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ISPRS Int. J. Geo-Inf., Volume 7, Issue 4 (April 2018)

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Open AccessArticle Use of DEMs Derived from TLS and HRSI Data for Landslide Feature Recognition
ISPRS Int. J. Geo-Inf. 2018, 7(4), 160; https://doi.org/10.3390/ijgi7040160
Received: 5 February 2018 / Revised: 10 April 2018 / Accepted: 19 April 2018 / Published: 23 April 2018
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Abstract
This paper addresses the problems arising from the use of data acquired with two different remote sensing techniques—high-resolution satellite imagery (HRSI) and terrestrial laser scanning (TLS)—for the extraction of digital elevation models (DEMs) used in the geomorphological analysis and recognition of landslides, taking
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This paper addresses the problems arising from the use of data acquired with two different remote sensing techniques—high-resolution satellite imagery (HRSI) and terrestrial laser scanning (TLS)—for the extraction of digital elevation models (DEMs) used in the geomorphological analysis and recognition of landslides, taking into account the uncertainties associated with DEM production. In order to obtain a georeferenced and edited point cloud, the two data sets require quite different processes, which are more complex for satellite images than for TLS data. The differences between the two processes are highlighted. The point clouds are interpolated on a DEM with a 1 m grid size using kriging. Starting from these DEMs, a number of contour, slope, and aspect maps are extracted, together with their associated uncertainty maps. Comparative analysis of selected landslide features drawn from the two data sources allows recognition and classification of hierarchical and multiscale landslide components. Taking into account the uncertainty related to the map enables areas to be located for which one data source was able to give more reliable results than another. Our case study is located in Southern Italy, in an area known for active landslides. Full article
(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)
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Open AccessArticle DASSCAN: A Density and Adjacency Expansion-Based Spatial Structural Community Detection Algorithm for Networks
ISPRS Int. J. Geo-Inf. 2018, 7(4), 159; https://doi.org/10.3390/ijgi7040159
Received: 12 March 2018 / Revised: 15 April 2018 / Accepted: 19 April 2018 / Published: 21 April 2018
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Abstract
Existing spatial community detection algorithms are usually modularity based. Motivated by different applications, these algorithms build appropriate spatial null models to describe spatial effects on the connection of nodes. Then, by choosing certain modularity maximizing strategies, they try to find interesting community structures
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Existing spatial community detection algorithms are usually modularity based. Motivated by different applications, these algorithms build appropriate spatial null models to describe spatial effects on the connection of nodes. Then, by choosing certain modularity maximizing strategies, they try to find interesting community structures hidden behind the null models. In this paper, a novel structural similarity-based spatial network community is defined, which is based on the shared neighbors of nodes. In addition, there are two other special node roles defined: the spatial hub and outlier. Then, a density and adjacency expansion-based spatial structural community detection algorithm for networks (DASSCAN) is proposed for mining these communities, hubs and outliers. DASSCAN uses structural similarity to measure the relationship between nodes, and then, structurally similar and spatially adjacent nodes are merged into communities using a density-based clustering method and spatial adjacency expansion strategy. Comparative experiments on two kinds of Chinese train line networks clarified the accuracy and efficiency of DASSCAN in finding the spatial structural communities, spatial hubs and outliers. The communities found can be used to uncover more interesting spatial structural patterns, and the hubs and outliers are more accurate and have more valuable meanings. Full article
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Open AccessArticle Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data
ISPRS Int. J. Geo-Inf. 2018, 7(4), 158; https://doi.org/10.3390/ijgi7040158
Received: 26 February 2018 / Revised: 29 March 2018 / Accepted: 19 April 2018 / Published: 21 April 2018
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Abstract
Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to
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Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field. Full article
(This article belongs to the Special Issue Web and Mobile GIS)
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Open AccessArticle Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics
ISPRS Int. J. Geo-Inf. 2018, 7(4), 157; https://doi.org/10.3390/ijgi7040157
Received: 16 February 2018 / Revised: 11 April 2018 / Accepted: 14 April 2018 / Published: 21 April 2018
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Abstract
This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover
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This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover mapping and updating from remotely sensed and national GIS-based data followed by area estimation. Map production follows a series of steps, namely data collection, change detection, supervised image classification, rule-based image classification, and map updating/generalization. Land cover area estimation is based on mapping but compensated for mapping error as estimated through thematic accuracy assessment. This general structure was applied to continental Portugal, successively updating a map of 2010 for the following years until 2015. The estimated land cover change was smaller than expected but the proposed framework was proved as a potential for statistics production at the national and European levels. Contextual and structural methodological challenges and bottlenecks are discussed, especially regarding mapping, accuracy assessment, and area estimation. Full article
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Open AccessFeature PaperArticle New Geospatial Approaches for Efficiently Mapping Forest Biomass Logistics at High Resolution over Large Areas
ISPRS Int. J. Geo-Inf. 2018, 7(4), 156; https://doi.org/10.3390/ijgi7040156
Received: 13 February 2018 / Revised: 13 April 2018 / Accepted: 15 April 2018 / Published: 20 April 2018
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Abstract
Adequate biomass feedstock supply is an important factor in evaluating the financial feasibility of alternative site locations for bioenergy facilities and for maintaining profitability once a facility is built. We used newly developed spatial analysis and logistics software to model the variables influencing
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Adequate biomass feedstock supply is an important factor in evaluating the financial feasibility of alternative site locations for bioenergy facilities and for maintaining profitability once a facility is built. We used newly developed spatial analysis and logistics software to model the variables influencing feedstock supply and to estimate and map two components of the supply chain for a bioenergy facility: (1) the total biomass stocks available within an economically efficient transportation distance; (2) the cost of logistics to move the required stocks from the forest to the facility. Both biomass stocks and flows have important spatiotemporal dynamics that affect procurement costs and project viability. Though seemingly straightforward, these two components can be difficult to quantify and map accurately in a useful and spatially explicit manner. For an 8 million hectare study area, we used raster-based methods and tools to quantify and visualize these supply metrics at 10 m2 spatial resolution. The methodology and software leverage a novel raster-based least-cost path modeling algorithm that quantifies off-road and on-road transportation and other logistics costs. The results of the case study highlight the efficiency, flexibility, fine resolution, and spatial complexity of model outputs developed for facility siting and procurement planning. Full article
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Open AccessArticle An Autonomous Ultra-Wide Band-Based Attitude and Position Determination Technique for Indoor Mobile Laser Scanning
ISPRS Int. J. Geo-Inf. 2018, 7(4), 155; https://doi.org/10.3390/ijgi7040155
Received: 2 March 2018 / Revised: 11 April 2018 / Accepted: 14 April 2018 / Published: 20 April 2018
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Abstract
Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning
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Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large, or it has many turns and narrow corridors. This paper proposes using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey. Full article
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Open AccessArticle Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain
ISPRS Int. J. Geo-Inf. 2018, 7(4), 154; https://doi.org/10.3390/ijgi7040154
Received: 20 March 2018 / Revised: 10 April 2018 / Accepted: 15 April 2018 / Published: 19 April 2018
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Abstract
This study explored the past and present land-use/land-cover (LULC) changes and urban expansion pattern for the cities of the Kathmandu valley and their surroundings using Landsat satellite images from 1988 to 2016. For a better analysis, LULC change information was grouped into seven
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This study explored the past and present land-use/land-cover (LULC) changes and urban expansion pattern for the cities of the Kathmandu valley and their surroundings using Landsat satellite images from 1988 to 2016. For a better analysis, LULC change information was grouped into seven time-periods (1988–1992, 1992–1996, 1996–2000, 2000–2004, 2004–2008, 2008–2013, and 2013–2016). The classification was conducted using the support vector machines (SVM) technique. A hybrid simulation model that combined the Markov-Chain and Cellular Automata (MC-CA) was used to predict the future urban sprawl existing by 2024 and 2032. Research analysis explored the significant expansion in urban cover which was manifested at the cost of cultivated land. The urban area totaled 40.53 km2 in 1988, which increased to 144.35 km2 in 2016 with an average annual growth rate of 9.15%, an overall increase of 346.85%. Cultivated land was the most affected land-use from this expansion. A total of 91% to 98% of the expanded urban area was sourced from cultivated land alone. Future urban sprawl is likely to continue, which will be outweighed by the loss of cultivated land as in the previous decades. The urban area will be expanded to 200 km2 and 238 km2 and cultivated land will decline to 587 km2 and 555 km2 by 2024 and 2032. Currently, urban expansion is occurring towards the west and south directions; however, future urban growth is expected to rise in the southern and eastern part of the study area, dismantling the equilibrium of environmental and anthropogenic avenues. Since the study area is a cultural landscape and UNESCO heritage site, balance must be found not only in developing a city, but also in preserving the natural environment and maintaining cultural artifacts. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessArticle The Implementation of Spatial Planning Objects in a 3D Cadastral Model
ISPRS Int. J. Geo-Inf. 2018, 7(4), 153; https://doi.org/10.3390/ijgi7040153
Received: 8 March 2018 / Revised: 28 March 2018 / Accepted: 15 April 2018 / Published: 18 April 2018
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Abstract
The paper concerns spatial planning in Poland and its connection with the cadastre. The Polish spatial planning system defines the set of colours, lines, hatches, etc. destined for the preparations of spatial plans, though this has so far not been followed by a
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The paper concerns spatial planning in Poland and its connection with the cadastre. The Polish spatial planning system defines the set of colours, lines, hatches, etc. destined for the preparations of spatial plans, though this has so far not been followed by a spatial planning model or application schema. The aim of this paper is to create a preliminary concept of the unified modelling language (UML) schema of database integrating 3D cadastre and 3D spatial planning. The authors initially define five unified modelling language classes representing spatial planning objects (four representing spatial objects and one a dictionary list). As spatial planning and cadastres are very strongly connected, these classes are implemented into a cadastral model that had been earlier enriched with 3D classes. The final results of this research are UML diagrams based on the Polish cadastral model as defined earlier in legal regulations. They comprise original cadastral model classes, 3D cadastral objects added in earlier research work, classes representing spatial planning objects and the relationships among them. Such a solution better connects cadastre and spatial planning on a structural level and introduces 3D elements into spatial planning which has basically been done in two dimensions. Full article
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Open AccessArticle Validity of VR Technology on the Smartphone for the Study of Wind Park Soundscapes
ISPRS Int. J. Geo-Inf. 2018, 7(4), 152; https://doi.org/10.3390/ijgi7040152
Received: 1 March 2018 / Revised: 7 April 2018 / Accepted: 15 April 2018 / Published: 18 April 2018
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Abstract
The virtual reality of the landscape environment supplies a high level of realism of the real environment, and may improve the public awareness and acceptance of wind park projects. The soundscape around wind parks could have a strong influence on the acceptance and
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The virtual reality of the landscape environment supplies a high level of realism of the real environment, and may improve the public awareness and acceptance of wind park projects. The soundscape around wind parks could have a strong influence on the acceptance and annoyance of wind parks. To explore this VR technology on realism and subjective responses toward different soundscapes of ambient wind parks, three different types of virtual reality on the smartphone tests were performed: aural only, visual only, and aural–visual combined. In total, 21 aural and visual combinations were presented to 40 participants. The aural and visual information used were of near wind park settings and rural spaces. Perceived annoyance levels and realism of the wind park environment were measured. Results indicated that most simulations were rated with relatively strong realism. Perceived realism was strongly correlated with light, color, and vegetation of the simulation. Most wind park landscapes were enthusiastically accepted by the participants. The addition of aural information was found to have a strong impact on whether the participant was annoyed. Furthermore, evaluation of the soundscape on a multidimensional scale revealed the key components influencing the individual’s annoyance by wind parks were the factors of “calmness/relaxation” and “naturality/pleasantness”. “Diversity” of the soundscape might correlate with perceived realism. Finally, the dynamic aural–visual stimuli using virtual reality technology could improve the environmental assessment of the wind park landscapes, and thus, provide a more comprehensible scientific decision than conventional tools. In addition, this study could improve the participatory planning process for more acceptable wind park landscapes. Full article
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Open AccessArticle Fusing Georeferenced and Stereoscopic Image Data for 3D Building Façade Reconstruction
ISPRS Int. J. Geo-Inf. 2018, 7(4), 151; https://doi.org/10.3390/ijgi7040151
Received: 8 February 2018 / Revised: 30 March 2018 / Accepted: 5 April 2018 / Published: 17 April 2018
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Abstract
3D building façade reconstruction has become a very popular topic in various applications related to restoration and preservation of architectural structures as well as urban planning. This paper deals with the reconstruction of realistic 3D models of buildings façades, in the urban environment
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3D building façade reconstruction has become a very popular topic in various applications related to restoration and preservation of architectural structures as well as urban planning. This paper deals with the reconstruction of realistic 3D models of buildings façades, in the urban environment for cultural heritage. We present an approach that enables the relation of stereoscopic images with tacheometry data. The proposed multimodal fusing scheme results in an accurate 3D realistic façade reconstruction and provides a fast and low cost solution. In the first stage of the proposed approach a 2D skeleton of the building is extracted from the viewed scene using Active Contour and Hough line extraction. The next stage of our method utilizes depth information, extracted from a stereoscopic layout, to infer the structural details of inner façade structures, such as windows or doors. In the final stage, the structural information extracted from the image data is integrated with georeferenced point datasets. The final output of our method is a georeferenced 3D model of the structure’s façade, which can be further refined with the use of image-driven texture information. Full article
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Open AccessArticle Spatial-Temporal Event Detection from Geo-Tagged Tweets
ISPRS Int. J. Geo-Inf. 2018, 7(4), 150; https://doi.org/10.3390/ijgi7040150
Received: 21 February 2018 / Revised: 7 April 2018 / Accepted: 9 April 2018 / Published: 15 April 2018
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Abstract
As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on
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As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA) algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events. Full article
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Open AccessArticle Verification of a GNSS Time Series Discontinuity Detection Approach in Support of the Estimation of Vertical Crustal Movements
ISPRS Int. J. Geo-Inf. 2018, 7(4), 149; https://doi.org/10.3390/ijgi7040149
Received: 8 February 2018 / Revised: 8 April 2018 / Accepted: 9 April 2018 / Published: 13 April 2018
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Abstract
Vertical crustal movements can be calculated on the basis of Global Navigation Satellite Systems (GNSS) permanent stations positioning results (the absolute motion) as well as on vectors between the stations (the relative motion). The time series, which are created in both cases, include,
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Vertical crustal movements can be calculated on the basis of Global Navigation Satellite Systems (GNSS) permanent stations positioning results (the absolute motion) as well as on vectors between the stations (the relative motion). The time series, which are created in both cases, include, apart from the information about height, measurement noise, and they are burdened with the influence of factors that are sometimes difficult to identify. These factors make momentary or long-term changes in height. The times of sudden changes in height (jumps) can be difficult to identify and estimate. In order to calculate the velocity of vertical movements, each of the jumps should be identified. It means that both the epoch of each jump and its value must be estimated. The authors of this article developed an algorithm that supports the process of creating the models of vertical crustal movements from GNSS data. The algorithm determines the epoch of a jump and estimates the velocity of vertical movements. The aim of the article is to verify the algorithm on the basis of height changes in adjacent stations of polish national CORS network ASG-EUPOS and to set proper algorithm parameters. The results received on the basis of the algorithm were evaluated and verified using four possible methods: visual evaluation, testing the algorithm using adjacent input parameter values, information in .log files and analysis of the loop misclosure. The results indicate that the algorithm functions properly and is useful in the creation of vertical crustal movement models from GNSS data. Full article
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Open AccessFeature PaperArticle Map Archive Mining: Visual-Analytical Approaches to Explore Large Historical Map Collections
ISPRS Int. J. Geo-Inf. 2018, 7(4), 148; https://doi.org/10.3390/ijgi7040148
Received: 1 March 2018 / Revised: 5 April 2018 / Accepted: 5 April 2018 / Published: 13 April 2018
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Abstract
Historical maps are unique sources of retrospective geographical information. Recently, several map archives containing map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The geographical information contained in such data archives makes it possible
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Historical maps are unique sources of retrospective geographical information. Recently, several map archives containing map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The geographical information contained in such data archives makes it possible to extend geospatial analysis retrospectively beyond the era of digital cartography. However, given the large data volumes of such archives (e.g., more than 200,000 map sheets in the United States Geological Survey topographic map archive) and the low graphical quality of older, manually-produced map sheets, the process to extract geographical information from these map archives needs to be automated to the highest degree possible. To understand the potential challenges (e.g., salient map characteristics and data quality variations) in automating large-scale information extraction tasks for map archives, it is useful to efficiently assess spatio-temporal coverage, approximate map content, and spatial accuracy of georeferenced map sheets at different map scales. Such preliminary analytical steps are often neglected or ignored in the map processing literature but represent critical phases that lay the foundation for any subsequent computational processes including recognition. Exemplified for the United States Geological Survey topographic map and the Sanborn fire insurance map archives, we demonstrate how such preliminary analyses can be systematically conducted using traditional analytical and cartographic techniques, as well as visual-analytical data mining tools originating from machine learning and data science. Full article
(This article belongs to the Special Issue Historic Settlement and Landscape Analysis)
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Open AccessEditorial Foreword to the Special Issue on Machine Learning for Geospatial Data Analysis
ISPRS Int. J. Geo-Inf. 2018, 7(4), 147; https://doi.org/10.3390/ijgi7040147
Received: 9 April 2018 / Revised: 9 April 2018 / Accepted: 10 April 2018 / Published: 13 April 2018
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Abstract
Advances in machine learning research are pushing the limits of geographical information sciences (GIScience) by offering accurate procedures to analyze small-to-big GeoData. This Special Issue groups together six original contributions in the field of GeoData-driven GIScience that focus mainly on three different areas:
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Advances in machine learning research are pushing the limits of geographical information sciences (GIScience) by offering accurate procedures to analyze small-to-big GeoData. This Special Issue groups together six original contributions in the field of GeoData-driven GIScience that focus mainly on three different areas: extraction of semantic information from satellite imagery, image recommendation, and map generalization. Different technical approaches are chosen for each sub-topic, from deep learning to latent topic models. Full article
(This article belongs to the Special Issue Machine Learning for Geospatial Data Analysis)
Open AccessArticle A Method of Mining Association Rules for Geographical Points of Interest
ISPRS Int. J. Geo-Inf. 2018, 7(4), 146; https://doi.org/10.3390/ijgi7040146
Received: 1 March 2018 / Revised: 4 April 2018 / Accepted: 6 April 2018 / Published: 10 April 2018
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Abstract
Association rule (AR) mining represents a challenge in the field of data mining. Mining ARs using traditional algorithms generates a large number of candidate rules, and even if we use binding measures such as support, reliability, and lift, there are still several rules
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Association rule (AR) mining represents a challenge in the field of data mining. Mining ARs using traditional algorithms generates a large number of candidate rules, and even if we use binding measures such as support, reliability, and lift, there are still several rules to keep, and domain experts are needed to extract the rules of interest from the remaining rules. The focus of this paper is on whether we can directly provide rule rankings and calculate the proportional relationship between the items in the rules. To address these two questions, this paper proposes a modified FP-Growth algorithm called FP-GCID (novel FP-Growth algorithm based on Cluster IDs) to generate ARs; in addition, a new method called Mean-Product of Probabilities (MPP) is proposed to rank rules and compute the proportion of items for one rule. The experiment is divided into three phases: the DBSCAN (Density-Based Scanning Algorithm with Noise) algorithm is used to cluster the geographic interest points and map the obtained clusters into corresponding transaction data; FP-GCID is used to generate ARs, which contain cluster information; and MPP is used to choose the best rule based on the rankings. Finally, a visualization of the rules is used to validate whether the two previously stated requirements were fulfilled. Full article
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