Generalizing surficial geological maps for scale change: ArcGIS tools vs. cellular automata model

Smirnoff, Anna; Paradis, S J; Boivin, Renaud René

doi:10.1016/j.cageo.2007.10.013

Cited by 16 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper presented a methodology for geological map generalization using a constraint-based approach. The main objective of this paper was to demonstrate the usefulness of the constraint-based approach in map generalization when applied to geological maps, not to solve geological map generalization comprehensively (more feature classes are addressed in [23,24]). This was done against the background that the constraint-based approach has so far not been explicitly applied to the automated generalization of geological maps.…”

Section: Discussionmentioning

confidence: 99%

“…Another experiment on the effectiveness of ArcGIS was conducted by Smirnoff et al [23], where a cellular automata (CA) approach was developed specifically for geological map generalization using ArcGIS tools as a basis. When comparing their results with those of a process using the generalization tools directly available in ArcGIS, they concluded that the cell-based, or cellular, automata model had essential advantages for the automated generalization of geological maps.…”

Section: Related Workmentioning

confidence: 99%

“…The CA was then run over the rasterized map with a Moore's neighborhood of radius 3 and 2 iterations of final CA generation. Following the CA processing, the raster map was converted back to a vector map, yielding the result shown in Figure 17 In this section, we compare the results that can be obtained by the proposed methodology with those of an approach based on cellular automata (CA) [23,24], as implemented in the GeoScaler toolbox [47]. As mentioned in Section 2, this approach can probably be considered as the state of the art of geological map generalization available in practice, which is why we used it as a benchmark for comparison.…”

Section: Comparison With Cellular Automata Approachmentioning

confidence: 99%

See 2 more Smart Citations

Geological Map Generalization Driven by Size Constraints

Sayidov

Aliakbarian

Weibel

2020

IJGI

View full text Add to dashboard Cite

Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and thus a complete automated system for geological map generalization is not yet available. In particular, while in other areas of map generalization constraint-based techniques have become the prevailing approach in the past two decades, generalization methods for geological maps have rarely adopted this approach. This paper seeks to fill this gap by presenting a methodology for the automation of geological map generalization that builds on size constraints (i.e., constraints that deal with the minimum area and distance relations in individual or pairs of map features). The methodology starts by modeling relevant size constraints and then uses a workflow consisting of generalization operators that respond to violations of size constraints (elimination/selection, enlargement, aggregation, and displacement) as well as algorithms to implement these operators. We show that the automation of geological map generalization is possible using constraint-based modeling, leading to improved process control compared to current approaches. However, we also show the limitations of an approach that is solely based on size constraints and identify extensions for a more complete workflow.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison With Cellular Automata Approachmentioning

confidence: 99%

See 1 more Smart Citation

Geological Map Generalization Driven by Size Constraints

Sayidov

Aliakbarian

Weibel

2020

IJGI

View full text Add to dashboard Cite

show abstract

“…The meta data is not restricted to the simple header file of ARCGIS (Smirnoff et al, 2008), it contains only the number of rows, cols, the location, the code for nodata values and the grid size. Important data, such as the geographic projection used, the model to create the data, the modelers name, the creation time, etc., can easily be stored in HDF.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Spatial Analysis and Modeling Tool Version 2 (SAMT2), a spatial modeling tool kit written in Python

Wieland

Groth

Linde

et al. 2015

Ecological Informatics

View full text Add to dashboard Cite

“…The generalisation of the 1:200,000 scale map has been processed primarily using an expert based process. The dimensions of the Province and its complexity and variability in terms of geology, relief, drainage, surface processes and land use make it difficult to obtain an automatic landscape system and related geomorphic assessment (Mackaness et al, 2007;Smirnoff et al, 2008). The digitalized 1:25,000 scale geomorphological map underwent statistical analysis regarding the relationships between landforms, geomorphic processes, bedrock and land use.…”

Section: Methodsmentioning

confidence: 99%

Geomorphological map and land units at 1:200,000 scale of the Siena Province (Southern Tuscany, Italy)

2011

View full text Add to dashboard Cite

Small scale geomorphological maps and land unit classification of large areas are not common due to the high degree of generalisation required to represent landforms originally mapped at larger scale (i.e. 1:10,000). Here we present a 1:200,000 scale geomorphological map of the Siena Province (Tuscany, Italy) where the landforms related to different processes have been synthesised into a map representing the main landscape units. The process of generalisation follows the overlapping of the main processes and landforms grouped in 6 main categories: geology, land use, slope, structural/selective erosion, gravity and running water. The final map indicates the presence of seven main homogeneous landscapes characterised by typical associations of landforms and processes. The reference scale allows the analysis of the entire Siena Province and may be used for landscape evaluation, including natural hazards and related susceptibility.

show abstract

Generalizing surficial geological maps for scale change: ArcGIS tools vs. cellular automata model

Cited by 16 publications

References 14 publications

Geological Map Generalization Driven by Size Constraints

Geological Map Generalization Driven by Size Constraints

Spatial Analysis and Modeling Tool Version 2 (SAMT2), a spatial modeling tool kit written in Python

Geomorphological map and land units at 1:200,000 scale of the Siena Province (Southern Tuscany, Italy)

Contact Info

Product

Resources

About