GIS Application to Regional Geological Structure Relationship Modelling Considering Semantics

He, Handong; Hu, Di; Lv, Guonian

doi:10.3390/ijgi7070267

Cited by 4 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the annotation system is determined before labeling and remains unchanged in the labeling process to ensure the consistency of labeling. By comprehensively referring to the relevant classic literature in natural hazards 37 – 40 and the annotation characteristics of NHNER, this paper formulates the following annotation system: …”

Section: Methodsmentioning

confidence: 99%

Deep learning-based methods for natural hazard named entity recognition

Sun

Liu

Cui

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide reference for natural hazard mitigation. The method of named entity recognition has many challenges, such as fast change, multiple types and various forms of named entities. This can introduce difficulties in research of natural hazard named entity recognition. To address the above problem, this paper constructed a natural disaster annotated corpus for training and evaluation model, and selected and compared several deep learning methods based on word vector features. A deep learning method for natural hazard named entity recognition can automatically mine text features and reduce the dependence on manual rules. This paper compares and analyzes the deep learning models from three aspects: pretraining, feature extraction and decoding. A natural hazard named entity recognition method based on deep learning is proposed, namely XLNet-BiLSTM-CRF model. Finally, the research hotspots of natural hazards papers in the past 10 years were obtained through this model. After training, the precision of the XLNet-BilSTM-CRF model is 92.80%, the recall rate is 91.74%, and the F1-score is 92.27%. The results show that this method, which is superior to other methods, can effectively recognize natural hazard named entities.

show abstract

Section: Methodsmentioning

confidence: 99%

Deep learning-based methods for natural hazard named entity recognition

Sun

Liu

Cui

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Geographic information system (GIS) geospatial tools are employed for the analysis of structural landform relationships. These specifically focus on the interrelationship between structural elements and geomorphologic features, such as basins and valleys, to unveil the spatial interrelation of elements within structural landforms [28][29][30][31]. This analysis makes it possible to express the forms, elements, and relationships of structural landforms through GIS.…”

Section: Introductionmentioning

confidence: 99%

GIS Approach for Expressing Structural Landforms: Forms, Elements, and Relationships

Liu,

Lu,

Meng

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

A structural landform is defined by its surface morphology, controlled by tectonics, lithology (arrangement and resistance), and folded structures, and demonstrated by the characteristics and relationships between geological and geomorphic elements. It is very important to use geographic information system (GIS) technology to accurately describe and express elements of structural landforms and their relationships. In this study, a GIS approach for expressing structural landforms, based on “forms–elements–relationships”, was developed. The contributions of this paper are as follows: (1) Combined with the surface morphological characteristics, the structural landforms were abstracted into geological and geomorphic elements, and the characteristics and relationships of these elements were analyzed. (2) The elements of structural landforms and their relationships were abstracted into spatial objects and topological relationships. The spatial objects of the structural landform were designed based on the types and characteristics of structural landform elements. The topological relationships were developed based on the definition of the structural landform morphotype. (3) The structural landform markup language (SLML) method of “forms–elements–relationships” was created. (4) Two typical structural landforms, namely, Qixia Mountain and Gaoli Mountain, were used as examples to verify the feasibility and effectiveness of the GIS approach for expressing structural landforms. This paper describes and expresses the “forms–elements–relationships” of structural landforms from the perspective of GIS, which is expected to promote the joint development of structural geomorphology and GIS.

show abstract

Semantic enrichment of building and city information models: A ten-year review

Xue

2021

Advanced Engineering Informatics

View full text Add to dashboard Cite

GIS Application to Regional Geological Structure Relationship Modelling Considering Semantics

Cited by 4 publications

References 23 publications

Deep learning-based methods for natural hazard named entity recognition

Deep learning-based methods for natural hazard named entity recognition

GIS Approach for Expressing Structural Landforms: Forms, Elements, and Relationships

Semantic enrichment of building and city information models: A ten-year review

Contact Info

Product

Resources

About