Finding landmarks within settled areas using hierarchical density-based clustering and meta-data from publicly available images

Pla-Sacristán, Eduardo; González-Díaz, Iván; Martínez-Cortés, Tomás; Díaz-de-María, Fernando

doi:10.1016/j.eswa.2019.01.046

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…Yang et al [51] introduced a robust noise-resistant approach based on Laplacian for POI identification using geo-and textual-tagged social photos data. Pla-Sacristán et al [52] proposed two density-based clustering algorithms, namely K-DBSCAN and V-DBSCAN that have a direct applicability on the task of automatic POI identification.…”

Section: A Spatial Extent Estimation Of Geographic Entities and Poi I...mentioning

confidence: 99%

Estimating Bounding Box for Point of Interest Using Social Media Geo-Tagged Photos

Bui

2023

IEEE Access

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The accuracy and completeness of information in geographical databases are very important for many location-based applications and services. However, the incompleteness in geographical databases is currently an issue. One consequence of this is that the geographic bounding boxes of many points of interests (POIs) have not been known. This paper studies the problem of estimating geographic bounding boxes for POIs using geo-tagged photos contributed by public users on social media. We present a novel approach using relevant geo-tagged photos of POIs to estimate geographic bounding boxes for the POIs. In the proposed method, we extend to apply survival analysis with random distance variable for our estimation. We demonstrate the superiority and effectiveness of our proposed approach over competing methods.INDEX TERMS Geographic bounding box, POI, survival analysis, geo-tagged photos, Flickr.

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Section: A Spatial Extent Estimation Of Geographic Entities and Poi I...mentioning

confidence: 99%

Estimating Bounding Box for Point of Interest Using Social Media Geo-Tagged Photos

Bui

2023

IEEE Access

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“…For compatibility with the USotW database, we consider a similar collection of fou Density-based clustering methods have also been used to identify tourist landmarks. For example, Pla-Sacristán et al [23] proposed the combination of a method they called "K-DBSCAN" to first identify places of interest, and another method they called "V-DBSCAN" to perform clustering of points into those places of interest. Their method used global positioning system (GPS) metadata of pictures (mostly taken by tourists) uploaded to a public website, but characteristic soundscapes might not necessarily be similar in nature to tourist landmarks depending on the perceptual attributes under study.…”

Section: Study Area and Contextmentioning

confidence: 99%

Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-Means Clustering

et al. 2022

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The ecological validity of soundscape studies usually rests on the choice of soundscapes that are representative of the perceptual space under investigation. For example, a soundscape pleasantness study might investigate locations with soundscapes ranging from “pleasant” to “annoying”. The choice of soundscapes is typically researcher led, but a participant-led process can reduce selection bias and improve result reliability. Hence, we propose a robust participant-led method to pinpoint characteristic soundscapes possessing arbitrary perceptual attributes. We validate our method by identifying Singaporean soundscapes spanning the perceptual quadrants generated from the “Pleasantness” and “Eventfulness” axes of the ISO 12913-2 circumplex model of soundscape perception, as perceived by local experts. From memory and experience, 67 participants first selected locations corresponding to each perceptual quadrant in each major planning region of Singapore. We then performed weighted k-means clustering on the selected locations, with weights for each location derived from previous frequencies and durations spent in each location by each participant. Weights hence acted as proxies for participant confidence. In total, 62 locations were thereby identified as suitable locations with characteristic soundscapes for further research utilizing the ISO 12913-2 perceptual quadrants. Audio–visual recordings and acoustic characterization of the soundscapes will be made in a future study.

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“…Hu et al [30] presented an united framework for abstracting and comprehending unban attractions using the DBSCAN clustering algorithm on Flickr photo data from different cities and discussed the spatiotemporal dynamics and some insights concluded from attractions. Pla-Sacristán et al [31] used the K + V-DBSCAN clustering algorithm based on Gaussian kernel density to identify most of the major tourist attractions in a certain area properly.…”

Section: Popular Attraction Recommendationmentioning

confidence: 99%

Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

Sun

Chen

Jin

et al. 2022

Journal of Advanced Transportation

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With the swift development of tourism all around the world, it has become vital to improve the recommendation of useful travel information to tourists to assure their convenience and satisfaction. In this paper, we propose a novel multi-objective optimal travel route recommendation framework, which collects tourists’ travel trajectories from their mobile phone signaling data. Then, the proposed framework preprocesses the mobile signaling data to transform raw trajectories into tourists’ travel sequences. Subsequently, the framework finds the popular attractions and frequent travel routes from the travel pattern sequences by using a frequent pattern mining method. Finally, an improved ant colony optimization (ACO) algorithm with a novel extensible heuristic factor approach is adopted to search the multi-objective optimal travel routes according to the popularity of attractions and travel time of tourists. The experimental results indicate that the proposed framework is efficient in recommending multi-objective optimal travel routes considering tourists’ travel time and attractions’ popularity while ensuring that the recommended travel route is suitable.

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Finding landmarks within settled areas using hierarchical density-based clustering and meta-data from publicly available images

Abstract: Fernando. (2019). Finding landmarks within settled areas using hierarchical density-based clustering and meta-data from publicly available images.

Cited by 12 publications

References 20 publications

Estimating Bounding Box for Point of Interest Using Social Media Geo-Tagged Photos

Estimating Bounding Box for Point of Interest Using Social Media Geo-Tagged Photos

Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-Means Clustering

Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

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