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Tytuł artykułu

Scene analysis of landslide geoscience and characterization of scene evolution

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The premise of ecological protection and sustainable development in coal mining areas is to find the law of surface damage, damage speed and damage mechanism, and then put forward preventive measures in a targeted manner. Taking the landslide in the coal mining mountain area of Shanxi Province as an example, this paper comprehensively integrates the disaster-pregnant environment and disaster information of the landslide, and constructs a multi-level model of the scene through the analysis of the landslide geology scene. The object features characterize the evolution process of landslides in terms of stratigraphic structure changes, surface changes, texture changes, shape characteristics, spectral characteristics, color characteristics, etc., the typical landslide types in Shanxi coal mining areas are verified by examples. The comprehensive consideration of geology, geographical environment, ecological environment, disaster environment and the damage of landslides to other important ground features, with the advent of the era of big data, various data obtained from air, space and ground monitoring can be applied to research. Through the cognition of landslide spatial scenes, it is helpful to understand the influence mechanism of external events on landslide activities, so as to better predict the spatial and temporal characteristics of landslide activities.
Czasopismo
Rocznik
Strony
1539--1564
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Institute of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
autor
  • Institute of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
autor
  • Institute of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
Bibliografia
  • 1. Bahmanyar R, Cui S, Datcu M (2015) A comparative study of bag-of-words and bag-of-topics models of EO image patches. IEEE Geosci Remote Sens Lett 12(6):1357–1361
  • 2. Chen S, Tian Y (2015) Pyramid of spatial relatons for scene-level land use classification. IEEE Trans Geosci Remote Sens 53(4):1947–1957
  • 3. Dalei P (2018) Research on early identification of potential hidden dangers of loess landslides - taking Heifangtai, Gansu as an example. Chengdu University of Technology, Chengdu
  • 4. Gómez C, White JC, Wulder MA (2016) Optical remotely sensed time series data for land cover classification: a review. ISPRS J Photogramm Remote Sens 116:55–72
  • 5. Guonian L (2017) Re-perspective of surveying and mapping geographic information from the perspective of geography. J Surv Mapp 46(10):1549–1556
  • 6. Guonian L, Zhaoyuan Y, Linwang Y (2018) Is the future of cartography a scenario study. J Earth Inform Sci 20(01):1–6
  • 7. Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42(1–2):177–196
  • 8. Huang Y (2020) Geographical scene data model construction and ontology expression. Nanjing Normal University, Nanjing
  • 9. Jianmei T (2019) Construction and application of earthquake disaster scenario knowledge graph considering the needs of multiple types of users. Southwest Jiaotong University, Chongqing
  • 10. Jiao H, Zhong Y, Zhang L (2012) Artificial DNA computing-based spectral encoding and matching algorithm for hyperspectral remote sensing data. IEEE Trans Geosci Remote Sens 50(10):4085–4104
  • 11. Lipson P, Grimson E, Sinha P, (1997) Configuration based scene classification and image indexing, In: IEEE Computer Society Conference on Computer Vison and Pattern Recognition Puerto Rico.
  • 12. Liqiang T, Zhaocheng G (2013) A study of remote sensing image features of typical landslides. Remote Sens Land Resour 25(1):86–92
  • 13. Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870
  • 14. Othman E, Bazi Y, Alajlan N, Alhichri H, Melgani F (2016) Using convolutional features and a sparse autoencoder for land-use scene classification. Int J Remote Sens 37(10):2149–2167
  • 15. Paisitkriangkrai S, Sherrah J, Janney P, Hengel A (2015) Effective semantic pixel labelling with convolutional networks and conditional random fields. In: Workshops of IEEE International Conference on Computer Vision and Pattern Recognition, pp 36–43.
  • 16. Qing Z (2019) Uav remote sensing data supports landslide VR scene exploration and analysis method. J Wuhan Univ (Inform Sci Ed). 44(07).
  • 17. Qing Z, Haowei Z, Yulin D, Xiao X, Fei L, Liguo Z, Haifeng Li (2019) A review of analysis methods for major Landslide hazards. J Surv Mapp 48(12):1551–1561
  • 18. Smith JR, Li C (1999) Image classification and querying using composite region templates. Comput Vis Image Underst 75:165–174
  • 19. Tao C, Pan H, Li Y, Zou Z (2015) Unsupervised spectral–spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification. IEEE Geosci Remote Sens Lett 12(12):2438–2442
  • 20. Wilkinson G (2005) Results and implications of a study of 15 years of satellite image classification experiments. IEEE Trans Geosci Remote Sens 43(3):433–440
  • 21. Wu J, Rehg J-M (2011) Centrist: a visual descriptor for scene categorization. IEEE Trans Pattern Anal Mach Intell 33(8):1489–1501
  • 22. Yushi C, Zhouhan L, Xing Z, Gang W, Yanfeng G (2014) Deep learning-based classification of hyperspectral data. IEEE J Select Top Appl Earth Observ Remote Sens 7(6):2094–2107
  • 23. Zan Z (2019) Extraction of road landslide elements in karst areas by 3D laser scanning technology. Sci Surveying and Mapping. 44(11).
  • 24. Zhang L, Huang X, Huang B, Li P (2006) A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens 44(10):2950
  • 25. Zhang F, Du B, Zhang L (2016) Scene classification via a gradient boosting random convolutional network framework. IEEE Trans Geosci Remote Sens 54(3):1793–1802
  • 26. Zhong Y, Cui M, Zhu Q, Zhang L (2015a) Scene classification based on multifeature probabilistic latent semantic analysis for high spatial resolution remote sensing images. J Appl Remote Sens 9(1):095064–095064
  • 27. Zhong Y, Zhu Q, Zhang L (2015b) Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery. IEEE Trans Geosci Remote Sens 53(11):6207–6222
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-296ed761-7008-4b6f-bf63-58139e01bd70
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