Dimensionality Reduction and Extraction of Engineering Remote Sensing Data Based on Building Information Modeling and Geographical Information System

Wang, Yonghua; Li, Yanfang; Zhang, Bing; Xing, Pengxiang

doi:10.1155/2022/1319087

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…BIM Technology includes the relevant building settings and spatial interfaces of all links such as architectural design, construction, and management. Simplify the BIM model for different research directions, use the BIM model to process remote sensing data, filter relevant information, and obtain the relevant key elements of urban construction land [13]. The key element extraction and data conversion process of BIM Technology is shown in Figure 2.…”

Section: Bim Key Element Extraction and Data Conversionmentioning

confidence: 99%

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

Zhang¹,

Wan

2022

Journal of Sensors

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In order to solve the problem of complex extraction caused by large feature dimension of remote sensing data, this paper proposes a dimension compression extraction method of urban building land remote sensing data under BIM Technology. Firstly, the remote sensing data is imported into the BIM model for lightweight processing to obtain the element information required for urban construction land and then analyze the urban construction land data, extract the key elements of BIM Technology through semantic filtering, and use the triangulation method to transform the remote sensing data into the triangulation model that can be processed by GIS model. Finally, the random projection method is used to reduce the dimension and compress the remote sensing data, and the remote sensing data extraction of urban construction land is realized through dictionary learning, vocabulary coding, and feature extraction. The experimental results show that the accuracy of extracting different land use types by this method is more than 99%, while the accuracy of extracting different land use types by depth learning method and PLS method is less than 98.5%. In addition, the signal-to-noise ratio of the image extracted by this method is significantly higher than that by depth learning method and PLS method. Conclusion. This method can effectively compress and extract the urban construction land in the remote sensing data, and the extraction accuracy of remote sensing data is high. It provides a technical basis for the approval of urban construction planning. It has the advantages of simple feature extraction and effective differentiation of ground objects.

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Section: Bim Key Element Extraction and Data Conversionmentioning

confidence: 99%

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

Zhang¹,

Wan

2022

Journal of Sensors

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“…The application of remote sensing and geographic information systems (GIS) is a critical component of modern forest monitoring and management (Al-Ghanmi and Al-Jabri, 2019; Kundu et al, 2020;Vancutsem et al, 2021;Trivedi et al, 2022;Wang et al, 2022). Remote sensing relies on aerial sensors to gather information about forests from a distance, while GIS is a powerful tool for organizing, managing, and analyzing geospatial data (Quamar et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…This helps to gain a deeper understanding of the causes of forest dynamics, such as human interventions, climate change, or natural variations (Nandasena et al, 2022;Ojeh et al, 2022;Alarcón Borges et al, 2023;Darem et al, 2023). The application of remote sensing and GIS in assessing forest dynamics provides detailed and reliable information, enabling more effective forest management and promoting the conservation of precious natural resources (Al-Ghanmi and Al-Jabri, 2019; Kundu et al, 2020;Wang et al, 2022;Fontgalland et al, 2023). It plays a crucial role in protecting and preserving the world's invaluable forest resources.…”

Section: Introductionmentioning

confidence: 99%

Assessing forest changes in Yok Don National Park and surrounding areas, Dak Lak province, Vietnam

Duong,

Son

2024

J. Degrade. Min. Land Manage.

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Forests serve as vital ecosystems, playing a pivotal role in preserving biodiversity, maintaining air and water quality, and regulating the climate, all while delivering many benefits to humanity and serving as a critical resource for diverse industries. This study investigated changes in forest cover within Yok Don National Park and surrounding areas in Dak Lak province, Vietnam, over the period from 2001 to 2021. The primary objective was to identify decadal shifts in forest cover through the utilization of freely accessible satellite remote sensing data and geographic information systems (GIS) tools. The maximum likelihood classification technique was employed to categorize Landsat images captured in 2001, 2011, and 2021. Furthermore, we evaluated the efficacy of the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) based classifications in detecting forest cover changes when compared to supervised classification. Initial estimates placed forest cover at 2796.91 km2, constituting 88.44% of the total land area in 2001, which subsequently declined to 2609.97 km2 (82.53%) in 2011, and further reduced to 1843.57 km2 (58.29%) in 2021. Severely reduced forest cover is often associated with the expansion of agriculture on the forest edge; other contributing factors include logging and illegal production land. Notably, the NDVI outperformed the SAVI in detecting forest cover changes. The findings of this research are anticipated to contribute to ongoing studies and enhance the effective monitoring of this protected forest area, thus providing invaluable insights for decision-makers.

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Dimensionality Reduction and Extraction of Engineering Remote Sensing Data Based on Building Information Modeling and Geographical Information System

Cited by 2 publications

References 19 publications

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

Assessing forest changes in Yok Don National Park and surrounding areas, Dak Lak province, Vietnam

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