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Cobb, Maria A.; Chung, Miyi; Foley, Harold; Petry, Frederick E.; Shaw, Kevin; Miller, H. Vincent

doi:10.1023/a:1009788905049

Cited by 132 publications

(15 citation statements)

References 5 publications

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“…In addition to the relation measurements, it can also be influenced by the reliability of the input sources and existing knowledge (Cholvy 2007). The probability of a relation can be expressed in terms of hard and soft confidence measures (Cobb et al 1998, Bloch et al 2001, Waltz and Waltz 2008). (6) Resolving steps are determined by the previous results and the specific application target.…”

Section: Spatial Data Fusion: Classification and Decompositionmentioning

confidence: 99%

Spatial data fusion in Spatial Data Infrastructures using Linked Data

Wiemann

Bernard

2015

International Journal of Geographical Information Science

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Section: Spatial Data Fusion: Classification and Decompositionmentioning

confidence: 99%

Spatial data fusion in Spatial Data Infrastructures using Linked Data

Wiemann

Bernard

2015

International Journal of Geographical Information Science

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“…In general, these methods can be categorized in vector to vector (closest to gaze map matching), vector to raster, and raster to raster data conflation [5]. Several techniques for vector to vector conflation exist [6,30], some specialized for the conflation of two road networks [23].…”

Section: Conflationmentioning

confidence: 99%

Gaze map matching

Kiefer

Giannopoulos

2012

Proceedings of the 20th International Conference on Advances in Geographic Information Systems

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This paper introduces gaze map matching as the problem of algorithmically interpreting eye tracking data with respect to geographic vector features, such as a road network shown on a map. This differs from previous eye tracking studies which have not taken into account the underlying vector data of the cartographic map. The paper explores the challenges of gaze map matching and relates it to the (vehicle) map matching problem. We propose a gaze map matching algorithm based on a Hidden Markov Model, and compare its performance with two purely geometric algorithms. Two eye tracking data sets recorded during the visual inspection of 14 road network maps of varying realism and complexity are used for this evaluation.

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“…Its main purpose is to produce better maps from sets of two different maps representing the same entities [1]. Cobb et al [5] observed the necessities of conflation for three main reasons: (1) updating from one to another, (2) improving positional or attribute accuracy, and (3) containing missing information. Besides, the volume of spatial data is rapidly increasing by means of several sources, including governmental and private agencies, and volunteers.…”

Section: Introductionmentioning

confidence: 99%

“…However, some studies have been based on points (buildings, POI, junctions, real-time GPS signals, etc.) [2,4,5], and polygons (buildings, triangles, blocks, parcels, etc.) [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A New, Score-Based Multi-Stage Matching Approach for Road Network Conflation in Different Road Patterns

Hacar

Gökgöz

2019

IJGI

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Road-matching processes establish links between multi-sourced road lines representing the same entities in the real world. Several road-matching methods have been developed in the last three decades. The main issue related to this process is selecting the most appropriate method. This selection depends on the data and requires a pre-process (i.e., accuracy assessment). This paper presents a new matching method for roads composed of different patterns. The proposed method matches road lines incrementally (i.e., from the most similar matching to the least similar). In the experimental testing, three road networks in Istanbul, Turkey, which are composed of tree, cellular, and hybrid patterns, provided by the municipality (authority), OpenStreetMap (volunteered), TomTom (private), and Basarsoft (private) were used. The similarity scores were determined using Hausdorff distance, orientation, sinuosity, mean perpendicular distance, mean length of triangle edges, and modified degree of connectivity. While the first four stages determined certain matches with regards to the scores, the last stage determined them with a criterion for overlapping areas among the buffers of the candidates. The results were evaluated with manual matching. According to the precision, recall, and F-value, the proposed method gives satisfactory results on different types of road patterns.

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Cited by 132 publications

References 5 publications

Spatial data fusion in Spatial Data Infrastructures using Linked Data

Spatial data fusion in Spatial Data Infrastructures using Linked Data

Gaze map matching

A New, Score-Based Multi-Stage Matching Approach for Road Network Conflation in Different Road Patterns

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