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Accurate paddy rice mapping is crucial for ensuring food security and guiding agricultural production. Vegetation indices are extensively employed to map paddy rice. However, most traditional normalized indices tend to be oversaturated during periods of lush vegetation due to normalization errors, resulting in uncertainties in paddy rice mapping. To address this issue, we introduce a novel red-edge rice index (RERI) in this study; this index comprises information from red, near-infrared, and red-edge bands without normalization. To extract single- and double-cropping rice features from potential rice areas, we employ GF-6 and Sentinel-2 images based on the proposed RERI and the random forest algorithm. The proposed method is validated in the Dingcheng District of Changde city, China, and the results are compared with those based on three normalized vegetation indices. The results show that the RERI yielded the highest levels of the accuracy for all the metrics, achieving an overall accuracy (OA) of 92.50% and a kappa coefficient of 0.8875. The RERI exhibited F1 scores of 92.26% for single-cropping rice, 93.00% for double-cropping rice, and 92.28% for non-rice areas. Our results highlight the potential of using the RERI for rice identification, and the effectiveness of our method for rice extraction is demonstrated.

The up-to-date building information is significant to urban planning and economic assessment. Automatic building change detection (BCD) from bi-temporal remote sensing images is essential for updating building status efficiently. Nevertheless, BCD remains challenging due to the complex building appearance, the diverse imaging conditions, and the building’s positional inconsistencies between the bi-temporal images. Recent convolutional neural network-based BCD methods have achieved impressive performance. However, most existing methods employed subtraction or concatenation to identify building changes. Such simple change-deciding operations ignore the spatial–temporal correlation between the bi-temporal features and cannot model the building changes effectively, resulting in overmuch misclassifications. This paper proposes a hierarchical XOR approximating network CDasXORNet to model building changes robustly. An XOR approximation operation is proposed to produce discriminative building differential features from the bi-temporal inputs. We assume that BCD and the logical XOR function have the same nature (i.e., when the two inputs are identical, the output is unchanged/False; otherwise, it is changed/True). This applies to the building change and unaltered pixels simultaneously. Thus, by approximating XOR operation, CDasXORNet can simultaneously exploit the spatial–temporal correlation and the changed and changeless information of buildings. Hierarchical XOR approximation operations are subsequently designed, which process only high-level features to mitigate the influence of substantial irrelevant spectral differences. In addition, the residual linear attention mechanism is introduced to refine the building change features further. Experiments on three publicly challenging datasets demonstrate that our method achieves promising BCD results with fewer commission errors and higher overall performance than the comparative approaches.

Change detection (CD) using deep learning techniques is a prominent topic in the field of remote sensing (RS). However, the existing methods require large amounts of labeled samples for supervised learning, which is time-consuming and labor-intensive. To address this challenge, semi-supervised learning methods that utilize a limited number of labeled samples along with a large pool of unlabeled samples have emerged as a compelling solution. We propose a novel semi-supervised CD (SSCD) network that combines self-training and consistency regularization, namely STCRNet. During the self-training phase, STCRNet selects unlabeled samples with reliable pseudolabels based on their prediction stability across different training epochs and the consistency between class activation maps and prediction results within the model. Then, we apply data augmentation to the reliable samples and enforce consistency regularization on the augmented samples using the pseudolabels to enhance the network's robustness. Moreover, feature consistency regularization is applied to the remaining unlabeled samples with image perturbations, thereby broadening the feature space and improving the model's generalization performance. The experimental results on two widely used datasets demonstrate that STCRNet achieves state-of-the-art performance, especially with a significantly small amount (5%–10%) of labeled samples. STCRNet presents a promising solution for SSCD.

Convolutional neural networks have demonstrated remarkable capability in extracting deep semantic features from images, leading to significant advancements in various image processing tasks. This success has also opened up new possibilities for change detection (CD) in remote sensing applications. But unlike the conventional image recognition tasks, the performance of AI models in CD heavily relies on the method used to fuse the features from two different phases of the image. The existing deep-learning-based methods for CD typically fuse features of bitemporal images using difference or concatenation techniques. However, these approaches often fail tails to prioritize potential change areas adequately and neglect the rich contextual information essential for discerning subtle changes, potentially leading to slower convergence speed and reduced accuracy. To tackle this challenge, we propose a novel feature fusion approach called feature-difference attention-based feature fusion CD network. This method aims to enhance feature fusion by incorporating a feature-difference attention-based feature fusion module, enabling a more focused analysis of change areas. Additionally, a deep-supervised attention module is implemented to leverage the deep surveillance module for cascading refinement of change areas. Furthermore, an atrous spatial pyramid pooling fast is employed to efficiently acquire multiscale object information. The proposed method is evaluated on two publicly available datasets, namely the WHU-CD and LEVIR-CD datasets. Compared with the state-of-the-art CD methods, the proposed method outperforms in all metrics, with an intersection over union of 92.49% and 85.56%, respectively.

With the growth in the vehicle industry, autonomous driving has become a hot topic worldwide and has attracted increasing attention from both industrial and academic sectors. Maps, as pivotal geospatial information carriers, play a vital role in route planning and navigation service. Compared with conventional maps, high-definition (HD) maps possesses higher precision, richer information, and various services and are regarded as critical infrastructure for autonomous driving. However, heterogeneous HD map data standards and models have different characteristics and advantages, and thus they rarely meet all autonomous driving requirements for different driving objectives. This research presents an interoperable map data model, the Open HD Map Service Model (OHDMSM), to provide a reference for HD map development. The designed OHDMSM, which contains three data layers and a set of corresponding interfaces, demonstrates high interoperability for HD map data fusion and application. As a proof of concept, an HD map data system is implemented with all functions following the designed data model and interfaces of OHDMSM. The design and development of OHDMSM data structures, interfaces and systems will benefit data requesting, updating, and interoperation for HD map data worldwide, which can be helpful for developing autonomous driving and intelligent transportation in the Digital Earth.

AbstractLandslide susceptibility analysis is an essential tool for landslide hazard management. Correlation analysis of the driving factors before landslide susceptibility analysis is crucial to obtain more accurate results and higher computational efficiency. This article presents an FR-weighted GeoDetector, which can, at different gridding scales, stably screen out the driving factors most relevant to historical landslides in the study area compared to the performance of the original GeoDetector. The correlation analysis result shows that the most relevant seven conditioning factors to historical landslides in the study area are: lithology, distance to road, elevation, slope, STI, SPI, and distance to faults. Four machine learning models (logistic regression [LR], random forest [RF], artificial neural network [ANN], and Xgboost) are implemented for landslide susceptibility analysis, demonstrating that such models can achieve higher accuracy with features filtered by the FR-weighted GeoDetector than with all features. The Xgboost models trained on seven and 12 features were used to generate landslide susceptibility maps. The overlay with historical landslides showed that the models trained on seven features generated a more reasonable landslide susceptibility map, proving that selecting crucial landslide conditioning factors is a better solution than using a full range of landslide conditioning factors.

Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field.

Spatiotemporal fusion (STF) is a cost-effective way to complement the spatiotemporal resolution of multisource images, which has been employed in various applications requiring image sequences. In real-world applications, the spectral accuracy, spatial accuracy, and efficiency of STF play a critical role. Despite this, most STF methods focus on improving spectral accuracy, whereas the challenges of spatial information loss and low efficiency have received limited attention. In addition, the improvements in spectral accuracy, spatial accuracy, and efficiency in STF are contradictory, and existing STF methods cannot balance them well, which limits their reliability and applicability for various STF tasks. To solve the above-mentioned issues, this study proposes an object-level hybrid STF method (OL-HSTFM), which incorporates the efficiency advantage of the object-level fusion strategy, spectral accuracy advantage of the three-step method (Fit-FC), and the spatial accuracy advantage of the spatial and temporal adaptive reflectance fusion model. The performance of OL-HSTFM was compared with two classic STF methods and eight state-of-the-art STF methods at two sites. The experimental results indicate that OL-HSTFM outperforms the other ten methods in overall performance and has excellent efficiency. Furthermore, this study proposes a new metric that can assess the accuracy of both spatial and spectral domains in STF, which provides a more comprehensive and intuitive measurement of the quality of fused images compared to commonly used metrics.

Landslide mapping (LM) from bitemporal remote sensing images is essential for disaster prevention and mitigation. Although bitemporal change detection technology has been applied for LM, there remains room for improvement in its accuracy and automation. In this article, a multilevel feature enhancement network (MFENet) is proposed for LM based on modules built in convolutional neural networks (CNNs) like CNN-Attention. MFENet mainly consists of three modules: the postevent feature enhancement module (PFEM), the bifeature difference enhancement module (BFDEM), and the flow direction calibration module (FDCM). Specifically, the main role of PFEM is to selectively fuse postevent multilayer features to provide discriminative postevent features. BFDEM fuses the multilayer differences of both pre-event and postevent features to generate high-quality change detection features, which are sufficiently powerful to distinguish foreground from background. FDCM uses a digital elevation model to calibrate the flow direction of each pixel of the landslide detection results to complete the LM task. Experiments were conducted to test the effectiveness of MFENet on two real-world regions, Lantau Island and Sharp Peak, Hong Kong, where landslides occurred after rainstorms. Compared with other state-of-the-art general change detection methods and landslide-specific change detection methods, the proposed method outperforms all metrics, with its intersection over union reaching 87.23%. The availability of additional features and the generalization performance of MFENet are demonstrated experimentally. It is anticipated that the proposed network will further contribute to disaster prevention and mitigation.

Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic $c$-means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. First, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Second, objects at the coarsest scale are classified into changed, unchanged, and uncertain categories by the proposed MRFPCM. Third, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged, and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three datasets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach.

The increasing amount of geotagged social media data provides a possible resource for location prediction. However, existing location prediction methods rarely incorporate temporal changes in mobility patterns, which could lead to unreliable predictions. In particular, human mobility patterns have changed greatly in the COVID-19 era. We propose a novel model to predict individuals’ movements in dynamic regions of interest (ROIs), taking into account changes in activity areas and movement regularity. To address changes in the activity areas, we design a new updating strategy that can ensure the realistic extraction of an individual’s ROIs. Then, we develop an integration model for changes in the movement regularity based on two newly proposed prediction methods that consider both rapid and slow changes. The proposed integration model is evaluated based on five real-world social media datasets; three Weibo datasets related to COVID-19 collected in three Chinese cities, one Twitter dataset collected in New York and one dense GPS dataset. The results demonstrate that the proposed model can achieve better performances than state-of-the-art models, especially when mobility patterns change greatly. Combined with related pandemic data, this study will benefit pandemic prevention and control.

Timely and accurate local climate zone (LCZ) classification maps are valuable for urban climate studies. The integration of remote sensing and street-level images is promising to produce high-quality LCZ maps, since the former can efficiently capture the information of landscapes on a large-scale while the latter include ground-level details. However, due to their significant differences in spatial distributions and capture views, as well as existing sampling issues of street-level images, how to fuse them effectively is challenging and remains an uncharted research area. To address these issues and fill the gap, this study proposes an effective method to integrate satellite and street-level images for LCZ mapping. Additionally, a simple yet effective street-level image sampling method is proposed. Extensive experiments have been performed and the results demonstrate the effectiveness of the proposed data fusion method and also confirm the usefulness of fusing street-level images with satellite images in enhancing the performance of LCZ mapping. Moreover, the proposed sampling method can increase data representativeness and avoid data redundancy, thus significantly reducing the number of required images while retaining high classification accuracy. To the best of our knowledge, this study is the first attempt to integrate cross-view satellite and street-level images for LCZ mapping. The study and proposed methods can contribute to the development of multi-source data fusion for LCZ map production and further benefit urban climatic research.

Abstract Background Motivated by the need for precise epidemic control and epidemic-resilient urban design, this study aims to reveal the joint and interactive associations between urban socioeconomic, density, connectivity, and functionality characteristics and the COVID-19 spread within a high-density city. Many studies have been made on the associations between urban characteristics and the COVID-19 spread, but there is a scarcity of such studies in the intra-city scale and as regards complex joint and interactive associations by using advanced machine learning approaches. Methods Differential-evolution-based association rule mining was used to investigate the joint and interactive associations between the urban characteristics and the spatiotemporal distribution of COVID-19 confirmed cases, at the neighborhood scale in Hong Kong. The associations were comparatively studied for the distribution of the cases in four waves of COVID-19 transmission: before Jun 2020 (wave 1 and 2), Jul–Oct 2020 (wave 3), and Nov 2020–Feb 2021 (wave 4), and for local and imported confirmed cases. Results The first two waves of COVID-19 were found mainly characterized by higher-socioeconomic-status (SES) imported cases. The third-wave outbreak concentrated in densely populated and usually lower-SES neighborhoods, showing a high risk of within-neighborhood virus transmissions jointly contributed by high density and unfavorable SES. Starting with a super-spread which considerably involved high-SES population, the fourth-wave outbreak showed a stronger link to cross-neighborhood transmissions driven by urban functionality. Then the outbreak diffused to lower-SES neighborhoods and interactively aggravated the within-neighborhood pandemic transmissions. Association was also found between a higher SES and a slightly longer waiting period (i.e., the period from symptom onset to diagnosis of symptomatic cases), which further indicated the potential contribution of higher-SES population to the pandemic transmission. Conclusions The results of this study may provide references to developing precise anti-pandemic measures for specific neighborhoods and virus transmission routes. The study also highlights the essentiality of reliving co-locating overcrowdedness and unfavorable SES for developing epidemic-resilient compact cities, and the higher obligation of higher-SES population to conform anti-pandemic policies.

COVID-19 has caused a serious economic shock which challenges the resilience of businesses around the world. Understanding the spatial distribution pattern of business resilience, as well as identifying factors that promote business resilience, is crucial to economic recovery. Most existing studies mainly analyze one side of the concept of resilience, such as how businesses closed, expanded, and innovated, while no studies take all the characteristics of resilience into account and analyze them from a geographical view. To fill this gap, this study first relates the method of calculating stability in ecology to geography, and proposes a point of interest (POI)-based index to evaluate an industry’s resilience in a city. Then, with the catering industry in Hong Kong as an example, the spatial distribution of resilience in June 2020 and December 2020 is investigated using the local indicators of spatial association (LISA) approach. An ordinary least squares (OLS) regression model is adopted to identify impactful factors on resilience. The results reveal that the resilience of restaurants is quite stable in local central areas, but areas near the checking points at Shenzhen in mainland China are severely affected. Most traditional location factors had the benefit of stabilization, while hospitals had negative responses. The presented analysis framework is possible to be easily generalized to other industries or cities. The overall result of the study provides a spatial understanding which would be essential as a reference for future urban planning regarding post-pandemic recovery.

In the face of persistent challenges posed by urbanization and climate change, the contemporary era has witnessed a growing urgency for urban intelligence and sustainable development. Consequently, a plethora of smart city schedules and policies have emerged, with smart city assessment serving as a pivotal benchmark for gauging policy effectiveness. However, owing to the inherent ambiguity of the smart city definition and the complexity of application scenarios, designers and decision-makers often struggle to ascertain their desired assessment frameworks swiftly and effectively. In this context, our study undertook a comprehensive analysis and comparative assessment of 33 recently introduced or inferred evaluation frameworks, drawn from a broad spectrum of extensive and longstanding research efforts. The overarching goal was to provide valuable reference points for designers and decision-makers navigating this intricate landscape. The assessment was conducted across seven key dimensions: generalizability, comprehensiveness, availability, flexibility, scientific rigor, transparency, and interpretability. These criteria hold the potential not only to guide the development trajectory and focus of upcoming smart city assessment models but also to serve as invaluable guidelines for stakeholders evaluating the outcomes of such models. Furthermore, they can serve as robust support for designers and decision-makers in their pursuit of targeted frameworks.

To achieve land cover change detection (LCCD) with both fine spatial and temporal resolutions from remote sensing images, subpixel mapping-based approaches have been widely studied in recent years. The fine spatial but coarse temporal resolution image and the coarse spatial but fine temporal image are used to accomplish LCCD by combining their advantages. However, the performance of subpixel mapping is easily affected by the accuracy of spectral unmixing, thereby reducing the reliability of LCCD. In this article, a novel subpixel change detection scheme based on improved abundance values is proposed to tackle the aforementioned problem, in which the spatial distribution of fine spatial resolution image is borrowed to promote the accuracy of spectral unmixing. First, the coarse spatial resolution image is used to generate the original abundance image by the spectral unmixing method. Second, the spatial distribution information of the fine spatial resolution image is incorporated into the original abundance image to obtain improved abundance values. Third, the fine spatial resolution subpixel map can be generated by the subpixel mapping method using the improved abundance values. At last, the fine resolution change map can be obtained by comparing the subpixel map with the fine spatial resolution image. Experiments are conducted on a simulated dataset based on Landsat-7 images and two real datasets based on Landsat-8 and MODIS images. The results of the real datasets showed that the proposed method can effectively improve the performance of LCCD with an overall accuracy of approximately 1.26% and 0.79% to the existing methods.

Due to its high efficiency and low cost, automatic extraction of building footprints from remotely sensed imagery has long been an important means to obtain building footprint information, which can be easily implemented using existing fully convolutional network (FCN)-based methods. However, such methods suffer from imperfections and thus accurately extracting building footprints from remotely sensed imagery remains a challenging task. For example, cascaded convolutions generally cannot preserve the spatial details well, leading to blurred boundaries and omission of small buildings. Insufficient multiscale features fusion without considering semantic gaps between different level features could yield misclassification. In addition, the fixed receptive field always produces discontinuous holey extracted large buildings. To this end, we propose a novel multiscale and multipath network with boundary enhancement (MMB-Net) that accurately extracts building footprints from remotely sensed imagery. More specially, a parallel multipath feature extraction module is first designed to capture high spatial information-preserved multiscale features with less semantic distances. In addition, the receptive field is enlarged and broadened by a multiscale features enhancement module. Then, an attention-based multiscale features fusion module is built to appropriately aggregate multiscale features. Lastly, a spatial enhancement module is presented to refine the extracted building boundaries by capturing boundary information from low-level features. The proposed MMB-Net has been tested on two benchmark data sets together with other SOTA approaches. The results show that MMB-Net can achieve promising building footprints extraction performances and it outperforms the SOTA methods. The implementation of MMB-Net is available at.

Extracting buildings accurately from very high-resolution (VHR) remote sensing imagery is challenging due to diverse building appearances, spectral variability, and complex background in VHR remote sensing images. Recent studies mainly adopt a variant of the fully convolutional network (FCN) with an encoder–decoder architecture to extract buildings, which has shown promising improvement over conventional methods. However, FCN-based encoder–decoder models still fail to fully utilize the implicit characteristics of building shapes. This adversely affects the accurate localization of building boundaries, which is particularly relevant in building mapping. A contour-guided and local structure-aware encoder–decoder network (CGSANet) is proposed to extract buildings with more accurate boundaries. CGSANet is a multitask network composed of a contour-guided (CG) and a multiregion-guided (MRG) module. The CG module is supervised by a building contour that effectively learns building contour-related spatial features to retain the shape pattern of buildings. The MRG module is deeply supervised by four building regions that further capture multiscale and contextual features of buildings. In addition, a hybrid loss function was designed to improve the structure learning ability of CGSANet. These three improvements benefit each other synergistically to produce high-quality building extraction results. Experimental results on the WHU and NZ32km2 building datasets demonstrate that compared with the tested algorithms, CGSANet can produce more accurate building extraction results and achieve the best intersection over union value 91.55% and 90.02%, respectively. Experiments on the INRIA building dataset further demonstrate the ability for generalization of the proposed framework, indicating great practical potential.

To better interpret the scene and facilitate the subsequent processing of large-scale point cloud, clustering is often implemented in the preprocessing stage. However, when the original density-based spatial clustering of application with noise (DBSCAN) approach is used for point cloud clustering, it is easy to categorize closely spaced vegetation points and nonvegetation points into the same cluster by mistake. Aiming at the problem, this article presents an improved DBSCAN by embedding a strategy of class constraint, which is called CC-DBSCAN. Specially, based on the RGB and label information of each point in the training samples, by using the logistic regression model, the logistic regression color index (LRCI) is calculated for each point in the clustering samples. Then, points to be clustered are classified as vegetation points and nonvegetation points through the LRCI. Furtherly, the class information of point is introduced as a constraint for ensuring the core point and its directly density-reachable points belong to the same class, thus, solving the problem that confusion cluster of the adjacent vegetation points and nonvegetation points. We evaluate our approach on the benchmark SensatUrban dataset, where Cambridge_28 scene dataset is taken as the training set and Cambridge_18 scene dataset is as the dataset to be clustered. Experimental results show that our method achieved 97.20% purity of point cluster, which outperforms the other DBSCAN methods. At the same time, it takes only 24.25 s for clustering 2 million points, which indicates that CC-DBSCAN has high computational efficiency and good practicability.

BackgroundFollowing the recent COVID-19 pandemic, returning to normalcy has become the primary goal of global cities. The key for returning to normalcy is to avoid affecting social and economic activities while supporting precise epidemic control. Estimation models for the spatiotemporal spread of the epidemic at the refined scale of cities that support precise epidemic control are limited. For most of 2021, Hong Kong has remained at the top of the “global normalcy index” because of its effective responses. The urban-community-scale spatiotemporal onset risk prediction model of COVID-19 symptom has been used to assist in the precise epidemic control of Hong Kong. ObjectiveBased on the spatiotemporal prediction models of COVID-19 symptom onset risk, the aim of this study was to develop a spatiotemporal solution to assist in precise prevention and control for returning to normalcy. MethodsOver the years 2020 and 2021, a spatiotemporal solution was proposed and applied to support the epidemic control in Hong Kong. An enhanced urban-community-scale geographic model was proposed to predict the risk of COVID-19 symptom onset by quantifying the impact of the transmission of SARS-CoV-2 variants, vaccination, and the imported case risk. The generated prediction results could be then applied to establish the onset risk predictions over the following days, the identification of high–onset-risk communities, the effectiveness analysis of response measures implemented, and the effectiveness simulation of upcoming response measures. The applications could be integrated into a web-based platform to assist the antiepidemic work. ResultsDaily predicted onset risk in 291 tertiary planning units (TPUs) of Hong Kong from January 18, 2020, to April 22, 2021, was obtained from the enhanced prediction model. The prediction accuracy in the following 7 days was over 80%. The prediction results were used to effectively assist the epidemic control of Hong Kong in the following application examples: identified communities within high–onset-risk always only accounted for 2%-25% in multiple epidemiological scenarios; effective COVID-19 response measures, such as prohibiting public gatherings of more than 4 people were found to reduce the onset risk by 16%-46%; through the effect simulation of the new compulsory testing measure, the onset risk was found to be reduced by more than 80% in 42 (14.43%) TPUs and by more than 60% in 96 (32.99%) TPUs. ConclusionsIn summary, this solution can support sustainable and targeted pandemic responses for returning to normalcy. Faced with the situation that may coexist with SARS-CoV-2, this study can not only assist global cities in responding to the future epidemics effectively but also help to restore social and economic activities and people’s normal lives.

Currently, finding ways to effectively control the spread of Omicron in regions with low vaccination rates is an urgent issue. In this study, we use a district-level model for predicting the COVID-19 symptom onset risk to explore and control the whole process of spread of Omicron in South Africa at a finer spatial scale. We found that in the early stage of the accelerated spread, Omicron spreads rapidly from the districts at the center of human mobility to other important districts of the human mobility network and its peripheral districts. In the subsequent diffusion–contraction stage, Omicron rapidly spreads to districts with low human mobility and then mainly contracts to districts with the highest human mobility. We found that increasing daily vaccination rates 10 times mainly reduced the symptom onset risk in remote areas with low human mobility. Implementing Alert Level 5 in the three districts at the epicenter, and Alert Level 1 in the remaining 49 districts, the spatial spread related to human mobility was effectively restricted, and the daily onset risk in districts with high human mobility also decreased by 20–80%.

After the fifth wave of the COVID-19 outbreak in May 2022, the Hong Kong government decided to ease the restrictions policy step by step. The main change was to re-open some venues that people like to visit and extend the hours of operation. With the implementation of the relaxed policy, however, the number of confirmed cases rose again. As a result, further relaxation was delayed. As an evaluation of the effectiveness of the restrictions policy could be a reference for future policies balancing viral spread and functionality of society, this paper aimed to respond to this question from the spatial point distribution view. The time, from late March 2020 to February 2021, during which the related policies took place was divided into six periods based on the policy trend (tightening or relaxing). The two-variable Ripley’s Kfunction was applied for each period to explore the spatial dependence between confirmed cases and venues as changes in the spatial pattern can reveal the effect of the policy. The results show that, as time passed, the clustering degree decreased and reached its lowest level from August to mid-November 2020, then significantly increased, with the extent of clustering becoming more remarkable and the significant cluster size widening. Our results indicate that the policy had a positive effect on suppressing the spread of the virus in mid-July 2020. Then, with the virus infiltrating the community, the policy had little impact on containing the virus but likely contributed to avoid further infection.

Modelling pedestrian movement uncertainty in complex urban environments is regarded as a meaningful and challenging task regarding the promotion of geospatial data mining and analysis. However, the traditional uncertainty prediction model only takes the movement distance or speed into consideration and is not able to adapt well to time-varying measurement errors. In this paper, a deep-learning framework is proposed for modelling pedestrian movement uncertainty in large-scale indoor areas, in which a hybrid deep-learning model combines a one-dimensional Convolutional Neural Network (1D-CNN) with a long short-term memory (LSTM) network is proposed for enhancing feature extraction performance and reducing time correlation errors. The proposed framework takes human motion related measurement features into consideration, in which the moving step-length and heading information during a time period are also reconstructed and modelled as the input to the deep-learning model. Compared with state-of-art algorithms applied to different real-world trajectory datasets, the proposed deep-learning approach demonstrates much better performance of uncertainty region prediction, including the different indexes (Euclidean error distance, completeness and density) This study has leaded to the provision of an effective and practical framework for modelling trajectory uncertainty of the pedestrian in challenging urban environments, and which is expected to benefit smart city and spatial perception related applications.

PurposeInvestigation of the community-level symptomatic onset risk regarding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern, is crucial to the pandemic control in the new normal.MethodsInvestigated in this study is the spatiotemporal symptom onset risk with Omicron BA.1, BA.2, and hamster-related Delta AY.127 by a joint analysis of community-based human mobility, virus genomes, and vaccinations in Hong Kong.ResultsThe spatial spread of Omicron BA.2 was found to be 2.91 times and 2.56 times faster than that of Omicron BA.1 and Delta AY.127. Identified has been an early spatial invasion process in which spatiotemporal symptom onset risk was associated with intercommunity and cross-community human mobility of a dominant source location, especially regarding enhancement of the effects of the increased intrinsic transmissibility of Omicron BA.2. Further explored is the spread of Omicron BA.1, BA.2, and Delta AY.127 under different full and booster vaccination rate levels. An increase in full vaccination rates has primarily contributed to the reduction in areas within lower onset risk. An increase in the booster vaccination rate can promote a reduction in those areas within higher onset risk.ConclusionsThis study has provided a comprehensive investigation concerning the spatiotemporal symptom onset risk of Omicron BA.1, BA.2, and hamster-related Delta AY.127, and as such can contribute some help to countries and regions regarding the prevention of the emergence of such as these variants, on a strategic basis. Moreover, this study provides scientifically derived findings on the impact of full and booster vaccination campaigns working in the area of the reduction of symptomatic infections.

Abstract How to reduce the health risks for commuters, caused by air pollution such as PM2.5 has always been an urgent issue needing to be solved. Proposed in this study, is a novel framework which enables greater avoidance of pollution and hence assists the provision of healthy travel. This framework is based on the estimation of on‐road PM2.5 throughout the whole city. First, the micro‐scale PM2.5 is predicted by land use regression (LUR) modeling enhanced by the use of the Landsat‐8 top‐of‐atmosphere (TOA) data and microscale geographic predictors. In particular, the green view index (GVI) factor derived, the sky view factor, and the index‐based built‐up index, are incorporated within the TOA‐LUR modeling. On‐road PM2.5 distributions are then mapped in high‐spatial‐resolution. The maps obtained can be used to find healthy travel routes with less PM2.5. The proposed framework was applied in high‐density Hong Kong by Landsat 8 images. External testing was based on mobile measurements. The results showed that the estimation performance of the proposed seasonal TOA‐LUR Geographical and Temporal Weighted Regression models is at a high‐level with an R2 of 0.70–0.90. The newly introduced GVI index played an important role in these estimations. The PM2.5 distribution maps at high‐spatial‐resolution were then used to develop an application providing Hong Kong residents with healthy route planning services. The proposed framework can, likewise, be applied in other cities to better ensure people's health when traveling, especially those in high‐density cities.

Semantic segmentation of 3D mobile laser scanning point clouds is the foundational task for scene understanding in several fields. Most existing segmentation methods tend to simply stack the common point attributes, such as the coordinates and intensity, but ignore their heterogeneous. This paper presents IDA-Net, an intensity-distribution aware network that mines the uniqueness and discrepancy of these two modalities in a separate way for point cloud segmentation under indoor corridor environments. Specifically, IDA-Net consists of two key components. Firstly, an intensity-distribution aware (IDA) descriptor is proposed to mine the intensity distribution pattern. It outputs a multi-channel mask for each point to represent the intensity distribution information. Secondly, a two-stage embedding network is designed to fuse the coordinates and intensity information efficiently. It includes a guiding operation in training stage and a refining operation in testing stage. IDA-Net was evaluated on two indoor corridor areas. Experimental results show that the proposed method significantly improves the performance of segmentation. Specifically, with backbone of KPConv, IDA-Net achieves high mIoU of 90.58% and 88.94% on the above two testing areas respectively, which demonstrates the superiority of the designed IDA descriptor and two-stage embedding network.

Constructing colorized point clouds from mobile laser scanning and images is a fundamental work in surveying and mapping. It is also an essential prerequisite for building digital twins for smart cities. However, existing public datasets are either in relatively small scales or lack accurate geometrical and color ground truth. This paper documents a multisensorial dataset named PolyU-BPCoMA which is distinctively positioned towards mobile colorized mapping. The dataset incorporates resources of 3D LiDAR, spherical imaging, GNSS and IMU on a backpack platform. Color checker boards are pasted in each surveyed area as targets and ground truth data are collected by an advanced terrestrial laser scanner (TLS). 3D geometrical and color information can be recovered in the colorized point clouds produced by the backpack system and the TLS, respectively. Accordingly, we provide an opportunity to benchmark the mapping and colorization accuracy simultaneously for a mobile multisensorial system. The dataset is approximately 800 GB in size covering both indoor and outdoor environments. The dataset and development kits are available at https://github.com/chenpengxin/PolyU-BPCoMa.git.

Abstract Background The urban built environment (BE) has been globally acknowledged as one of the main factors that affects the spread of infectious disease. However, the effect of the street network on coronavirus disease 2019 (COVID-19) incidence has been insufficiently studied. Severe acute respiratory syndrome coronavirus 2, which causes COVID-19, is far more transmissible than previous respiratory viruses, such as severe acute respiratory syndrome coronavirus, which highlights the role of the spatial configuration of street network in COVID-19 spread, as it is where humans have contact with each other, especially in high-density areas. To fill this research gap, this study utilized space syntax theory and investigated the effect of the urban BE on the spatial diffusion of COVID-19 cases in Hong Kong. Method This study collected a comprehensive dataset including a total of 3815 confirmed cases and corresponding locations from January 18 to October 5, 2020. Based on the space syntax theory, six space syntax measures were selected as quantitative indicators for the urban BE. A linear regression model and Geographically Weighted Regression model were then applied to explore the underlying relationships between COVID-19 cases and the urban BE. In addition, we have further improved the performance of GWR model considering the spatial heterogeneity and scale effects by adopting an adaptive bandwidth. Result Our results indicated a strong correlation between the geographical distribution of COVID-19 cases and the urban BE. Areas with higher integration (a measure of the cognitive complexity required for a pedestrians to reach a street) and betweenness centrality values (a measure of spatial network accessibility) tend to have more confirmed cases. Further, the Geographically Weighted Regression model with adaptive bandwidth achieved the best performance in predicting the spread of COVID-19 cases. Conclusion In this study, we revealed a strong positive relationship between the spatial configuration of street network and the spread of COVID-19 cases. The topology, network accessibility, and centrality of an urban area were proven to be effective for use in predicting the spread of COVID-19. The findings of this study also shed light on the underlying mechanism of the spread of COVID-19, which shows significant spatial variation and scale effects. This study contributed to current literature investigating the spread of COVID-19 cases in a local scale from the space syntax perspective, which may be beneficial for epidemic and pandemic prevention.

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6

Global land cover (GLC) data are an indispensable resource for understanding the relationship between human activities and the natural environment. Estimating their classification accuracy is significant for studying environmental change and sustainable development. With the rapid emergence of various GLC products, the lack of high-quality reference data poses a severe risk to traditional accuracy estimation methods, in which reference data are always required. Thus, meeting the needs of large-scale, fast evaluation for GLC products becomes challenging. The triple collocation approach (TCCA) is originally applied to assess classification accuracy in earthquake damage mapping when ground truth is unavailable. TCCA can provide unbiased accuracy estimation of three classification systems when their errors are conditionally independent. In this study, we extend the idea of TCCA and test its performance in the accuracy estimation of GLC data without ground reference data. Firstly, to generate two additional classification systems besides the original GLC data, a k-order neighbourhood is defined for each assessment unit (i.e., geographic tiles), and a local classification strategy is implemented to train two classifiers based on local samples and features from remote sensing images. Secondly, to reduce the uncertainty from complex classification schemes, the multi-class problem in GLC is transformed into multiple binary-class problems when estimating the accuracy of each land class. Building upon over 15 million sample points with remote sensing features retrieved from Google Earth Engine, we demonstrate the performance of our method on WorldCover 2020, and the experiment shows that screening reliable sample points during training local classifiers can significantly improve the overall estimation with a relative error of less than 4% at the continent level. This study proves the feasibility of estimating GLC accuracy using the existing land information and remote sensing data, reducing the demand for costly reference data in GLC assessment and enriching the assessment approaches for large-scale land cover data.

Understanding the space–time pattern of the transmission locations of COVID-19, as well as the relationship between the pattern, socioeconomic status, and environmental factors, is important for pandemic prevention. Most existing research mainly analyzes the locations resided in or visited by COVID-19 cases, while few studies have been undertaken on the space–time pattern of the locations at which the transmissions took place and its associated influencing factors. To fill this gap, this study focuses on the space–time distribution patterns of COVID-19 transmission locations and the association between such patterns and urban factors. With Hong Kong as the study area, transmission chains of the four waves of COVID-19 outbreak in Hong Kong during the time period of January 2020 to June 2021 were reconstructed from the collected case information, and then the locations of COVID-19 transmission were inferred from the transmission chains. Statistically significant clusters of COVID-19 transmission locations at the level of tertiary planning units (TPUs) were detected and compared among different waves of COVID-19 outbreak. The high-risk areas and the associated influencing factors of different waves were also investigated. The results indicate that COVID-19 transmission began with the Hong Kong Island, further moved northward towards the New Territories, and finally shifted to the south Hong Kong Island, and the transmission population shows a difference between residential locations and non-residential locations. The research results can provide health authorities and policy-makers with useful information for pandemic prevention, as well as serve as a guide to the public in the avoidance of activities and places with a high risk of contagion.

Urban hotspots are regions with intensive passenger flow, sound infrastructure, and thriving business during a certain period of time, which mirror the travel behavior of residents. Taxi trajectory is one of the important data sources for urban hotspot detection. Unfortunately, it should be pointed out that quite a few of the relevant studies have ignored the temporal dynamics or network-constrained characteristics of urban hotspots, making the detecting results less reasonable and reliable. In this study, a two-phase clustering approach is proposed to detect urban hotspot with taxi trajectory. Concretely, in the first phase, spatiotemporal hierarchical density-based spatial clustering of applications with noise is utilized to cluster the trajectory points with spatial and temporal attributes, which is essential for understanding the evolution of urban hotspots over time. In the second phase, the idea of region growing is introduced to further filter noise, in which the spatial similarity between data points is measured by the route distance, considering that the trajectory data are constrained by the road network. A case study is carried out by the proposed method. Meanwhile, in combination with the Luojia1-01 night-time light remote sensing data and POI data, the reliability of the clustering results is verified and the semantic meaning of the discovered clusters is enriched. Furthermore, not only the spatiotemporal distribution but also the trip lengths and directions of the detected hotspots are explored. These findings can serve as a scientific basis for policymakers in traffic control, public facilities planning, as well as location-based service.

Monitoring pollution of PM2.5 on walkable roads is important for resident health in high-density cities. Due to the spatiotemporal resolution limitations of aerosol optical depth observation, fixed-point monitoring, or traditional mobile measurement instruments, the microscale PM2.5 distribution in the walking environment cannot be fully estimated at the fine scale. In this article, by the integration of mobile measurement data, OpenStreetMap (OSM) data, Landsat images, and other multisource data in land-use regression (LUR) models, a novel framework is proposed to estimate and map PM2.5 distribution in a typical microscale walkable environment of the high-density city Hong Kong. First, the PM2.5 data on the typical walking paths were collected by the handheld mobile measuring instruments, to be selected as the dependent variables. Second, geographic prediction factors calculated by Google Street View, OSM data, Landsat images, and other multisource data were further selected as independent variables. Then, these dependent and independent variables were put into the LUR models to estimate the PM2.5 concentration on sidewalks, footbridges, and footpaths in the microscale walkable environment. The proposed models showed high performance relative to those in similar studies (adj R2, 0.593 to 0.615 [sidewalks]; 0.641 to 0.682 [footpaths]; 0.783 to 0.797 [footbridges]). This article is beneficial for mapping PM2.5 concentration in the microscale walking environment and the identification of hot spots of air pollution, thereby helping people avoid the PM2.5 hotspots and indicating a healthier walking path.

Automatic extraction of road from multisource remote sensing data has always been a challenging task. Factors such as shadow occlusion and multisource data alignment errors prevent current deep learning-based road extraction methods from acquiring road features with high complementarity, redundancy, and crossover. Unlike previous works that capture contexts by multiscale feature fusion, we propose a dual attention dilated-LinkNet (DAD-LinkNet) to adaptively integrate local road features with their global dependencies by joint using satellite image and floating vehicle trajectory data. First, a joint least-squares feature matching-based floating vehicle trajectory correction model is used to correct the floating vehicle trajectory; then a convolutional network model DAD-LinkNet based on a dual-attention mechanism is proposed, and road features are extracted from the channel domain and spatial domain of the target image in turn by constructing a dual-attention module in the dilated convolutional layer and adopting a cascade connection; a weighted hyperparameter loss function is used as the loss function of the model; finally, the road extraction is completed based on the proposed DAD-LinkNet model. Experiments on three datasets show that the proposed DAD-LinkNet model outperforms the state-of-the-art methods in terms of accuracy and connectivity.

Wenzhong Shi et al. propose an extended Weight Kernel Density Estimation model to predict the COVID-19 onset risk, with and without the Wuhan lockdown, and corresponding symptom onset and spatial heterogeneity in 347 Chinese cities. The authors find that the lockdown delayed COVID-19 peak onset by 1–2 days and decreased onset risk by up to 21%.

Spatiotemporal fusion technique can combine the advantages of temporal resolution and spatial resolution of different images to achieve continuous monitoring for the Earth’s surface, which is a feasible solution to resolve the trade-off between the temporal and spatial resolutions of remote sensing images. In this paper, an object-based spatiotemporal fusion model (OBSTFM) is proposed to produce spatiotemporally consistent data, especially in areas experiencing non-shape changes (including phenology changes and land cover changes without shape changes). Considering different changes that might occur in different regions, multi-resolution segmentation is first employed to produce segmented objects, and then a linear injection model is introduced to produce preliminary prediction. In addition, a new optimized strategy to select similar pixels is developed to obtain a more accurate prediction. The performance of proposed OBSTFM is validated using two remotely sensed dataset experiencing phenology changes in the heterogeneous area and land cover type changes, experimental results show that the proposed method is advantageous in such areas with non-shape changes, and has satisfactory robustness and reliability in blending large-scale abrupt land cover changes. Consequently, OBSTFM has great potential for monitoring highly dynamic landscapes.

In the last decade, land cover products are produced at a global scale and updated with an unprecedent speed with the development of earth observation and mapping techniques. However, assessing large-scale land cover datasets is always a challenging task because of lack of ground truth and high dependency on manual inspection. To promote the efficiency of assessment, this study introduces a generic framework that identifies potential land cover classification errors without traditional reference data. Depending on rich features in remote sensing data, the overall procedure of can be regarded as a multi-class anomaly detection problem. To improve the performance in dealing with complex classification scheme, a pairing strategy is firstly proposed for pairwise analysis on each two classes, so that the multi-class problem is decomposed into multiple smaller binary-class problems. Secondly, the proximity matrix of each class pair is generated on the basis of extra trees model, which is applicable to mixed numerical and categorical data and provides robust proximity measurement by including multiple levels of randomness. Finally, an improved proximity-based anomaly detection algorithm is applied, and the detection results of each class pair are ensembled to obtain the final anomaly score of each land instance. Experiments show that the overall area under the receiver operating characteristic curve score reaches approximately 0.9 for synthetic datasets and 0.8 for real-life datasets. The proposed method is expected to provide users valuable data for a more efficient assessment in the absence of ground truth in practice.

Extracting buildings from remotely sensed data is a fundamental task in many geospatial applications. However, this task is resistant to automation due to variability in building shapes and the environmental complexity surrounding buildings. To solve this problem, this article introduces a novel automatic building extraction method that integrates LiDAR data and high spatial resolution imagery using adaptive iterative segmentation and hierarchical overlay analysis based on data fusion. An adaptive iterative segmentation method overcomes over- and undersegmentation based on the globalized probability of boundary contour detection algorithm. A data-fusion-based hierarchical overlay analysis extracts building candidate regions based on segmentation results. A morphological operation optimizes a building candidate region to obtain final building results. Experiments were conducted on the international society for photogrammetry and remote sensing (ISPRS) Vaihingen benchmark dataset. The extracted building footprints were compared with those extracted using the state-of-the-art methods. Evaluation results show that the proposed method achieved the highest area-based quality compared to results from the other tested methods on the ISPRS website. A detailed comparison with four state-of-the-art methods shows that the proposed method requiring no samples achieves competitive extraction results. Furthermore, the proposed method achieved a completeness of 94.1%, a correctness of 90.3%, and a quality of 85.5% over the whole Vaihingen dataset, indicating that the method is robust, with great potential in practical applications.

Tree-based machine learning and deep learning approaches are widely applied in ozone (O3) retrieval, but they cannot achieve high accuracy and interpretability simultaneously. To overcome this limitation, a tree-based ensemble deep learning model, named semi-SILDM, was proposed for O3 prediction at both national (5 km) and urban scales (250 m) in China. The Moderate Resolution Imaging Spectroradiometer (MODIS) Top of Atmosphere (TOA) measurements were first investigated through significant linear and nonlinear relationships with surface O3. To examine the actual predictive ability of the semi-SIDLM, time-based validation was employed to divide data chronologically by year into training (2018), validation (2017), and test data (2019). The semi-SIDLM predicted O3 in 2019 showed a coefficient of determination (R2) of 0.71 (0.69) and a Root Mean Square Error (RMSE) of 21.88 (26.59) µg/m3 at the national (urban) scale in China. In addition to its high accuracy, the semi-SIDLM has interpretability for retrieval results, which indicates the strong influence of the Fangshan and Tongzhou districts on the principle O3 Beijing urban area; the temporal characteristics reveal the higher contributions of May–July to O3 pollution compared to other months. The proposed model of this study will benefit further studies on O3 monitoring and deepen the understanding of its spatiotemporal characteristics.

Abstract Understanding why or how the emergence of SARS‐CoV‐2 variants has occurred and how to control them is crucial as regards the potential of global reopening. To explore and further understand the spatiotemporal dynamics of the B.1.1.7 spread in the 368 districts of Taiwan, a district‐level geographic prediction model of the risk of COVID‐19 symptom onset has been proposed. It has been found that, (a) the human mobility, epidemic alert measures, and vaccination rates all played an important role in the spatiotemporal heterogeneity of B.1.1.7 transmission; (b) for regions with high human mobility and low vaccination rates, the partial relaxation of entry quarantine measures for specific imported groups would, in fact, lead to a wide spread of B.1.1.7 with a consequent doubling of high‐onset‐risk areas and together with the overall onset risk, a further increase of more than 20% would occur; (c) compared with the closing of business places and public venues in all districts, both lockdown in those areas of high‐onset‐risk and the gathered control effects regarding other districts, the control of B.1.1.7 spread would be better enabled by an onset risk reduction of up to 91.36%. Additionally, an increase in the vaccination rate in each district by up to 5–10 times would further reduce the onset risk by 6.07%–62.22%.

In simultaneous localization and mapping (SLAM), loop closure detection is a significant yet still open problem. It contributes to construct a globally consistent and accurate map. This paper proposes a fast and compact loop closure detection method (FastLCD) based on comprehensive descriptors and machine learning to achieve reliable and precise results using 3D point cloud for indoor LiDAR mobile mapping. Comprehensive descriptors proposed in this paper encode discriminative multimodality features to describe each scan of point clouds. The specific values of descriptors of point cloud scan pairs are fed into a machine learning model. We leverage the pre-trained learning model as a classifier to distinguish whether a pair of laser scans is a loop candidate. Then, to ensure the results’ precision, a novel double-deck loop candidate verification strategy is used to reject false positives. The algorithm is evaluated on datasets of some typical indoor environments. Compared with some state-of-the-art loop closure detection algorithms, the proposed FastLCD algorithm demonstrates superior performance in precision and recall rate. Moreover, the method proposed also exhibits high time efficiency, excellent generalization performance and insensitivity to threshold changes.

Car-hailing service has increasingly become popular and fundamentally changed the way people travel in the era of sharing economy. Although such service brings convenience to people's lives, it also causes safety and property concerns. Many studies have been conducted to access the efficiency and effectiveness of car-hailing, but little has been done on its safety monitoring. However, with the rapid development of information technologies such as Internet of Things (IoT), Geographical Information Science (GIS) and automatic monitoring, a more advantageous approach than the current simple drivers screening and testing is feasible. A new model including five indexes i.e. region dangerous index, offset distance of the origin-destination, real-time speed under traffic conditions, vehicle travel time and passenger information, is therefore proposed in this paper based on big data mining of the historical vehicle GPS trajectory data. Experiments were conducted to validate the model in the Gangzha District of Nantong City, China. Several other types of data were used in the experiments, e.g. points of interest (POI), road network data and urban image. The results showed that the proposed model effectively monitored the vehicle when it was driving in a “potentially dangerous area”. In addition, the model could accurately identify the driver's abnormal driving behaviors, such as bypass and abnormal stop. The prediction accuracy of the experiments was 92.06%, among which the discrimination accuracy of the abnormal stop was 100% and that of the detour was 90.57%. All these validate the applicability of the model for future management systems for car-hailing services.

Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a distance parameter was adaptively calculated to cluster high-density points, which can reduce the uncertainty introduced by human subjective factors. Secondly, the remaining points were assigned according to the spatial characteristics of the clustered points for a more reasonable judgment of noise points. Then, to face the varying density problem, a re-segmentation strategy was designed to segment the appropriate clusters into low- and high-density clusters. Lastly, the noise points produced in the re-segmentation step were recovered to reduce unnecessary noise. Compared with other algorithms, ELV showed better performance on real-life datasets and reached 0.42 on the Silhouette coefficient (SC) indicator, with an improvement of more than 16.67%. ELV ensures reliable clustering results, especially when the density differences of the activity points are large, and can be valuable in some applications, such as location prediction and recommendation.

The data presented in this article are associated with the research article entitled “Bioactive Constituents Study of Pugionium cornutum L. Gaertn on Intestinal Motility” [1]. The aim of this data was to provide the 1D, 2D NMR and MS spectrum of novel bioactive compounds from P. cornutum. Keywords: Traditional Mongolian medicine, Pugionium cornutum L. Gaertn, Novel bioactive constituents, Thiohydantoin derivatives

The rapid increase in anthropogenic activities, socioeconomic development, and land use land cover (LULC) changes since the opening of economic reforms (1978), have changed the ecosystem service value (ESV) in Guangdong, Hong Kong, and Macao (GKHM) region located in South China. This leads to the requirement of a significant tailored analysis of ecosystem services regarding incisive and relevant planning to ensure sustainability at regional level. This study focuses on the use of Landsat satellite imagery to quantify the precise impact of LULC changes on the ecosystem services in GHKM over the past three decades (1986-2017). The most renowned established unit value transfer method has been employed to calculate the ESV. The results show that the total ecosystem service value in GHKM has decreased from 680.23 billion CNY in 1986 to 668.45 billion CNY in 2017, mainly due to the decrease in farmland and fishponds. This overall decrease concealed the more dynamic and complex nature of the individual ESV. The most significant decrease took place in the values of water supply (-22.20 billion CNY, -14.72%), waste treatment (-20.77 billion CNY, -14.63%), and food production (-7.96 billion CNY, -33.18%). On the other hand, the value of fertile soil formation and retention (6.28 billion CNY, +7.26%) and recreation and culture (5.09 billion CNY, +12.91%) increased. Furthermore, total ESV and ESV per capita decreased significantly with the continuous increase in total gross domestic product (GDP) and GDP per capita. A substantial negative correlation exists between farmland ESV and GDP indicating human encroachment into a natural and semi natural ecosystems. The results suggest that in the rapidly urbanizing region, the protection of farmland and to control the intrusion of urban areas has marked an important societal demand and a challenge to the local government. This required a pressing need for smart LULC planning and to improve policies and regulation to guarantee ecosystem service sustainability for acceptable life quality in the study area and other fast expanding urban areas in China.

Landslide trails are important elements of landslide inventory maps, providing valuable information for landslide risk and hazard assessment. Compared with traditional manual mapping, skeletonization methods offer a more cost-efficient way to map landslide trails, by automatically generating centerlines from landslide polygons. However, a challenge to existing skeletonization methods is that expert knowledge and manual intervention are required to obtain a branchless skeleton, which limits the applicability of these methods. To address this problem, a new workflow for landslide trail extraction (LTE) is proposed in this study. To avoid generating redundant branches and to improve the degree of automation, two endpoints, i.e., the crown point and the toe point, of the trail were determined first, with reference to the digital elevation model. Thus, a fire extinguishing model (FEM) is proposed to generate skeletons without redundant branches. Finally, the effectiveness of the proposed method is verified, by extracting landslide trails from landslide polygons of various shapes and sizes, in two study areas. Experimental results show that, compared with the traditional grassfire model-based skeletonization method, the proposed FEM is capable of obtaining landslide trails without spurious branches. More importantly, compared with the baseline method in our previous work, the proposed LTE workflow can avoid problems including incompleteness, low centrality, and direction errors. This method requires no parameter tuning and yields excellent performance, and is thus highly valuable for practical landslide mapping.

3D GIS has attracted increasing attention from academics, industries, and governments with the increase in the requirements for the interoperability and integration of different sources of spatial data. Three-dimensional road extraction based on multisource remote sensing data is still a challenging task due to road occlusion and topological complexity. This paper presents a novel framework for 3D road extraction by integrating LiDAR point clouds and high-resolution remote sensing imagery. First, a multiscale collaborative representation-based road probability estimation method was proposed to segment road surfaces from high-resolution remote sensing imagery. Then, an automatic stratification process was conducted to specify the layer values of each road segment. Additionally, a multifactor filtering strategy was proposed in consideration of the complexity of ground features and the existence of noise in LiDAR points. Lastly, a least-square-based elevation interpolation method is used for restoring the elevation information of road sections blocked by overpasses. The experimental results based on two datasets in Hong Kong Island show that the proposed method obtains competitively satisfactory results.

In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework is proposed that includes three methods. First, a rule-based approach is proposed to identify the home location of the elderly cardholders based on individual travel pattern. The result has strong correlation with the real elderly population. Second, the clustering method is adopted to group bus stops based on the elderly travel flow. The center points of clusters are utilized to construct a Voronoi diagram. Third, a quasi-gravity model is proposed to reveal the elderly mobility between regions, using the public facilities index. The model measures the elderly travel number between regions, according to public facilities index on the basis of the total number of point of interest (POI) data. Beijing is used as an example to demonstrate the applicability of the proposed methods, and the methods can be widely used for urban planning, design and management regarding the aging population.

Remote sensing change detection (CD) plays an important role in Earth observation. In this paper, we propose a novel fusion approach for unsupervised CD of multispectral remote sensing images, by introducing majority voting (MV) into fuzzy topological space (FTMV). The proposed FTMV approach consists of three principal stages: (1) the CD results of different difference images produced by the fuzzy C-means algorithm are combined using a modified MV, and an initial fusion CD map is obtained; (2) by using fuzzy topology theory, the initial fusion CD map is automatically partitioned into two parts: a weakly conflicting part and strongly conflicting part; (3) the weakly conflicting pixels that possess little or no conflict are assigned to the current class, while the pixel patterns with strong conflicts often misclassified are relabeled using the supported connectivity of fuzzy topology. FTMV can integrate the merits of different CD results and largely solve the conflicting problem during fusion. Experimental results on three real remote sensing images confirm the effectiveness and efficiency of the proposed method.

Artificial intelligence is widely applied to estimate ground-level fine particulate matter (PM2.5) from satellite data by constructing the relationship between the aerosol optical thickness (AOT) and the surface PM2.5 concentration. However, aerosol size properties, such as the fine mode fraction (FMF), are rarely considered in satellite-based PM2.5 modeling, especially in machine learning models. This study investigated the linear and non-linear relationships between fine mode AOT (fAOT) and PM2.5 over five AERONET stations in China (Beijing, Baotou, Taihu, Xianghe, and Xuzhou) using AERONET fAOT and 5-year (2015–2019) ground-level PM2.5 data. Results showed that the fAOT separated by the FMF (fAOT = AOT × FMF) had significant linear and non-linear relationships with surface PM2.5. Then, the Himawari-8 V3.0 and V2.1 FMF and AOT (FMF&AOT-PM2.5) data were tested as input to a deep learning model and four classical machine learning models. The results showed that FMF&AOT-PM2.5 performed better than AOT (AOT-PM2.5) in modelling PM2.5 estimations. The FMF was then applied in satellite-based PM2.5 retrieval over China during 2020, and FMF&AOT-PM2.5 was found to have a better agreement with ground-level PM2.5 than AOT-PM2.5 on dust and haze days. The better linear correlation between PM2.5 and fAOT on both haze and dust days (dust days: R = 0.82; haze days: R = 0.56) compared to AOT (dust days: R = 0.72; haze days: R = 0.52) partly contributed to the superior accuracy of FMF&AOT-PM2.5. This study demonstrates the importance of including the FMF to improve PM2.5 estimations and emphasizes the need for a more accurate FMF product that enables superior PM2.5 retrieval.