Deriving multi‐scale GEODATA from TOPO‐250K road network Data

Kazemi, S.; Lim, Shen Hin

doi:10.1080/14498596.2007.9635111

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…The scale of the predisposing factors directly influences the map elements' representation and detail, as well as the choice of the scale of analysis of the final results (Leitner, 2004;Stoter et al, 2014). The choice in the level of detail will also constrain the modeling results.…”

Section: Introductionmentioning

confidence: 99%

Effects of different land use and land cover data on the landslide susceptibility zonation of road networks

Meneses

Pereira

Reis

2019

Nat. Hazards Earth Syst. Sci.

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Abstract. This work evaluates the influence of land use and land cover (LUC) data with different properties on the landslide susceptibility zonation of the road network in the Zêzere watershed (Portugal). The information value method was used to assess the landslide susceptibility using two models: one including detailed LUC data (the Portuguese Land Cover Map – COS) and the other including more generalized LUC data (the CORINE Land Cover – CLC). A set of fixed independent layers was considered as landslide predisposing factors (slope angle, slope aspect, slope curvature, slope-over-area ratio, soil, and lithology) while COS and CLC were used to find the differences in the landslide susceptibility zonation. A landslide inventory was used as a dependent layer, including 259 shallow landslides obtained from the photointerpretation of orthophotos from 2005, and further validated in three sample areas. The landslide susceptibility maps were assigned to the road network data and resulted in two landslide susceptibility road network maps. The models' performance was evaluated with prediction and success rate curves and the area under the curve (AUC). The landslide susceptibility results obtained in the two models present a high accuracy in terms of the AUC (>90 %), but the model with more detailed LUC data (COS) produces better results in the landslide susceptibility zonation on the road network with the highest landslide susceptibility.

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Section: Introductionmentioning

confidence: 99%

Effects of different land use and land cover data on the landslide susceptibility zonation of road networks

Meneses

Pereira

Reis

2019

Nat. Hazards Earth Syst. Sci.

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“…A graphical overview of GES architecture is shown in Figures 2 and 3 and its GUI, key features, tools, and windows are presented in Figure 4. The system is built in the Java-Python-C programming environments with input from the cartographic knowledge acquisition process, based on the International Cartographic Generalisation Survey (Kazemi andLim, 2007 andKazemi andForghani, 2015). The system consists of four main components: graphical interface, setting, algorithms, and outputs of spatial attribute data.…”

Section: System Architecture and Key Featuresmentioning

confidence: 99%

A Machine-Learning Approach to Generalisation of GIS Data

Forghani

Kazemi

Bruce

2021

IJG

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The automation of map generalisation in this study involves an expert system approach that consists of four main components including knowledge acquisition, an inference engine, knowledge representation and a user interface. The acquired knowledge was then utilised to build a knowledge-based solution: a ‘Generalisation Expert System’ (GES) developed in Java, Python and C programming environments for the delivery of generalised geographical features. Its capabilities are demonstrated in a case study through generalising several line and polyline databases over the study area in Canberra, Australia. The cartographic and GIS software communities will benefit from this study through access to a set of tools, guidelines and protocols that incorporate a standardised cartographic generalisation methodology. The results of the trials utilising GES were analysed: a series of generalisation routines were performed to assess the quality of simplification results for different spatial layers. Cartometric measures such as the total length and number of line or polyline segments were used as indices of generalisation to quantify generalisation performance for the target small scale. For example, there are 101,228 segments in 1:250,000 scale and 9,491 segments in 1:500,000 scale contours over the study area. This requires a reduction in the complexity and the density of elevation data. Changes in the representation of contour features at 1:250,000 and 1:500,000 scales as a result of generalisation were quantified. Outputs from map derivation have been analysed applying the Radical Law, this determines the retained number of objects for a given scale change and the number of objects of the original source map. Testing demonstrated that the implemented algorithms in GES are able to extract characteristic vertices on the original entity lines and polylines (e.g. for roads) while excluding non-characteristic lines and polylines to reduce irrelevant computation. This study has demonstrated reasonable improvements in Vertex Reduction, Classification and Merge, Enhanced Douglas-Peucker and Douglas-Peucker-Peschier algorithms. The test results show that GES generalises line features accurately while still maintaining their geometric relations. Existing generalisation software requires advanced technical skills from users; GES however, has a basic and user friendly Graphical User Interface (GUI) which is an advantage to users with basic technical skills and understanding of spatial data management. Changes to geographic parameters should be updated in multi-scale maps and spatial databases in near real-time. GES can be developed as a potential tool for generalising large-scale maps into smaller scales, and creating maps of different themes across a variety of scales. Test results have also demonstrated that the methodology developed improves the efficiency of line and polyline generalisation. This study aims to contribute to generalisation system design and the production of a clear framework for users. Experiments presented in this book can be applied to real world problems such as the generalisation of road networks and area features using GES. Future research should be directed towards developing web mapping platforms with generalisation functionality at varying scales.

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“…15 Due to the variation of the road network morphology (length versus width of the roads typologies), the selection of appropriate GI which integrates the analysis of ruptures of the roads caused by landslides requires a systematic assessment of more detailed properties of the landslide predisposing factors (Drobnjak et al, 2016;Imprialou and Quddus, 2017;Kazemi and Lim, 2005;Orongo, 2011) in order to obtain detailed landslide susceptibility results at the local scale (roads). 20 In this context, the main goal of this work is the assessment of land use and land cover GI properties influence on the landslide susceptibility zonation of road network.…”

Section: Introductionmentioning

confidence: 99%