, enrichment factors, and mobile phone signal heat data. The model was tested experimentally, using core areas of Zhengzhou City in China as an example. When the results were compared with a Baidu map, we confirmed that making full use of nonlinear spatial relationships between POIs delivers high levels of identification accuracy for urban functional areas.

The Impact of Urbanization on Extreme Climate Indices in the Yangtze River Economic Belt, China

Urbanization has been proven to be a critical factor in modifying local or regional climate characteristics. This research aims to examine the impact of urbanization on extreme climate indices in the Yangtze River Economic Belt (YREB), China, by using meteorological observation data from 2000 to 2019. Three main steps are involved. First, a clustered threshold method based on remote-sensing nighttime light data is used to extract urban built-up areas, and urban and rural meteorological stations can be identified based on the boundary of urban built-up areas. Nonparametric statistical tests, namely, the Mann–Kendall test and Sen’s slope, are then applied to measure the trend characteristics of extreme climate indices. Finally, the urbanization contribution rate is employed to quantify the impact of urbanization on extreme climate indices. The results indicate that urbanization has a more serious impact on extreme temperature indices than on extreme precipitation indices in the YREB. For extreme temperature indices, urbanization generally causes more (less) frequent occurrence of warm (cold) events. The impact of urbanization on different extreme temperature indices has heterogeneous characteristics, including the difference in contamination levels and spatial variation of the impacted cities. For extreme precipitation indices, only a few cities impacted by urbanization are detected, but among these cities, urbanization contributes to increasing the trend of all indices.

Extraction of Urban Built-Up Area Based on Deep Learning and Multi-Sources Data Fusion—The Application of an Emerging Technology in Urban Planning

With the rapid expansion of urban built-up areas in recent years, it has become particularly urgent to develop a fast, accurate and popularized urban built-up area extraction method system. As the direct carrier of urban regional relationship, urban built-up area is an important reference to judge the level of urban development. The accurate extraction of urban built-up area plays an important role in formulating scientific planning thus to promote the healthy development of both urban area and rural area. Although nighttime light (NTL) data are used to extract urban built-up areas in previous studies, there are certain shortcomings in using NTL data to extract urban built-up areas. On the other hand, point of interest (POI) data and population migration data represent different attributes in urban space, which can both assist in modifying the deficiencies of NTL data from both static and dynamic spatial elements, respectively, so as to improve the extraction accuracy of urban built-up areas. Therefore, this study attempts to propose a feasible method to modify NTL data by fusing Baidu migration (BM) data and POI data thus accurately extracting urban built-up areas in Guangzhou. More accurate urban built-up areas are extracted using the method of U-net deep learning network. The maximum built-up area extracted from the study is 1103.45 km2, accounting for 95.21% of the total built-up area, and the recall rate is 0.8905, the precision rate is 0.8121, and the F1 score is 0.8321. The results of using POI data and BM data to modify NTL data to extract built-up areas have not been significantly improved due to the fact that the more data get fused, the more noise there would be, which would ultimately affect the results. This study analyzes the feasibility and insufficiency of using big data to modify NTL data through data fusion and feature extraction system, which has important theoretical and practical significance for future studies on urban built-up areas and urban development.

Analyzing the Dynamic Spatiotemporal Changes in Urban Extension across Zhejiang Province Using NPP-VIIRS Nighttime Light Data

Nighttime light remote sensing technologies provide methods for studying spatiotemporal changes in urban areas. In this research, we study the changes in the urban zone in Zhejiang Province based on NPP/VIIRS nighttime light data. Moreover, we propose a methodology to extract urban zones through a buffer threshold analysis method and apply the standard deviation ellipse, urban scale increment, “dual-core” primacy and urban-scale Gini index to uncover the evolution of urban dynamics in Zhejiang Province. The results show that the highest overall urban area extraction accuracy was 95.9%; the highest Kappa coefficient was 91%. The nighttime light intensity changes observed in most cities in Zhejiang Province reflected three periods: “high-speed growth” from 2012–2014, “low-speed growth” from 2014–2018 and “high-speed growth” from 2019–2020. The growth rates observed during the 2019–2020 “high-speed growth” period exceeded those of the 2012–2014 period. Over nine years, the growth rates of the total nighttime light values in all cities ranged from 40% to 319%. Third-tier cities such as Quzhou and Lishui showed significant increases. Second-tier cities, such as Jinhua, Huzhou and Taizhou, had growth rates over 100%. From 2012–2014, the development rates increased in cities in southern Zhejiang Province, such as in Wenzhou and Taizhou, thus promoting a southward shift in the center of gravity. After 2014, the development rates increased in cities in northern Zhejiang Province, such as Hangzhou, Ningbo and Jiaxing, thus promoting a northward shift in the center of gravity, with the center stabilizing in the Keqiao District-Yuecheng District of Shaoxing. According to the changes observed in the “dual-core” primacy and urban-scale Gini index results derived from 2012 to 2020, the development of cities in Zhejiang Province has become more balanced over the past nine years.

Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas

Changes in the spatial expansion of urban built-up areas are of great significance for the analysis of China’s urbanization process and economic development. Nighttime light data can be used to extract urban built-up areas in a large-scale and long-time series. In this article, we introduced the UNet model, a semantic segmentation network, as a base architecture, added spatial attention and channel attention modules to the encoder part to improve the boundary integrity and semantic consistency of the change feature map, and constructed an urban built-up area extraction model—CBAM_UNet. Also, we used this model to extract urban built-up areas from 2012 to 2021 and analyzed the spatial and temporal expansion of China’s urban built-up areas in terms of expansion speed, expansion intensity, expansion direction, and gravity center migration. In the last decade, the distribution pattern of urban built-up areas in China has gradually changed from “center” to “periphery-networked” distribution pattern. It reveals a trend from agglomeration to the dispersion of urban built-up areas in China. It provides a reference for China’s urban process and its economic development.