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  1. Article ; Online: Landslide susceptibility mapping with feature fusion transformer and machine learning classifiers incorporating displacement velocity along Karakoram highway

    Yulong Zhou / Muhammad Afaq Hussain / Zhanlong Chen

    Geocarto International, Vol 38, Iss

    2023  Volume 1

    Abstract: AbstractThe Karakoram Highway (KKH) is a pivotal gateway within the framework of the China-Pakistan Economic Corridor. Nevertheless, its distinct and intricate geographical characteristics make it susceptible to recurrent landslides. As an essential tool, ...

    Abstract AbstractThe Karakoram Highway (KKH) is a pivotal gateway within the framework of the China-Pakistan Economic Corridor. Nevertheless, its distinct and intricate geographical characteristics make it susceptible to recurrent landslides. As an essential tool, landslide susceptibility mapping (LSM) is significant in managing and alleviating landslides near the KKH. Moreover, landslide conditioning factors (LCFs) are crucial determinants influencing the outcomes of LSM. However, existing methods primarily rely on machine learning algorithms, which do not adequately account for the intricate spatial characteristics and patterns between LCFs and landslide occurrences. In response, this study introduces the feature fusion transformer (FFTR) module, constructed based on the foundations of the transformer framework, to fuse the spatial information features of all LCFs. Subsequently, the abstract high-level spatial features obtained are fed into diverse machine learning classifiers, including random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision trees (GBDT), categorical boosting (CatBoost), and extremely randomized trees (ET), to generate landslide susceptibility maps. Displacement velocity calculated by Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) is incorporated in the LSM. The results demonstrate that FFTR-RF achieves premium performance in the area under the curve (AUC) (94%), accuracy (87.31%), precision (87.21%), recall (88.02%), and F1-score (87.61%). Incorporating displacement velocity into LSM results predicted by models enhances the comprehensiveness of LSM. These methods will furnish early warning systems for landslide disasters along the KKH, thus aiding recommendations for mitigating landslides’ social and economic losses.
    Keywords Feature fusion transformer ; random forest ; landslide susceptibility mapping ; Karakoram highway ; displacement velocity ; Physical geography ; GB3-5030
    Subject code 910
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher Taylor & Francis Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Deep Learning and Machine Learning Models for Landslide Susceptibility Mapping with Remote Sensing Data

    Muhammad Afaq Hussain / Zhanlong Chen / Ying Zheng / Yulong Zhou / Hamza Daud

    Remote Sensing, Vol 15, Iss 4703, p

    2023  Volume 4703

    Abstract: Karakoram Highway (KKH) is an international route connecting South Asia with Central Asia and China that holds socio-economic and strategic significance. However, KKH has extreme geological conditions that make it prone and vulnerable to natural ... ...

    Abstract Karakoram Highway (KKH) is an international route connecting South Asia with Central Asia and China that holds socio-economic and strategic significance. However, KKH has extreme geological conditions that make it prone and vulnerable to natural disasters, primarily landslides, posing a threat to its routine activities. In this context, the study provides an updated inventory of landslides in the area with precisely measured slope deformation (Vslope), utilizing the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) and PS-InSAR (persistent scatterer interferometric synthetic aperture radar) technology. By processing Sentinel-1 data from June 2021 to June 2023, utilizing the InSAR technique, a total of 571 landslides were identified and classified based on government reports and field investigations. A total of 24 new prospective landslides were identified, and some existing landslides were redefined. This updated landslide inventory was then utilized to create a landslide susceptibility model, which investigated the link between landslide occurrences and the causal variables. Deep learning (DL) and machine learning (ML) models, including convolutional neural networks (CNN 2D), recurrent neural networks (RNNs), random forest (RF), and extreme gradient boosting (XGBoost), are employed. The inventory was split into 70% for training and 30% for testing the models, and fifteen landslide causative factors were used for the susceptibility mapping. To compare the accuracy of the models, the area under the curve (AUC) of the receiver operating characteristic (ROC) was used. The CNN 2D technique demonstrated superior performance in creating the landslide susceptibility map (LSM) for KKH. The enhanced LSM provides a prospective modeling approach for hazard prevention and serves as a conceptual reference for routine management of the KKH for risk assessment and mitigation.
    Keywords convolutional neural network ; recurrent neural networks ; landslide susceptibility mapping ; extreme gradient boosting ; random forest ; Science ; Q
    Language English
    Publishing date 2023-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

    Zhanlong CHEN / Shuangjiang LI / Yongyang XU / Daozhu XU / Chao MA / Junli ZHAO

    Journal of Geodesy and Geoinformation Science, Vol 6, Iss 2, Pp 51-

    2023  Volume 61

    Abstract: The exploration of building detection plays an important role in urban planning, smart city and military. Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images, we ... ...

    Abstract The exploration of building detection plays an important role in urban planning, smart city and military. Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images, we present an effective YOLOv3 framework, corner regression-based YOLOv3 (Correg-YOLOv3), to localize dense building accurately. This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box. By extending output dimensions, the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile. Finally, we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively. The experimental results achieve high performance in precision (96.45%), recall rate (95.75%), F1 score (96.10%) and average precision (98.05%), which were 2.73%, 5.4%, 4.1% and 4.73% higher than that of YOLOv3. Therefore, our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.
    Keywords |high resolution remote sensing image|correg-yolov3|corner regression|dense buildings|object detection ; Science ; Q ; Geodesy ; QB275-343
    Subject code 720
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher Surveying and Mapping Press
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: The Precise Representation Model of Topological Relations of Complex Planar Objects

    Zhanlong CHEN, Wen YE

    Journal of Geodesy and Geoinformation Science, Vol 2, Iss 3, Pp 18-

    2019  Volume 30

    Abstract: For complex planar objects, which are composed of simple spatial objects, the existent models of topological relations may not be able to describe some topological attributes of complex objects well. Taking the topological content between complex objects ...

    Abstract For complex planar objects, which are composed of simple spatial objects, the existent models of topological relations may not be able to describe some topological attributes of complex objects well. Taking the topological content between complex objects into account, this paper presents a model of basic topological relations between line/planar objects, and then in which the basic topological relations and the concept of the overlapping area are leveraged to describe the topological relations of simple planar objects. The definition of traversing of the hole’s boundary and planar with a hole is used to describe the topological relations between complex planar objects. Finally, the five basic topological relationship description modes of complex planar objects are summarized to realize description of the details of topological relations between partitions of complex planar objects.
    Keywords |spatial combination|topological relation|boundary intersection|precise representation ; Science ; Q ; Geodesy ; QB275-343
    Subject code 514
    Language English
    Publishing date 2019-09-01T00:00:00Z
    Publisher Surveying and Mapping Press
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: PS-InSAR-Based Validated Landslide Susceptibility Mapping along Karakorum Highway, Pakistan

    Muhammad Afaq Hussain / Zhanlong Chen / Run Wang / Muhammad Shoaib

    Remote Sensing, Vol 13, Iss 4129, p

    2021  Volume 4129

    Abstract: Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data ... ...

    Abstract Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data availability problems. A comprehensive landslide inventory and a landslide susceptibility mapping (LSM) along the Karakorum Highway were created in recent research. The extreme gradient boosting (XGBoost) and random forest (RF) models were used to compare and forecast the association between causative parameters and landslides. These advanced machine learning (ML) models can measure environmental issues and risks for any area on a regional scale. Initially, 74 landslide locations were determined along the KKH to prepare the landslide inventory map using different data. The landslides were randomly divided into two sets for training and validation at a proportion of 7/3. Fifteen landslide conditioning variables were produced for susceptibility mapping. The interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technique investigated the deformation movement of extracted models in the susceptible zones. It revealed a high line of sight (LOS) deformation velocity in both models’ sensitive zones. For accuracy comparison, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve approach was used, which showed 93.44% and 92.22% accuracy for XGBoost and RF, respectively. The XGBoost method produced superior results, combined with PS-InSAR results to create a new LSM for the area. This improved susceptibility model will aid in mitigating the landslide disaster, and the results may assist in the safe operation of the highway in the research area.
    Keywords Karakorum Highway ; susceptibility mapping ; interferometric synthetic aperture radar ; extreme gradient boosting ; random forest ; Science ; Q
    Subject code 333
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Sentinel-1A for monitoring land subsidence of coastal city of Pakistan using Persistent Scatterers In-SAR technique

    Muhammad Afaq Hussain / Zhanlong Chen / Muhammad Shoaib / Safeer Ullah Shah / Junaid Khan / Zheng Ying

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    2022  Volume 18

    Abstract: Abstract Karachi is located in the southern part of Pakistan along the Arabian Sea coast. Relevant institutions are concerned about the possibility of ground subsidence in the city, contributing to the comparative sea-level rise. So yet, no direct ... ...

    Abstract Abstract Karachi is located in the southern part of Pakistan along the Arabian Sea coast. Relevant institutions are concerned about the possibility of ground subsidence in the city, contributing to the comparative sea-level rise. So yet, no direct measurement of the subsidence rate and its relation to city submergence danger has been made. SAR (Synthetic Aperture Radar) interferometry is a powerful method for obtaining millimeter-accurate surface displacement measurements. The Sentinel-1 satellite data provide extensive geographical coverage, regular acquisitions, and open access. This research used the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technology with Sentinel-1 SAR images to monitor ground subsidence in Karachi, Pakistan. The SARPROZ software was used to analyze a series of Sentinel-1A images taken from November 2019 to December 2020 along ascending and descending orbit paths to assess land subsidence in Karachi. The cumulative deformation in Line of Sight (LOS) ranged from − 68.91 to 76.06 mm/year, whereas the vertical deformation in LOS ranged from − 67.66 to 74.68 mm/year. The data reveal a considerable rise in subsidence from 2019 to 2020. The general pattern of subsidence indicated very high values in the city center, whereas locations outside the city center saw minimal subsidence. Overall, the proposed technique effectively maps, identifies, and monitors land areas susceptible to subsidence. This will allow for more efficient planning, construction of surface infrastructure, and control of subsidence-induced risks.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Analyzing Spatial Community Pattern of Network Traffic Flow and Its Variations across Time Based on Taxi GPS Trajectories

    Wenhao Yu / Menglin Guan / Zhanlong Chen

    Applied Sciences, Vol 9, Iss 10, p

    2019  Volume 2054

    Abstract: The transport system is a critical component of the urban environment in terms of its connectivity, aggregation, and dynamic functions. The transport system can be considered a complex system due to the massive traffic flows generated by the spatial ... ...

    Abstract The transport system is a critical component of the urban environment in terms of its connectivity, aggregation, and dynamic functions. The transport system can be considered a complex system due to the massive traffic flows generated by the spatial interactions between land uses. Benefiting from the recent development of location-aware sensing technologies, large volumes of traffic flow data (e.g., taxi trajectory data) have been increasingly collected in spatial databases, which provides new opportunities to interpret transport systems in cities. This paper aims to analyze network traffic flow from the perspective of the properties of spatial connectivity, spatial aggregation, and spatial dynamics. To this end, we propose a three level framework to mine intra-city vehicle trajectory data. More specifically, the first step was to construct the network traffic flow, with nodes and edges representing the partitioned regions and associated traffic flows, respectively. We then detected community structures of network traffic flow based on their structural and traffic volume properties. Finally, we analyzed the variations of those communities across time for the dynamic transport system. Through experiments in Beijing city, we found that the method is effective in interpreting the mechanisms of urban space, and can provide references for administrative divisions.
    Keywords traffic flows ; taxi trajectory ; float cars ; spatial community ; transport system ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 380
    Language English
    Publishing date 2019-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

    Zhanlong Chen / Xiaochuan Ma / Liang Wu / Zhong Xie

    ISPRS International Journal of Geo-Information, Vol 8, Iss 2, p

    2019  Volume 98

    Abstract: Accurate and reasonable clustering of spatial data results facilitates the exploration of patterns and spatial association rules. Although a broad range of research has focused on the clustering of spatial data, only a few studies have conducted a deeper ...

    Abstract Accurate and reasonable clustering of spatial data results facilitates the exploration of patterns and spatial association rules. Although a broad range of research has focused on the clustering of spatial data, only a few studies have conducted a deeper exploration into the similarity approach mechanism for clustering polygons, thereby limiting the development of spatial clustering. In this study, we propose a novel fuzzy similarity approach for spatial clustering, called Extend Intuitionistic Fuzzy Set-Interpolation Boolean Algebra (EIFS-IBA). When discovering polygon clustering patterns by spatial clustering, this method expresses the similarities between polygons and adjacent graph models. Shape-, orientation-, and size-related properties of a single polygon are first extracted, and are used as indices for measuring similarities between polygons. We then transform the extracted properties into a fuzzy format through normalization and fuzzification. Finally, the similarity graph containing the neighborhood relationship between polygons is acquired, allowing for clustering using the proposed adjacency graph model. In this paper, we clustered polygons in Staten Island, United States. The visual result and two evaluation criteria demonstrated that the EIFS-IBA similarity approach is more expressive compared to the conventional similarity (ConS) approach, generating a clustering result more consistent with human cognition.
    Keywords clustering ; similarity approaches ; EIFS-IBA ; Geography (General) ; G1-922
    Subject code 006
    Language English
    Publishing date 2019-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Realization and application of geological cloud platform

    Gaodian Zhou / Xinqing Wang / Weitao Chen / Xianju Li / Zhanlong Chen

    Big Earth Data, Vol 4, Iss 4, Pp 464-

    2020  Volume 478

    Abstract: In recent years, with the progress of computer technology, some traditional industries such as geology are facing changes in industrial structure and application mode. So we try to apply big data and virtualization technology in the field of geoscience. ... ...

    Abstract In recent years, with the progress of computer technology, some traditional industries such as geology are facing changes in industrial structure and application mode. So we try to apply big data and virtualization technology in the field of geoscience. This study aims at addressing the existing problems in geological applications, such as data sharing, data processing and computing performance. A Geological Cloud Platform has been designed and realized preliminarily with big data and virtualization technology. The application of the Geological Cloud Platform can be divided into two parts: 1) to nest the geological computing model in cloud platform and visualize the results and 2) to use relevant software to conduct data analysis and processing in virtual machines of Windows or Linux system. Finally, we prospect Carlin-type deposits in Nevada by using the spatial data model ArcSDM in the virtual machine.
    Keywords geological cloud platform ; virtualization technology ; big data technology ; geological application ; carlin-type deposits ; arcsdm ; Geography. Anthropology. Recreation ; G ; Geology ; QE1-996.5
    Subject code 550
    Language English
    Publishing date 2020-10-01T00:00:00Z
    Publisher Taylor & Francis Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Service Area Delimitation of Fire Stations with Fire Risk Analysis

    Wenhao Yu / Yujie Chen / Zhanlong Chen / Zelong Xia / Qi Zhou

    International Journal of Environmental Research and Public Health, Vol 17, Iss 6, p

    Implementation and Case Study

    2020  Volume 2030

    Abstract: Under the rapid development of urbanization, fire service becomes one of the biggest contributive factors to personal health and property safety. A reasonable plan of fire services should first address the issue of service area delimitation for fire ... ...

    Abstract Under the rapid development of urbanization, fire service becomes one of the biggest contributive factors to personal health and property safety. A reasonable plan of fire services should first address the issue of service area delimitation for fire emergency facilities. Specifically, there are two key factors for fire services including rescue efficiency and load balancing, which are usually handled by the space partitioning methods (e.g., Voronoi diagram). The traditional methods tend to model the space in a homogeneous plane with Euclidean distance, while in reality, the movement of rescuing is constrained by the street network. In addition, the built environment is complex by its variation of fire risk across places. Therefore, we propose a novel constrained Voronoi diagram for fire service area delimitation by adding the datasets of street network and historical fire incidents. Considering the prior knowledge that a fire engine is expected to reach the location of incident within five minutes, which is also called Golden 5 min, we propose a network partitioning algorithm which is able to increase the five-minute coverage of fire stations. Through a case study in Nanjing, China, we demonstrate the practicability of the proposed method in delimitating service areas of fire stations across time.
    Keywords fire stations ; fire incident ; service area ; emergency facilities ; Medicine ; R
    Subject code 690
    Language English
    Publishing date 2020-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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