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Xin Huang
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, PR China

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Journal article
Published: 12 July 2021 in Remote Sensing of Environment
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Knowledge of building height is critical for understanding the urban development process. High-resolution optical satellite images can provide fine spatial details within urban areas, while they have not been applied to building height estimation over multiple cities and the feasibility of mapping building height at a fine scale (< 5 m) remains understudied. Multi-view satellite images can describe vertical information of buildings, due to the inconsistent response of buildings (e.g., spectral and structural variations) to different viewing angles, but they have not been employed to deep learning-based building height estimation. In this context, we introduce high-resolution ZY-3 multi-view images to estimate building height at a spatial resolution of 2.5 m. We propose a multi-spectral, multi-view, and multi-task deep network (called M3Net) for building height estimation, where ZY-3 multi-spectral and multi-view images are fused in a multi-task learning framework. A random forest (RF) method using multi-source features is also carried out for comparison. We select 42 Chinese cities with diverse building types to test the proposed method. Results show that the M3Net obtains a lower root mean square error (RMSE) than the RF, and the inclusion of ZY-3 multi-view images can significantly lower the uncertainty of building height prediction. Comparison with two existing state-of-the-art studies further confirms the superiority of our method, especially the efficacy of the M3Net in alleviating the saturation effect of high-rise building height estimation. Compared to the vanilla single/multi-task models, the M3Net also achieves a lower RMSE. Moreover, the spatial-temporal transferability test indicates the robustness of the M3Net to imaging conditions and building styles. The test of our method on a relatively large area (covering about 14,120 km2) further validates the scalability of our method from the perspectives of both efficacy and quality. The source code will be made available at https://github.com/lauraset/BuildingHeightModel.

ACS Style

Yinxia Cao; Xin Huang. A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities. Remote Sensing of Environment 2021, 264, 112590 .

AMA Style

Yinxia Cao, Xin Huang. A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities. Remote Sensing of Environment. 2021; 264 ():112590.

Chicago/Turabian Style

Yinxia Cao; Xin Huang. 2021. "A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities." Remote Sensing of Environment 264, no. : 112590.

Research article
Published: 06 May 2021 in Geo-spatial Information Science
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The Local Climate Zone (LCZ) scheme provides researchers with a standard method to monitor the Urban Heat Island (UHI) effect and conduct temperature studies. How to generate reliable LCZ maps has therefore become a research focus. In recent years, researchers have attempted to use Landsat imagery to delineate LCZs and generate maps worldwide based on the World Urban Database and Access Portal Tools (WUDAPT). However, the mapping results obtained by the WUDAPT method are not satisfactory. In this paper, to generate more accurate LCZ maps, we propose a novel Convolutional Neural Network (CNN) model (namely, LCZ-CNN), which is designed to cope with the issues of LCZ classification using Landsat imagery. Furthermore, in this study, we applied the LCZ-CNN model to generate LCZ mapping results for China’s 32 major cities distributed in various climatic zones, achieving a significantly better accuracy than the traditional classification strategies and a satisfactory computational efficiency. The proposed LCZ-CNN model achieved satisfactory classification accuracies in all 32 cities, and the Overall Accuracies (OAs) of more than half of the cities were higher than 80%. We also designed a series of experiments to comprehensively analyze the proposed LCZ-CNN model, with regard to the transferability of the network and the effectiveness of multi-seasonal information. It was found that the first convolutional stage, corresponding to low-level features, shows better transferability than the second and third convolutional stages, which extract high-level and more image- or task-oriented features. It was also confirmed that the multi-seasonal information can improve the accuracy of LCZ classification. The thermal characteristics of the different LCZ classes were also analyzed based on the mapping results for China’s 32 major cities, and the experimental results confirmed the close relationship between the LCZ classes and the magnitude of the Land Surface Temperature (LST).

ACS Style

Xin Huang; Anling Liu; Jiayi Li. Mapping and analyzing the local climate zones in China’s 32 major cities using Landsat imagery based on a novel convolutional neural network. Geo-spatial Information Science 2021, 1 -30.

AMA Style

Xin Huang, Anling Liu, Jiayi Li. Mapping and analyzing the local climate zones in China’s 32 major cities using Landsat imagery based on a novel convolutional neural network. Geo-spatial Information Science. 2021; ():1-30.

Chicago/Turabian Style

Xin Huang; Anling Liu; Jiayi Li. 2021. "Mapping and analyzing the local climate zones in China’s 32 major cities using Landsat imagery based on a novel convolutional neural network." Geo-spatial Information Science , no. : 1-30.

Research article
Published: 06 May 2021 in Geo-spatial Information Science
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To contain the outbreak of COVID-19 in Wuhan, unprecedented interventions, including city lockdown and community closure, have been implemented. However, most of the current studies focused on evaluation of the city lockdown, but paid limited attention to the impacts of the community containment measures within the city. This research addressed this important issue from the perspective of urban planning, based on the epidemic and remote sensing data of 194 communities of Wuhan. We found that the number of confirmed cases of communities is highly related to urban planning factors, e.g. area percentage of buildings and density of neighboring markets. These factors are relevant to the residents’ activity patterns, which therefore impact the mode of virus transmission. Our research confirmed the effectiveness of the community-oriented control strategies, provided a valuable reference for other cities that are suffering from the epidemic, and exhibited new thoughts into future urban planning.

ACS Style

Xin Huang; Qiquan Yang; Junjing Yang. Importance of community containment measures in combating the COVID-19 epidemic: from the perspective of urban planning. Geo-spatial Information Science 2021, 1 -9.

AMA Style

Xin Huang, Qiquan Yang, Junjing Yang. Importance of community containment measures in combating the COVID-19 epidemic: from the perspective of urban planning. Geo-spatial Information Science. 2021; ():1-9.

Chicago/Turabian Style

Xin Huang; Qiquan Yang; Junjing Yang. 2021. "Importance of community containment measures in combating the COVID-19 epidemic: from the perspective of urban planning." Geo-spatial Information Science , no. : 1-9.

Journal article
Published: 06 April 2021 in ISPRS Journal of Photogrammetry and Remote Sensing
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Functional zones are the basic units of cities and the mapping work is fundamental to urban management, investigation, and research. The existing urban functional zone (UFZ) mapping methods usually utilize visual features from high spatial resolution optical images, and focus on two-dimensional image features, such as texture and landscape. However, UFZ is a comprehensive concept including geographical, social, and economic aspects. Therefore, it is reasonable to simultaneously take into account the characteristics of both human activities and image visual features for its accurate interpretation. Multi-view optical satellite images can delineate the physical characteristics of a city, i.e., 3D structures; on the other hand, high spatial resolution nighttime light images are important signals of human activities. These two data sources can be complementary in representing urban landscapes. However, to our knowledge, in the current literature, neither multi-view images nor high spatial resolution nighttime light images have been used for UFZ mapping, and it is poorly understood whether their individual or combined use can achieve satisfactory results. Therefore, in this study, a daytime and nighttime data fusion method, that is, the fusion of daytime multi-view optical images (Ziyuan3-01, 2.1 m) and high spatial resolution nighttime light images (Jilin1-07, 0.92 m), was proposed for UFZ mapping. In particular, a building enhanced nighttime light index (BENI) was proposed to improve the ability of the nighttime light images in discriminating between different functional zones. To verify the effectiveness of the proposed method, experiments were conducted in two megacities of China, i.e., Beijing and Wuhan. Our results indicated that: 1) an OA of ~80% was obtained by the spectral based method; 2) the addition of multi-view features led to ~84% OA, an increment of 4% compared with the spectral features; and 3) the inclusion of nighttime features achieved 85–90% OA, which further improved the OA of daytime features by 1–6%. It was also shown that the proposed BENI was superior to the original nighttime light brightness in identifying functional zones. In general, this study verified the effectiveness and complementarity of daytime (including multispectral and multi-view images) and nighttime images in UFZ mapping, and provided new thoughts for day and night data fusion and urban mapping.

ACS Style

Xin Huang; Junjing Yang; Jiayi Li; Dawei Wen. Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 175, 403 -415.

AMA Style

Xin Huang, Junjing Yang, Jiayi Li, Dawei Wen. Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 175 ():403-415.

Chicago/Turabian Style

Xin Huang; Junjing Yang; Jiayi Li; Dawei Wen. 2021. "Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery." ISPRS Journal of Photogrammetry and Remote Sensing 175, no. : 403-415.

Journal article
Published: 05 April 2021 in IEEE Geoscience and Remote Sensing Magazine
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ACS Style

Dawei Wen; Xin Huang; Francesca Bovolo; Jiayi Li; Xinli Ke; Anlu Zhang; Jon Atli Benediktsson. Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine 2021, PP, 2 -35.

AMA Style

Dawei Wen, Xin Huang, Francesca Bovolo, Jiayi Li, Xinli Ke, Anlu Zhang, Jon Atli Benediktsson. Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine. 2021; PP (99):2-35.

Chicago/Turabian Style

Dawei Wen; Xin Huang; Francesca Bovolo; Jiayi Li; Xinli Ke; Anlu Zhang; Jon Atli Benediktsson. 2021. "Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions." IEEE Geoscience and Remote Sensing Magazine PP, no. 99: 2-35.

Journal article
Published: 04 February 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Satellite-derived land surface temperatures (LSTs) are a critical parameter in various fields. Unfortunately, there are numerous gaps in LST products due to cloud contamination and orbital gaps. In previous studies, various gapfilling methods have been developed. However, most of those methods use only spatiotemporal information to fill gaps. In this study, a gapfilling method called the enhanced hybrid (EH) method that integrates spatiotemporal information and information from other similar LST products was proposed. The accuracy of the EH method was compared with the accuracies of three other gapfilling methods that only use spatiotemporal information: Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST), interpolation of the mean anomalies (IMAs), and Gapfill. It was found that the correlations between the four LST products were strong, indicating that using information from other products may improve the accuracy of gapfilling. On average, the mean absolute errors (MAEs) of the data filled using the EH method were 23.7%-52.7% lower than those of RSDAST, 35.4%-38.7% lower than those of IMA, and 38.5%-46.9% lower than those of the Gapfill method. The usage of information from other similar LST products was the main reason for the high accuracy observed for the EH method. In addition, the LST images filled using the RSDAST and IMA methods had some outliers, while there were fewer obvious outliers in the LST images filled with the EH method. It was concluded that the EH method is a robust gapfilling method with a high accuracy.

ACS Style

Rui Yao; Lunche Wang; Xin Huang; Liang Sun; Ruiqing Chen; Xiaojun Wu; Wei Zhang; Zigeng Niu. A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.

AMA Style

Rui Yao, Lunche Wang, Xin Huang, Liang Sun, Ruiqing Chen, Xiaojun Wu, Wei Zhang, Zigeng Niu. A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.

Chicago/Turabian Style

Rui Yao; Lunche Wang; Xin Huang; Liang Sun; Ruiqing Chen; Xiaojun Wu; Wei Zhang; Zigeng Niu. 2021. "A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.

Journal article
Published: 20 January 2021 in Journal of Cleaner Production
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As the two most common types of land cover in cities, vegetation (Veg) and artificial surfaces (AS) often exhibit competitive effects, i.e., cooling effect and warming effect, on land surface temperature (LST). Hitherto, the change of this competitive effect along the proportion gradient of AS within urban areas and their implications for urban construction still lacks adequate attention and discussion. To fill this gap, we made a quantitative analysis of the relationship between Veg (trees or grassland), AS and LST in 35 major cities of China by using Ziyuan-3 (ZY-3) high-resolution satellite observations. Results found that: (1) in each city, there exists a certain threshold (or “turning point”) along the proportion gradient of AS, exceeds which AS replaces Veg as the variable that have dominant effect on LST (i.e., the warming effect of AS is always stronger than the cooling effect of Veg); (2) for most cities, the turning points of AS for grassland and trees are 60% and 70%, respectively; (3) the turning point for cities at a higher development level is lower, indicating that even a relatively low AS coverage (∼50–60%) in these areas can lead to an evident rise in LST; 4) compared to cities in temperate and tropical climate zones, the turning point for arid/semi-arid cities is higher, implying that their urban Veg shows a better performance in mitigating urban heat stress. This study represents a systematic investigation of the competitive effect of urban Veg and AS on LST, and the understanding of turning point provides a new perspective for stakeholders to integrate urban development and temperature regulation in planning initiatives.

ACS Style

Yue Liu; Xin Huang; Qiquan Yang; Yinxia Cao. The turning point between urban vegetation and artificial surfaces for their competitive effect on land surface temperature. Journal of Cleaner Production 2021, 292, 126034 .

AMA Style

Yue Liu, Xin Huang, Qiquan Yang, Yinxia Cao. The turning point between urban vegetation and artificial surfaces for their competitive effect on land surface temperature. Journal of Cleaner Production. 2021; 292 ():126034.

Chicago/Turabian Style

Yue Liu; Xin Huang; Qiquan Yang; Yinxia Cao. 2021. "The turning point between urban vegetation and artificial surfaces for their competitive effect on land surface temperature." Journal of Cleaner Production 292, no. : 126034.

Journal article
Published: 23 November 2020 in IEEE Transactions on Geoscience and Remote Sensing
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The recent availability of high-resolution multiview ZY-3 satellite images, with angular information, can provide an opportunity to capture 3-D structural features for classification. In high-resolution image classification over urban areas, objects with diverse vertical structures make urban landscape more heterogeneous in 3-D space and consequently can make the classification challenging. In this article, a novel multiangle gray-level cooccurrence tensor feature is proposed based on the multiview bands of the ZY-3 imagery, namely, GLCMMA-T. The GLCMMA-T feature captures the distributions of the gray-level spatial variation under different viewing angles, which can depict the 3-D textures and structures of urban objects. The spectral and GLCMMA-T tensor features are interpreted by two 3-D convolutional neural network (CNN) streams and then concatenated as the input to the fully connected layer. This novel multispectral and multiangle 3-D convolutional neural network (M²-3-DCNN) combines the spectral and angular information, and the fused feature has the potential to provide a comprehensive description of urban objects with complex vertical structures. The experimental results on ZY-3 multiview images from four test areas indicate that the proposed method can significantly improve the classification accuracy when compared with several state-of-the-art multiangle features and deep-learning-based image classification methods.

ACS Style

Xin Huang; Shuang Li; Jiayi Li; Xiuping Jia; Jun Li; Xiao Xiang Zhu; Jon Atli Benediktsson. A Multispectral and Multiangle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images Over Urban Areas. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -20.

AMA Style

Xin Huang, Shuang Li, Jiayi Li, Xiuping Jia, Jun Li, Xiao Xiang Zhu, Jon Atli Benediktsson. A Multispectral and Multiangle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images Over Urban Areas. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-20.

Chicago/Turabian Style

Xin Huang; Shuang Li; Jiayi Li; Xiuping Jia; Jun Li; Xiao Xiang Zhu; Jon Atli Benediktsson. 2020. "A Multispectral and Multiangle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images Over Urban Areas." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-20.

Journal article
Published: 05 November 2020 in International Journal of Applied Earth Observation and Geoinformation
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Timely and accurate global urban maps are fundamental in monitoring urbanization process and understanding environmental degradation. Therefore, this paper proposed a locally adaptive and fully automated global mapping method and produced an updated 250 m MODIS global urban area product (MGUP) from 2001 to 2018. The proposed approach mainly consists of 1) automated samples extraction from existing global products, 2) locally adaptive samples selection and trained classification in each 5° × 5° grid, and 3) post-processing in terms of the spatio-temporal context. To validate the product, 9 groups of samples for every two years from 2001 to 2018, amounting to over 150,000 sample points, were collected manually from Landsat imagery as global validation dataset. Accuracy assessment indicates that MGUP has a F-score of 0.88, achieving better results than the contemporary global products, i.e., MCD12Q1.v5 (0.82), MCD12Q1.v6 (0.86), and CCI-LC (0.86). Analysis of urban expansion based on MGUP shows that the world’s urban area increased to 802233 km2 and accounted for 0.54% of the Earth’s land surface in 2018. The total global urban area expanded by 1.68 times from 2001 to 2018. At continent level, urban density varies considerably, and the highest and lowest one is in Europe (1.78%) and Oceania (0.15%), respectively. At national level, large increment of urban area mainly occurs in North America, Asia, and South America; and countries having high growth rates are mainly developing countries in Africa and Asia. MGUP can be downloaded at https://www.researchgate.net/publication/339873537_MGUP_annual_global_2001_2018.

ACS Style

Xin Huang; Jiongyi Huang; Dawei Wen; Jiayi Li. An updated MODIS global urban extent product (MGUP) from 2001 to 2018 based on an automated mapping approach. International Journal of Applied Earth Observation and Geoinformation 2020, 95, 102255 .

AMA Style

Xin Huang, Jiongyi Huang, Dawei Wen, Jiayi Li. An updated MODIS global urban extent product (MGUP) from 2001 to 2018 based on an automated mapping approach. International Journal of Applied Earth Observation and Geoinformation. 2020; 95 ():102255.

Chicago/Turabian Style

Xin Huang; Jiongyi Huang; Dawei Wen; Jiayi Li. 2020. "An updated MODIS global urban extent product (MGUP) from 2001 to 2018 based on an automated mapping approach." International Journal of Applied Earth Observation and Geoinformation 95, no. : 102255.

Journal article
Published: 29 August 2020 in Remote Sensing
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The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.71 and 0.83, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level.

ACS Style

Wei Chen; Yuyu Zhou; Qiusheng Wu; Gang Chen; Xin Huang; Bailang Yu. Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sensing 2020, 12, 2805 .

AMA Style

Wei Chen, Yuyu Zhou, Qiusheng Wu, Gang Chen, Xin Huang, Bailang Yu. Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sensing. 2020; 12 (17):2805.

Chicago/Turabian Style

Wei Chen; Yuyu Zhou; Qiusheng Wu; Gang Chen; Xin Huang; Bailang Yu. 2020. "Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China." Remote Sensing 12, no. 17: 2805.

Journal article
Published: 07 June 2020 in Remote Sensing of Environment
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Rivers are essential to the Earth's ecosystem, but the current understanding of river width variability is limited, owing to the sparse distribution of gauging stations. Remote sensing data enable the surveying and analysis of river geomorphology by providing multi-temporal Earth observation data from satellites at fine spatial and temporal resolutions. We proposed an optimized RivWidth method to automatically calculate width for all channels in a water map and parallelized it to produce the Multi-temporal China River Width (MCRW) dataset, which is the first 30-m multi-temporal river width dataset for China during 1990–2015, including estimates under both seasonal fluctuations and dynamic inundation frequencies. The MCRW dataset is made up of 1.3 × 108 seasonal estimates of river width, and covers 1.4 × 105 km of rivers in China. We validated the MCRW dataset against in-situ measurements. The MCRW estimates at maximum water extent showed a satisfactory accuracy of 15.0% and 15.2% for the mean absolute percentage error (MAPE) and the relative root-mean-square error (RRMSE), respectively. The MCRW dataset was further compared with the current state-of-the-art global product, the Global River Widths from Landsat (GRWL) dataset, which demonstrated the superiority of the MCRW in describing the basins of China. Our analysis indicated that the mean river widths of China in both summer and winter have increased over the past 25 years, and river width of the Yangtze River mainstream in the lower drainage region has shown a downward trend while the its middle reaches and tributaries (upstream of the Three Gorges Dam) have shown an upward trend. We also developed a locally adaptive search method to quantify seasonal (summer and winter) river width variability. The results revealed that most of the rivers were wider in summer during the study period, and mainstream of Yangtze River in middle/lower region exhibited less seasonal variability than its tributaries. Larger widths were observed in the middle reaches of the Yellow River and the upper reaches of the Black River in winter due to ice-jam floods. Overall, the generated MCRW dataset has the potential to serve as a fundamental resource in Earth system science, and could provide valuable support to surface water resource and riverine management.

ACS Style

Jie Yang; Xin Huang; Qiuhong Tang. Satellite-derived river width and its spatiotemporal patterns in China during 1990–2015. Remote Sensing of Environment 2020, 247, 111918 .

AMA Style

Jie Yang, Xin Huang, Qiuhong Tang. Satellite-derived river width and its spatiotemporal patterns in China during 1990–2015. Remote Sensing of Environment. 2020; 247 ():111918.

Chicago/Turabian Style

Jie Yang; Xin Huang; Qiuhong Tang. 2020. "Satellite-derived river width and its spatiotemporal patterns in China during 1990–2015." Remote Sensing of Environment 247, no. : 111918.

Journal article
Published: 29 April 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Building footprint maps are vital to many remote sensing (RS) applications, such as 3-D building modeling, urban planning, and disaster management. Due to the complexity of buildings, the accurate and reliable generation of the building footprint from RS imagery is still a challenging task. In this article, an end-to-end building footprint generation approach that integrates convolution neural network (CNN) and graph model is proposed. CNN serves as the feature extractor, while the graph model can take spatial correlation into consideration. Moreover, we propose to implement the feature pairwise conditional random field (FPCRF) as a graph model to preserve sharp boundaries and fine-grained segmentation. Experiments are conducted on four different data sets: 1) Planetscope satellite imagery of the cities of Munich, Paris, Rome, and Zurich; 2) ISPRS Benchmark data from the city of Potsdam; 3) Dstl Kaggle data set; and 4) Inria Aerial Image Labeling data of Austin, Chicago, Kitsap County, Western Tyrol, and Vienna. It is found that the proposed end-to-end building footprint generation framework with the FPCRF as the graph model can further improve the accuracy of building footprint generation by using only CNN, which is the current state of the art.

ACS Style

Qingyu Li; Yilei Shi; Xin Huang; Xiao Xiang Zhu. Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF). IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 7502 -7519.

AMA Style

Qingyu Li, Yilei Shi, Xin Huang, Xiao Xiang Zhu. Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF). IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (11):7502-7519.

Chicago/Turabian Style

Qingyu Li; Yilei Shi; Xin Huang; Xiao Xiang Zhu. 2020. "Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)." IEEE Transactions on Geoscience and Remote Sensing 58, no. 11: 7502-7519.

Journal article
Published: 09 April 2020 in Remote Sensing of Environment
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Automatically monitoring newly constructed building areas (NCBAs) is essential for efficient land resource management and sustainable urban development, particularly in the rapidly urbanizing country of China. In this regard, time-series multi-view high-resolution optical satellite images can provide fine spatial details for clearly characterizing NCBAs, but this leads to great heterogeneity and complexity, owing to the high spectral variation, complicated imaging conditions, and different viewing angles. Moreover, to date, the vertical features and time-series information from these images have not been fully exploited for urban change detection. In this paper, our primary objective is to automatically detect the presence of NCBAs, and meanwhile, to investigate the feasibility of identifying their change timing using time-series multi-view ZY-3 high-resolution satellite images. To this aim, we propose an automatic change detection method consisting of three components: 1) firstly, we jointly use planar-vertical features to delineate the NCBAs; 2) object-based temporal correction is subsequently applied to improve the spatiotemporal consistency of the features; and 3) finally, a multi-temporal change detection model is used to simultaneously capture the NCBAs and the change timing. We applied the method on two urban fringe areas of Beijing (7 multi-temporal image sets) and Shanghai (7 multi-temporal image sets), respectively, which are cities that have been experiencing rapid urbanization. The experimental results confirmed the effectiveness of the proposed method. For both study areas, the F-score values reached nearly 90% in terms of NCBA detection, and with respect to the change timing, the overall accuracies with a one-year tolerance strategy reached around 92%. The joint use of the planar-vertical features and the inclusion of multi-temporal images make the proposed method a promising approach for automatically providing the spatiotemporal information of NCBAs in practical applications.

ACS Style

Xin Huang; Yinxia Cao; Jiayi Li. An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images. Remote Sensing of Environment 2020, 244, 111802 .

AMA Style

Xin Huang, Yinxia Cao, Jiayi Li. An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images. Remote Sensing of Environment. 2020; 244 ():111802.

Chicago/Turabian Style

Xin Huang; Yinxia Cao; Jiayi Li. 2020. "An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images." Remote Sensing of Environment 244, no. : 111802.

Short communication
Published: 03 April 2020 in Science Bulletin
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Our knowledge of the climatic feedbacks of irrigation is still limited because of high uncertainty of current modeling results and insufficient coverage of in-situ observations. Here, by using satellite observations, we present a global-scale observation-driven assessment of the impact of irrigation on land surface temperature (LST). Irrigation is found to reduce LST, especially in arid regions during warm season. This cooling effect is largely attributed to the irrigation-induced increase in evapotranspiration. Our findings have important implications for future climatic and agricultural researches, under the circumstance of sustaining global warming. Download : Download high-res image (146KB)Download : Download full-size image

ACS Style

Qiquan Yang; Xin Huang; Qiuhong Tang. Global assessment of the impact of irrigation on land surface temperature. Science Bulletin 2020, 65, 1440 -1443.

AMA Style

Qiquan Yang, Xin Huang, Qiuhong Tang. Global assessment of the impact of irrigation on land surface temperature. Science Bulletin. 2020; 65 (17):1440-1443.

Chicago/Turabian Style

Qiquan Yang; Xin Huang; Qiuhong Tang. 2020. "Global assessment of the impact of irrigation on land surface temperature." Science Bulletin 65, no. 17: 1440-1443.

Journal article
Published: 06 March 2020 in ISPRS Journal of Photogrammetry and Remote Sensing
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Timely and reliable land-use/land-cover (LULC) change dynamic monitoring is the basis of urban understanding and planning. However, either the training sample shortage or the error accumulation in the multi-temporal processing inevitably restricts the monitoring performance. In this paper, to overcome these problems, we present a label-noise robust active learning method, which automatically collects reliable and informative samples from the images and builds a unified classification system with these augmented samples. In more detail, a Bayesian sample collection process that fuses the unsupervised transition information and the multi-temporal land-cover information is designed to provide candidate samples with “from-to” labels. A reliability-based multi-classifier active learning method is then proposed to adaptively allocate the more reliable samples to the classes that are difficulty to classify. Finally, a fusion of the multiple multi-date classifications trained by the selected samples is implemented to identify the change type of interest. The dynamic monitoring results for Shanghai, Shenzhen, and Shiyan in China, two megacities with rapid and obvious urbanization and a small city with relatively slow urbanization, indicate that the proposed method achieves a significantly higher accuracy than the current state-of-the art methods. The sample accuracy verified by the high spatial resolution reference maps endorses the applicability of the sample collection, while the reliability-based active learning further ensures the robustness of the proposed method in the label-noise situation. The presented method was tested in two difficult situations (a small training sample case and a training sample set without joint labeling), so that the robustness and accuracy of the approach can be expected to be of a similar or better quality in cases with more training samples. Given its effectiveness and robustness, the proposed method could be widely applied in LULC change dynamic monitoring.

ACS Style

Jiayi Li; Xin Huang; Xiaoyu Chang. A label-noise robust active learning sample collection method for multi-temporal urban land-cover classification and change analysis. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 163, 1 -17.

AMA Style

Jiayi Li, Xin Huang, Xiaoyu Chang. A label-noise robust active learning sample collection method for multi-temporal urban land-cover classification and change analysis. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 163 ():1-17.

Chicago/Turabian Style

Jiayi Li; Xin Huang; Xiaoyu Chang. 2020. "A label-noise robust active learning sample collection method for multi-temporal urban land-cover classification and change analysis." ISPRS Journal of Photogrammetry and Remote Sensing 163, no. : 1-17.

Journal article
Published: 06 March 2020 in Science Bulletin
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Detailed and precise urban land-cover maps are crucial for urban-related studies. However, there are limited ways of mapping high-resolution urban land cover over large areas. In this paper, we propose an operational framework to map urban land cover on the basis of Ziyuan-3 satellite images. Based on this framework, we produced the first high-resolution (2 m) urban land-cover map (Hi-ULCM) covering the 42 major cities of China. The overall accuracy of the Hi-ULCM dataset is 88.55%, of which 14 cities have an overall accuracy of over 90%. Most of the producer’s accuracies and user’s accuracies of the land-cover classes exceed 85%. We further conducted a landscape pattern analysis in the 42 cities based on Hi-ULCM. In terms of the comparison between the 42 cities in China, we found that the difference in the land-cover composition of urban areas is related to the climatic characteristics and urbanization levels, e.g., cities with warm climates generally have higher proportions of green spaces. It is also interesting to find that cities with higher urbanization levels are more habitable, in general. From the landscape viewpoint, the geometric complexity of the landscape increases with the urbanization level. Compared with the existing medium-resolution land-cover/use datasets (at a 30-m resolution), Hi-ULCM represents a significant advance in accurately depicting the detailed land-cover footprint within the urban areas of China, and will be of great use for studies of urban ecosystems.

ACS Style

Xin Huang; Ying Wang; Jiayi Li; Xiaoyu Chang; Yinxia Cao; Junfeng Xie; Jianya Gong. High-resolution urban land-cover mapping and landscape analysis of the 42 major cities in China using ZY-3 satellite images. Science Bulletin 2020, 65, 1039 -1048.

AMA Style

Xin Huang, Ying Wang, Jiayi Li, Xiaoyu Chang, Yinxia Cao, Junfeng Xie, Jianya Gong. High-resolution urban land-cover mapping and landscape analysis of the 42 major cities in China using ZY-3 satellite images. Science Bulletin. 2020; 65 (12):1039-1048.

Chicago/Turabian Style

Xin Huang; Ying Wang; Jiayi Li; Xiaoyu Chang; Yinxia Cao; Junfeng Xie; Jianya Gong. 2020. "High-resolution urban land-cover mapping and landscape analysis of the 42 major cities in China using ZY-3 satellite images." Science Bulletin 65, no. 12: 1039-1048.

Journal article
Published: 01 February 2020 in Science of The Total Environment
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Urbanization significantly influences ozone via two conditions of its formation: 1) precursor concentration; and 2) chemical regime. Recently, there has been raised concern about the influence of urban agglomerations on these two conditions. Although valuable efforts have been made, some contrary viewpoints exist. Meanwhile, urban agglomerations in developed and developing regions are experiencing different urbanization processes, so a systematic comparison between these two regions is warranted. In this context, by leveraging multi-source geospatial datasets, this paper systematically gauges the influence of urban agglomerations on ozone precursor conditions and further investigates the spatiotemporal variations. Based on the analysis of 71 global agglomerations during 2005-2016, it is found that: 1) not all urban agglomerations have a positive effect on ozone precursor conditions; 2) the negative effects of urban agglomerations can be attributed to the low latitudes and the ecological areas (p < 0.05); 3) the agglomeration influence intensifies with the increase of built-up area, population, and latitude (p < 0.05); 4) the anthropogenic nitrogen oxide (NOx) emission from all sectors can aggravate the magnitude of the urban agglomeration influence (p < 0.05), while for volatile organic compounds (VOCs), only the contribution of industrial emissions is significant (p < 0.05); and 5) in view of the temporal dynamics, the influence of urban agglomeration on ozone precursor condition is opposite in developed and developing regions. This study will provide important insights for future urban agglomeration studies and ozone pollution monitoring with geospatial datasets.

ACS Style

Jiayi Li; Yuan Gao; Xin Huang. The impact of urban agglomeration on ozone precursor conditions: A systematic investigation across global agglomerations utilizing multi-source geospatial datasets. Science of The Total Environment 2020, 704, 135458 .

AMA Style

Jiayi Li, Yuan Gao, Xin Huang. The impact of urban agglomeration on ozone precursor conditions: A systematic investigation across global agglomerations utilizing multi-source geospatial datasets. Science of The Total Environment. 2020; 704 ():135458.

Chicago/Turabian Style

Jiayi Li; Yuan Gao; Xin Huang. 2020. "The impact of urban agglomeration on ozone precursor conditions: A systematic investigation across global agglomerations utilizing multi-source geospatial datasets." Science of The Total Environment 704, no. : 135458.

Review
Published: 25 December 2019 in Science China Earth Sciences
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The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution (HR) remote sensing data and emerging of new satellite sensors, HR observation of urban areas has become increasingly possible, which provides us with more elaborate urban information. However, the strong heterogeneity in the spectral and spatial domain of HR imagery brings great challenges to urban remote sensing. In recent years, numerous approaches were proposed to deal with HR image interpretation over complex urban scenes, including a series of features from low level to high level, as well as state-of-the-art methods depicting not only the urban extent, but also the intra-urban variations. In this paper, we aim to summarize the major advances in HR urban remote sensing from the aspects of feature representation and information extraction. Moreover, the future trends are discussed from the perspectives of methodology, urban structure and pattern characterization, big data challenge, and global mapping.

ACS Style

Jianya Gong; Chun Liu; Xin Huang. Advances in urban information extraction from high-resolution remote sensing imagery. Science China Earth Sciences 2019, 63, 463 -475.

AMA Style

Jianya Gong, Chun Liu, Xin Huang. Advances in urban information extraction from high-resolution remote sensing imagery. Science China Earth Sciences. 2019; 63 (4):463-475.

Chicago/Turabian Style

Jianya Gong; Chun Liu; Xin Huang. 2019. "Advances in urban information extraction from high-resolution remote sensing imagery." Science China Earth Sciences 63, no. 4: 463-475.

Journal article
Published: 09 December 2019 in Science of The Total Environment
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The effect of irrigation on temperature has attracted much attention because its cooling effect may mask the warming due to other factors, such as greenhouse gas forcing. Although many studies have examined the irrigation cooling effect (ICE) based on near-surface air temperature from meteorological observations or climate model simulations, few studies have directly addressed the effect of irrigation on land surface temperature (LST), which is closely linked to the surface energy balance and near-surface air temperature. In this paper, an ICE detection (ICED) method is proposed to assess the effect of irrigation on LST using the Moderate Resolution Imaging Spectroradiometer (MODIS) products across China. The magnitude of the ICE is calculated as the LST difference between irrigated area and adjacent non-irrigated area in the self-adaptive moving window determined by the ICED method. The results show that irrigation cools daytime LST by 1.15 K, and cools nighttime LST by 0.13 K, on average, across irrigated areas in China. The effect of irrigation on LST differs greatly among the climate zones and seasons, characterized by the enhanced ICE in arid regions and the growing season. In the arid climate zone, nearly all the irrigated areas show a lower daytime LST than the adjacent non-irrigated areas, leading to a strong ICE magnitude of >6 K in the growing season. In the humid climate zone, the impact of irrigation on LST is generally negligible, with a magnitude around zero throughout the year. This study provides observational evidence and a comprehensive assessment of the effect of irrigation on LST. The proposed ICED method has the potential to be used to study the spatiotemporal variation of the effect of irrigation on LST over other regions with intensive irrigation.

ACS Style

Qiquan Yang; Xin Huang; Qiuhong Tang. Irrigation cooling effect on land surface temperature across China based on satellite observations. Science of The Total Environment 2019, 705, 135984 .

AMA Style

Qiquan Yang, Xin Huang, Qiuhong Tang. Irrigation cooling effect on land surface temperature across China based on satellite observations. Science of The Total Environment. 2019; 705 ():135984.

Chicago/Turabian Style

Qiquan Yang; Xin Huang; Qiuhong Tang. 2019. "Irrigation cooling effect on land surface temperature across China based on satellite observations." Science of The Total Environment 705, no. : 135984.

Journal article
Published: 09 December 2019 in Science of The Total Environment
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Spatially continuous satellite data have been widely used to estimate monthly air temperature (Ta). However, it is still not clear whether the estimated monthly Ta is temporally consistent with observed Ta or not. In this study, the accuracies of interannual variations and temporal trends in estimated monthly Ta were systematically analyzed for Mainland China during 2001–2018. The differences in accuracy among five ways to input data into the model were investigated. The Cubist algorithm and ten variables were used to estimate monthly Ta. It was found that inputting data for the same month into the model can generate more accurate results than inputting all data into the model. Using temporal variables (i.e., month and year) can significantly increase the accuracy of estimated Ta. These results can be explained by different relationships between Ta and auxiliary variables that appear at different times. Thus, using temporal variables can help distinguish between different relationships and improve accuracy levels of the estimated Ta. When applying the best method (inputting data for the same month into the model and using the year as a temporal variable), the coefficient of determination (R2) of estimated monthly mean Ta, interannual variations in monthly mean Ta and temporal trends in monthly mean Ta were recorded as 0.997, 0.731 and 0.848, respectively. The root mean squared errors (RMSEs) of estimated monthly mean Ta, interannual variations in monthly mean Ta and temporal trends in monthly mean Ta were recorded as 0.629 °C, 0.593 °C and 0.201 °C/decade, respectively. An accurate, national coverage, 1 km spatial resolution and long time series (2001–2018) monthly mean, maximum and minimum Ta dataset was finally developed. The dataset can be of great use to many fields such as climatology, hydrology and ecology.

ACS Style

Rui Yao; Lunche Wang; Xin Huang; Long Li; Jia Sun; Xiaojun Wu; Weixia Jiang. Developing a temporally accurate air temperature dataset for Mainland China. Science of The Total Environment 2019, 706, 136037 .

AMA Style

Rui Yao, Lunche Wang, Xin Huang, Long Li, Jia Sun, Xiaojun Wu, Weixia Jiang. Developing a temporally accurate air temperature dataset for Mainland China. Science of The Total Environment. 2019; 706 ():136037.

Chicago/Turabian Style

Rui Yao; Lunche Wang; Xin Huang; Long Li; Jia Sun; Xiaojun Wu; Weixia Jiang. 2019. "Developing a temporally accurate air temperature dataset for Mainland China." Science of The Total Environment 706, no. : 136037.