Built-up Area Extraction Research Articles

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.

Machine learning based combinatorial analysis for land use and land cover assessment in Kyiv City (Ukraine)

The main goal of this study is to evaluate different models for further improvement of the accuracy of land use and land cover (LULC) classification on Google Earth Engine using random forest (RF) and support vector machine (SVM) learning algorithms. Ten indices, namely normalized difference vegetation index, normalized difference soil index, index-based built-up index, biophysical composition index, built-up area extraction index (BAEI), urban index, new built-up index, band ratio for built-up area, bare soil index, and normalized built up area index, were used as input parameters for the machine learning algorithms to improve classification accuracy. The combinatorial analysis of the Sentinel-2 bands and the aforementioned indices allowed us to create four combinations based on surface reflectance characteristics. The study includes data from April 2020 to September 2021 and April 2022 to June 2022. The multitemporal Sentinel-2 data with spatial resolutions of 10 m were used to determine the LULC classification. The major land use classes such as water, forest, grassland, urban areas, and other lands were obtained. Generally, the RF algorithm showed higher accuracy than the SVM. The overall accuracy for RF and SVM was 86.56% and 84.48%, respectively, and the mean Kappa was 0.82 and 0.79, respectively. Using the combination 2 with the RF algorithm and combination 4 with the SVM algorithm for LULC classification was more accurate. The additional use of vegetation indices allowed to increase in the accuracy of LULC classification and separate classes with similar reflection spectra.

Built-Up Area Extraction Combing Densely Connected Dual-Attention Network and Multiscale Context

Built-Up Area Extraction Combing Densely Connected Dual-Attention Network and Multiscale Context

MANAGING, ASSESSING AND MONITORING URBAN SPRAWL USING REMOTE SENSING; CHANGE DETECTION, URBAN EXPANSION INTENSITY INDEX (UEII), AND SHANNON ENTROPY: A CASE STUDY OF AMMAN CITY, JORDAN (1990–2022)

Mapping and monitoring of land use land cover (LULC) changes in Amman city are important for sustainable regional development, urban planning, and environmental and land use management. To comprehend the temporal and the spatial variability of LULC change, six time-period satellite images of the study area have been examined based on remote sensing (RS) and geographic information system (GIS) techniques. The changes in LULC patterns of Amman city were monitored for the periods 1990-1995, 1995-1999, 1999-2004, 2004-2013, 2013-2017 and 2017-2022. Employing Spatio-temporal Landsat TM and ETM imagery acquired from 1990 to 2022 to comprehend, detect, and quantify the spatial pattern of urban sprawl. Supervised classification was used to extract built-up areas and measure changes in urban land-use class across the metropolis. The result of the urban expansion intensity index shows that urban expansion hits its peak between 2013 and 2017, with a 1.75 index resulting in high-speed development between the time frames. Shannon’s entropy approach was quantified to detect the urban sprawl phenomenon. The results showed that the Relative Shannon entropy values are above 0.5, indicating dispersedly spread-urban sprawl. Land Absorption Coefficient (LAC) and Land Consumption Ratio (LCR) were also used. A strong positive correlation between road networks and urban sprawl (0.7) indicates that urbanization plays a major role in the occurrence of urban sprawl. The findings obtained from this work would provide urban planners and decision-makers in Amman the tools and data they need, to implement planning interventions for sustainable future development and to mitigate the impacts of urban sprawl on different aspects on the environment.

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.

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.

Built-Up Area Extraction from GF-3 SAR Data Based on a Dual-Attention Transformer Model

Built-up area (BA) extraction using synthetic aperture radar (SAR) data has emerged as a potential method in urban research. Currently, typical deep-learning-based BA extractors show high false-alarm rates in the layover areas and subsurface bedrock, which ignore the surrounding information and cannot be directly applied to large-scale BA mapping. To solve the above problems, a novel transformer-based BA extraction framework for SAR images is proposed. Inspired by SegFormer, we designed a BA extractor with multi-level dual-attention transformer encoders. First, the hybrid dilated convolution (HDC) patch-embedding module keeps the surrounding information of the input patches. Second, the channel self-attention module is designed for dual-attention transformer encoders and global modeling. The multi-level structure is employed to produce the coarse-to-fine semantic feature map of BAs. About 1100 scenes of Gaofen-3 (GF-3) data and 200 scenes of Sentinel-1 data were used in the experiment. Compared to UNet, PSPNet, and SegFormer, our model achieved an 85.35% mean intersection over union (mIoU) and 94.75% mean average precision (mAP) on the test set. The proposed framework achieved the best results in both mountainous and plain terrains. The experiments using Sentinel-1 shows that the proposed method has a good generalization ability with different SAR data sources. Finally, the BA map of China for 2020 was obtained with an overall accuracy of about 86%, which shows high consistency with the global urban footprint. The above experiments proved the effectiveness and robustness of the proposed framework in large-scale BA mapping.

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.

Built-Up Area Mapping for the Greater Bay Area in China from Spaceborne SAR Data Based on the PSDNet and Spatial Statistical Features

Built-up areas (BAs) information acquisition is essential to urban planning and sustainable development in the Greater Bay Area in China. In this paper, a pseudo-Siamese dense convolutional network, namely PSDNet, is proposed to automatically extract BAs from the spaceborne synthetic aperture radar (SAR) data in the Greater Bay Area, which considers the spatial statistical features and speckle features in SAR images. The local indicators of spatial association, including Moran’s, Geary’s, and Getis’ together with the speckle divergence feature, are calculated for the SAR data, which can indicate the potential BAs. The amplitude SAR images and the corresponding features are then regarded as the inputs for PSDNet. In this framework, a pseudo-Siamese network can independently learn the BAs discrimination ability from the SAR original amplitude image and the features. The DenseNet is adopted as the backbone network of each channel, which can improve the efficiency while extracting the deep features of the BAs. Moreover, it also has the ability to extract the BAs with multi-scale sizes by using a multi-scale decoder. The Sentinel-1 (S1) SAR data for the Greater Bay Area in China are used for the experimental validation. Our method of BA extraction can achieve above 90% accuracy, which is similar to the current urban extraction product, demonstrating that our method can achieve BA mapping for spaceborne SAR data.

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.

Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia

Impervious surface extraction with high accuracy is important for monitoring urban expansion to sustainably manage the land resources and save the environment. In this context, use of spectral built-up indices has been extensively explored. This study examines the performance of Built-up Area Extraction Index (BAEI), Band Ratio for Built-up Area (BRBA), Modified Built-up Index (MBI), Normalized Built-up Area Index (NBAI), New Built-up Index (NBI), Normalized Difference Built-up Index (NDBI), and Urban Index (UI) in the classification and change detection of impervious surfaces using sentinel-2A MSI imagery. All these maps were classified into built-up and non-built-up areas, and evaluated based on histogram overlap and spectral discrimination index (SDI). Simultaneously, support vector machine (SVM) algorithm was employed to classify the imageries into land-use and land-cover (LULC) classes, viz., bare land, built-up, forest, vegetation, and water bodies. The findings indicate that NBAI, NBI, and NDBI have the highest SDI values of 1.24, 1.23 and 1.54; 1.06, 1.08, and 1.23; 1.16, 1.1 and 1.26 for 2016, 2018, and 2020, respectively. However, other indices show unsatisfactory results. The LULC change between 2016 and 2020 showed that the built-up area, bare land, and water bodies have increased by 9,084.5 ha, 813 ha, and 2 ha, respectively whereas vegetation and forest areas declined by 9,279.3 ha and 620.2 ha, respectively. The classification overall accuracy was 81%, 86%, and 82% for 2016, 2018, and 2020 images, respectively thereby affirming that spectral built-up indices in urban environments can deduce impervious surface extraction quickly and accurately.

A review on spectral indices for built-up area extraction using remote sensing technology

A review on spectral indices for built-up area extraction using remote sensing technology

Rural Built-Up Area Extraction from Remote Sensing Images Using Spectral Residual Methods with Embedded Deep Neural Network

A rural built-up area is one of the most important features of rural regions. Rapid and accurate extraction of rural built-up areas has great significance to rural planning and urbanization. In this paper, the spectral residual method is embedded into a deep neural network to accurately describe the rural built-up areas from large-scale satellite images. Our proposed method is composed of two processes: coarse localization and fine extraction. Firstly, an improved Faster R-CNN (Regions with Convolutional Neural Network) detector is trained to obtain the coarse localization of the candidate built-up areas, and then the spectral residual method is used to describe the accurate boundary of each built-up area based on the bounding boxes. In the experimental part, we firstly explored the relationship between the sizes of built-up areas and the kernels in the spectral residual method. Then, the comparing experiments demonstrate that our proposed method has better performance in the extraction of rural built-up areas.

Impact Assessment of Urban Built-up Area on Groundwater Level of District Faisalabad, Pakistan

This paper attempts to explore the impact of built-up areas on groundwater levels in district Faisalabad.To understand the rate of built-up area expansion and changes in the level of groundwater in the study area, groundwaterdata has been acquired from the Land Reclamation Department for all tehsils in district Faisalabad. This study wasconducted to assess spatial and temporal variation of groundwater level because of the built-up area change. Descriptivestatistics (Scatter Plot correlation technique) have been applied to figure out changes in groundwater levels. Furthermore,some built-up area extraction methods have been incorporated that are always found to be effective tools for theassessment of built-up area change. The spatial analysis tool Spline has applied to 79 bore points in all tehsils of districtFaisalabad. Results clearly show that a built-up increase of 41 km2in Faisalabad city is causing 1-foot depletion ingroundwater as compared to other tehsils, where a built-up increase is negligible. Such research studies of groundwaterchanges would assist the planners to adopt effective sustainable measures.

Research on the Extraction Method of Urban Built-Up Areas With an Improved Night Light Index

Nighttime light (NTL) data has been widely used in the research of urban built-up areas extraction. Due to the low resolution and the overflow characteristics of NTL data, it is difficult to extract urban built-up areas in small cities with undeveloped economy and small areas. How to extract them accurately becomes an important problem. Based on Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Land Surface Temperature NLST), we proposed an improved night light index, named Vegetation, Building and Temperature Enhanced NTL Urban Index (VBTANUI) to accurately extract built-up areas in small cities by the marker-based watershed segmentation algorithm. Taking Jiayuguan, Ezhou and Wuzhishan, China as three examples, we extracted their urban built-up areas. Comparing with the four traditional NTL indexes, such as VANUI, EANI, BANUI65 and BANUI43, the results showed that: (1) the VBTANUI could effectively enhance the light intensity of the built-up boundaries; (2) F1 scores of VBTANUI in three cities are of 89.60%, 95.70% and 82.75% respectively, which are 6.04%, 7.12%, 8.10% and 8.47% higher in average than that of traditional four indexes; (3) VBTANUI is feasible and effective in the extraction of built-up areas in small cities with undeveloped economy and small areas.

SARBuD1.0： A SAR building dataset based on GF-3 FSII imageries for built-up area extraction with deep learning method

SARBuD1.0： A SAR building dataset based on GF-3 FSII imageries for built-up area extraction with deep learning method

Research on the change of Spatio-temporal characteristics of Urban built-up area based on remote Sensing-- A case study of Yulin City, Shaanxi Province

Since the reform and opening up, the process of urbanization in China has been at a high level. In order to accurately obtain the boundary of urban built-up area and the spatiotemporal change characteristics of urban built-up area in recent years, this paper takes Yulin City as an example. With the help of ArcGIS, ERDAS and other platforms, we combine spatial comparison method and index method to extract urban built-up area from DMSP-OLS night light data and Landsat image data. At the same time, the confusion matrix is used to verify the extraction boundary. The research shows that: (1) Using DMSP-OLS night light data and Landsat data to extract the built-up area, the overall accuracy can reach 99%; (2) The urban built-up area of Yulin City expanded at a high speed from 2005 to 2018; (3) The urbanization process in the northeast of Yulin City is obviously higher than that in the south in recent years. This study has a certain reference value for the extraction of urban built-up areas and the development of urbanization in Yulin City by using DMSP-OLS night light data and Landsat data.

An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI

Timely and accurate extraction of urban built-up areas is crucial to addressing environmental problems related to fast changes in urban land cover, which is fundamental for optimizing land use patterns and supporting global sustainable development. Nighttime light (NTL) from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) offer a new data source for extracting urban information. However, this kind of data suffer from drawbacks of blooming effects. To address this problem, in this study, the Enhanced Nighttime Light Urban Index (ENUI) approach, which involves the combination of NPP-VIIRS NTL with the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), is proposed and tested. This approach was used to rapidly monitor the urban built-up areas in the Guangdong-Hong Kong-Macao Greater Bay Area in 2012, 2015, and 2018. The average overall accuracy and Map-level Image Classification Efficacy (MICE) for the extraction results are 93.56% and 0.77, respectively, while those of the Local-Optimized Thresholding (LOT) are 86.48% and 0.54, respectively; meanwhile, the average F-score values, user's accuracy and producer's accuracy for urban areas using the proposed approach increased by 9.98%, 10.90% and 8.67%, respectively, compared with the LOT. These findings suggest that this approach has a higher extraction accuracy than the LOT; this is primarily ascribed to the integration of NTL data with the NDVI, NDWI, and NDBI, which increases the variability of nighttime light in the urban core area and adequately alleviates the blooming effects of nighttime light brightness in water bodies and vegetated areas. The proposed approach shows great potentials to accurately and effectively monitor multi-temporal urban information and address environmental issues using NPP-VIIRS NTL data in global urban agglomerations.

Urban development analysis using built-up area maps based on multiple high-resolution satellite data

Analysis of built-up areas—the most significant artificial urban areas—reveals physical development processes. Unlike previous research involving medium-resolution remote sensing (RS) images, this study used built-up area maps generated from multiple high-resolution RS images with abundant built-up area edge information to analyze development in Zhengzhou. A transferable built-up area extraction (TBUAE) algorithm was developed to map the built-up area maps. The algorithm allows the developed deep learning model used on a certain satellite image to be eligible for other types of satellite images by altering the data distribution with adaptive Wallis filtering (DT-AWF). The proposed method alleviates the pressure of deep learning on the demand for new satellite image samples that are time-consuming and laborious. Additionally, the accuracy of built-up area mapping using this method exceeds 90%. Quantitative and qualitative analyses were conducted on the map results to observe the urban development of Zhengzhou from 2016 to 2020. We found that Zhengzhou has expanded rapidly since it was defined as a central city in central China in 2016. Additionally, the suburban built-up area has expanded rapidly and developed together with the central city. Further, affected by the policy, the built-up areas in different regions of Zhengzhou has changed differently, the urban edge is more simplified, and the urban internal structure is more compact.