Open AccessArticle
Guided Image Filtering-Based Pan-Sharpening Method: A Case Study of GaoFen-2 Imagery
ISPRS Int. J. Geo-Inf. 2017, 6(12), 404; doi:10.3390/ijgi6120404 -
Abstract
GaoFen-2 (GF-2) is a civilian optical satellite self-developed by China equipped with both multispectral and panchromatic sensors, and is the first satellite in China with a resolution below 1 m. Because the pan-sharpening methods on GF-2 imagery have not been a focus of
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GaoFen-2 (GF-2) is a civilian optical satellite self-developed by China equipped with both multispectral and panchromatic sensors, and is the first satellite in China with a resolution below 1 m. Because the pan-sharpening methods on GF-2 imagery have not been a focus of previous works, we propose a novel pan-sharpening method based on guided image filtering and compare the performance to state-of-the-art methods on GF-2 images. Guided image filtering was introduced to decompose and transfer the details and structures from the original panchromatic and multispectral bands. Thereafter, an adaptive model that considers the local spectral relationship was designed to properly inject spatial information back into the original spectral bands. Four pairs of GF-2 images acquired from urban, water body, cropland, and forest areas were selected for the experiments. Both quantitative and visual inspections were used for the assessment. The experimental results demonstrated that for GF-2 imagery acquired over different scenes, the proposed approach consistently achieves high spectral fidelity and enhances spatial details, thereby benefitting the potential classification procedures. Full article
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Open AccessArticle
Towards Sustainable Urban Planning Through Transit-Oriented Development (A Case Study: Tehran)
ISPRS Int. J. Geo-Inf. 2017, 6(12), 402; doi:10.3390/ijgi6120402 -
Abstract
Sustainable development is regarded as a pivotal factor for smart urban planning. Transit-Oriented Development (TOD) is a well-known land use transportation integration (LUTI) planning method, which can fulfill sustainable development objectives. In this study, a new spatial index is developed to measure TOD
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Sustainable development is regarded as a pivotal factor for smart urban planning. Transit-Oriented Development (TOD) is a well-known land use transportation integration (LUTI) planning method, which can fulfill sustainable development objectives. In this study, a new spatial index is developed to measure TOD levels in neighborhoods of Tehran, the capital of Iran. To develop the TOD index, several criteria and indicators are first computed using spatial analyses, before being aggregated using a fuzzy-analytic hierarchy process (fuzzy-AHP). The fuzzy-AHP method generates three types of factor maps: that are optimistic, pessimistic, and moderate. This process evaluates the sensitivity of the TOD index by determining the indicators’ weights from various views, or perspectives. The results of this sensitivity analysis show the robustness of results from various views. Furthermore, in order to assess the efficiency of the proposed method, the moderate TOD-level map is compared with both the level of public transit services and trip attraction in neighborhoods. This comparison shows that the TOD map has an accuracy of 77 percent in urban modeling, which verifies the efficiency of the proposed method for measuring TOD. Full article
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Open AccessArticle
Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads
ISPRS Int. J. Geo-Inf. 2017, 6(12), 403; doi:10.3390/ijgi6120403 -
Abstract
Many studies have used Global Navigation Satellite System (GNSS) traces to successfully extract segments of road networks because such data can be rapidly updated at a low cost. However, most studies have not focused on extracting intersections, which are indispensable parts of road
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Many studies have used Global Navigation Satellite System (GNSS) traces to successfully extract segments of road networks because such data can be rapidly updated at a low cost. However, most studies have not focused on extracting intersections, which are indispensable parts of road networks in terms of connectivity. However, extracted intersections often present unsatisfactory precision and misleading connectivity. This study proposes a novel method for extracting road intersections from Global Position System (GPS) trace points and for capturing intersections with better accuracy. The key to improving the geometric accuracy of intersections is to identify the dominant orientations of road segments around intersections, merge similar orientations and maintain independent conflicting orientations. Extracting intersections by aligning the dominant orientations can largely reduce location offsets and road distortions. Experiments are performed to demonstrate the increased accuracy and connectivity of extracted road intersections by the proposed method. Full article
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Open AccessArticle
A Remote Sensing Approach to Environmental Monitoring in a Reclaimed Mine Area
ISPRS Int. J. Geo-Inf. 2017, 6(12), 401; doi:10.3390/ijgi6120401 -
Abstract
Mining for resources extraction may lead to geological and associated environmental changes due to ground movements, collision with mining cavities, and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates
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Mining for resources extraction may lead to geological and associated environmental changes due to ground movements, collision with mining cavities, and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates and an increase in ground water tables, which may cause surface area inundation. Consequently, a reclaimed mine area may experience surface area collapse, i.e., subsidence, and degradation of vegetation productivity. Thus, monitoring short-term landscape dynamics in a reclaimed mine area may provide important information on the long-term geological and environmental impacts of mining activities. We studied landscape dynamics in Kirchheller Heide, Germany, which experienced extensive soil movement due to longwall mining without stowing, using Landsat imageries between 2013 and 2016. A Random Forest image classification technique was applied to analyze land-use and landcover dynamics, and the growth of wetland areas was assessed using a Spectral Mixture Analysis (SMA). We also analyzed the changes in vegetation productivity using a Normalized Difference Vegetation Index (NDVI). We observed a 19.9% growth of wetland area within four years, with 87.2% growth in the coverage of two major waterbodies in the reclaimed mine area. NDVI values indicate that the productivity of 66.5% of vegetation of the Kirchheller Heide was degraded due to changes in ground water tables and surface flooding. Our results inform environmental management and mining reclamation authorities about the subsidence spots and priority mitigation areas from land surface and vegetation degradation in Kirchheller Heide. Full article
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Open AccessArticle
An Automatic Road Network Construction Method Using Massive GPS Trajectory Data
ISPRS Int. J. Geo-Inf. 2017, 6(12), 400; doi:10.3390/ijgi6120400 -
Abstract
Automatically acquiring comprehensive, accurate, and real-time mapping information and translating this information into digital maps are challenging problems. Traditional methods are time consuming and costly because they require expensive field surveying and labor-intensive post-processing. Recently, the ubiquitous use of positioning technology in vehicles
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Automatically acquiring comprehensive, accurate, and real-time mapping information and translating this information into digital maps are challenging problems. Traditional methods are time consuming and costly because they require expensive field surveying and labor-intensive post-processing. Recently, the ubiquitous use of positioning technology in vehicles and other devices has produced massive amounts of trajectory data, which provide new opportunities for digital map production and updating. This paper presents an automatic method for producing road networks from raw vehicle global positioning system (GPS) trajectory data. First, raw GPS positioning data are processed to remove noise using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules. Then, a new road network construction algorithm is used to incrementally merge trajectories into a directed graph representing a digital map. Furthermore, the average road traffic volume and speed are calculated and assigned to corresponding road segments. To evaluate the performance of the method, an experiment was conducted using 5.76 million trajectory data points from 200 taxis. The result was qualitatively compared with OpenStreetMap and quantitatively compared with two existing methods based on the F-score. The findings show that our method can automatically generate a road network representing a digital map. Full article
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Open AccessArticle
Exploratory Data Analysis of Synthetic Aperture Radar (SAR) Measurements to Distinguish the Sea Surface Expressions of Naturally-Occurring Oil Seeps from Human-Related Oil Spills in Campeche Bay (Gulf of Mexico)
ISPRS Int. J. Geo-Inf. 2017, 6(12), 379; doi:10.3390/ijgi6120379 -
Abstract
An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills—the use of satellite sensors for this task is poorly documented in
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An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills—the use of satellite sensors for this task is poorly documented in scientific literature. A long-term RADARSAT dataset (2008–2012) is exploited to investigate oil slicks in Campeche Bay (Gulf of Mexico). Simple Classification Algorithms to distinguish the oil slick type are designed based on standard multivariate data analysis techniques. Various attributes of geometry, shape, and dimension that describe the oil slick Size Information are combined with SAR-derived backscatter coefficients—sigma-(σo), beta-(βo), and gamma-(γo) naught. The combination of several of these characteristics is capable of distinguishing the oil slick type with ~70% of overall accuracy, however, the sole and simple use of two specific oil slick’s Size Information (i.e., area and perimeter) is equally capable of distinguishing seeps from spills. The data mining exercise of our EDA promotes a novel idea bridging petroleum pollution and remote sensing research, thus paving the way to further investigate the satellite synoptic view to express geophysical differences between seeped and spilled oil observed on the sea surface for systematic use. Full article
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Open AccessArticle
A Polygon and Point-Based Approach to Matching Geospatial Features
ISPRS Int. J. Geo-Inf. 2017, 6(12), 399; doi:10.3390/ijgi6120399 -
Abstract
A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to
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A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching. Full article
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Open AccessArticle
Development of a Change Detection Method with Low-Performance Point Cloud Data for Updating Three-Dimensional Road Maps
ISPRS Int. J. Geo-Inf. 2017, 6(12), 398; doi:10.3390/ijgi6120398 -
Abstract
Three-dimensional (3D) road maps have garnered significant attention recently because of applications such as autonomous driving. For 3D road maps to remain accurate and up-to-date, an appropriate updating method is crucial. However, there are currently no updating methods with both satisfactorily high frequency
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Three-dimensional (3D) road maps have garnered significant attention recently because of applications such as autonomous driving. For 3D road maps to remain accurate and up-to-date, an appropriate updating method is crucial. However, there are currently no updating methods with both satisfactorily high frequency and accuracy. An effective strategy would be to frequently acquire point clouds from regular vehicles, and then take detailed measurements only where necessary. However, there are three challenges when using data from regular vehicles. First, the accuracy and density of the points are comparatively low. Second, the measurement ranges vary for different measurements. Third, tentative changes such as pedestrians must be discriminated from real changes. The method proposed in this paper consists of registration and change detection methods. We first prepare the synthetic data obtained from regular vehicles using mobile mapping system data as a base reference. We then apply our proposed change detection method, in which the occupancy grid method is integrated with Dempster–Shafer theory to deal with occlusions and tentative changes. The results show that the proposed method can detect road environment changes, and it is easy to find changed parts through visualization. The work contributes towards sustainable updates and applications of 3D road maps. Full article
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Open AccessReview
Trends and Opportunities of BIM-GIS Integration in the Architecture, Engineering and Construction Industry: A Review from a Spatio-Temporal Statistical Perspective
ISPRS Int. J. Geo-Inf. 2017, 6(12), 397; doi:10.3390/ijgi6120397 -
Abstract
The integration of building information modelling (BIM) and geographic information system (GIS) in construction management is a new and fast developing trend in recent years, from research to industrial practice. BIM has advantages on rich geometric and semantic information through the building life
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The integration of building information modelling (BIM) and geographic information system (GIS) in construction management is a new and fast developing trend in recent years, from research to industrial practice. BIM has advantages on rich geometric and semantic information through the building life cycle, while GIS is a broad field covering geovisualization-based decision making and geospatial modelling. However, most current studies of BIM-GIS integration focus on the integration techniques but lack theories and methods for further data analysis and mathematic modelling. This paper reviews the applications and discusses future trends of BIM-GIS integration in the architecture, engineering and construction (AEC) industry based on the studies of 96 high-quality research articles from a spatio-temporal statistical perspective. The analysis of these applications helps reveal the evolution progress of BIM-GIS integration. Results show that the utilization of BIM-GIS integration in the AEC industry requires systematic theories beyond integration technologies and deep applications of mathematical modeling methods, including spatio-temporal statistical modeling in GIS and 4D/nD BIM simulation and management. Opportunities of BIM-GIS integration are outlined as three hypotheses in the AEC industry for future research on the in-depth integration of BIM and GIS. BIM-GIS integration hypotheses enable more comprehensive applications through the life cycle of AEC projects. Full article
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Open AccessArticle
Inferring Social Functions Available in the Metro Station Area from Passengers’ Staying Activities in Smart Card Data
ISPRS Int. J. Geo-Inf. 2017, 6(12), 394; doi:10.3390/ijgi6120394 -
Abstract
The function of a metro station area is vital for city planners to consider when establishing a context-aware Transit-Oriented Development policy around the station area. However, the functions of metro station areas are hard to infer using the static land use distribution and
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The function of a metro station area is vital for city planners to consider when establishing a context-aware Transit-Oriented Development policy around the station area. However, the functions of metro station areas are hard to infer using the static land use distribution and other traditional survey datasets. In this paper, we propose a method to infer the functions occurring around the metro station catchment areas according to the patterns of staying activities derived from smart card data. We first define the staying activities by the spatial and temporal constraints of the two consecutive alighting and boarding records from the individual travel profile. Then we cluster and label the whole staying activities by considering the features of duration, frequency, and start time. By analyzing the percentage of different types of aggregated activities happening around each metro station, we cluster and explore the functions of the metro station area. Taking Wuhan as a case study, we analyze the results of Wuhan metro systems and discuss the similarities and differences between the functions and the land use distribution around the station area. The results show that although there exist some agreements, there is also a gap between the human activities and the land uses around the station area. These findings could give us deeper insight into how people act around the stations by metro systems, which will ultimately benefit the urban planning and policy development. Full article
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Open AccessArticle
Land-Use Suitability in Northeast Iran: Application of AHP-GIS Hybrid Model
ISPRS Int. J. Geo-Inf. 2017, 6(12), 396; doi:10.3390/ijgi6120396 -
Abstract
Land-use suitability is the ability of a given type of land to support a defined use. Analysis of land-use suitability requires the consideration of a variety of criteria, not only the natural/physical capacity of a land unit, but also its socioeconomic and environmental
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Land-use suitability is the ability of a given type of land to support a defined use. Analysis of land-use suitability requires the consideration of a variety of criteria, not only the natural/physical capacity of a land unit, but also its socioeconomic and environmental impact implications. As land suitability is assessed within a Geographic Information System (GIS) environment, it is formulated as a multi-criteria decision making (MCDM) problem. The study was conducted in the Sangab Plain in northeast Iran. We investigated the study area’s suitability for grassland and agricultural uses. A hybrid method of the analytic hierarchy process (AHP) and GIS methodology was applied to evaluate land suitability based on a set of criteria and sub-criteria. Results showed that 20% of the study area had high (rich), 65% had medium (fair), and 15% had low (poor) suitability for agriculture. In terms of grassland use, the comparable amounts were, respectively, about 7%, 23%, and 70%. The lands of the Sangab Plain have medium potential for agricultural use and low potential for grassland use. This paper used both qualitative and quantitative techniques. Full article
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Open AccessFeature PaperArticle
Species Distribution Modeling: Comparison of Fixed and Mixed Effects Models Using INLA
ISPRS Int. J. Geo-Inf. 2017, 6(12), 391; doi:10.3390/ijgi6120391 -
Abstract
Invasive alien species are among the most important, least controlled, and least reversible of human impacts on the world’s ecosystems, with negative consequences affecting biodiversity and socioeconomic systems. Species distribution models have become a fundamental tool in assessing the potential spread of invasive
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Invasive alien species are among the most important, least controlled, and least reversible of human impacts on the world’s ecosystems, with negative consequences affecting biodiversity and socioeconomic systems. Species distribution models have become a fundamental tool in assessing the potential spread of invasive species in face of their native counterparts. In this study we compared two different modeling techniques: (i) fixed effects models accounting for the effect of ecogeographical variables (EGVs); and (ii) mixed effects models including also a Gaussian random field (GRF) to model spatial correlation (Matérn covariance function). To estimate the potential distribution of Pittosporum undulatum and Morella faya (respectively, invasive and native trees), we used geo-referenced data of their distribution in Pico and São Miguel islands (Azores) and topographic, climatic and land use EGVs. Fixed effects models run with maximum likelihood or the INLA (Integrated Nested Laplace Approximation) approach provided very similar results, even when reducing the size of the presences data set. The addition of the GRF increased model adjustment (lower Deviance Information Criterion), particularly for the less abundant tree, M. faya. However, the random field parameters were clearly affected by sample size and species distribution pattern. A high degree of spatial autocorrelation was found and should be taken into account when modeling species distribution. Full article
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Open AccessArticle
The Use of Nadir and Oblique UAV Images for Building Knowledge
ISPRS Int. J. Geo-Inf. 2017, 6(12), 393; doi:10.3390/ijgi6120393 -
Abstract
This paper focuses on the processing and study of 3D models obtained from images captured by an unmanned aerial vehicle (UAV). In particular, we wanted to study the accuracy gains achieved in the surveying and the measurement, such as height, area, and volume,
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This paper focuses on the processing and study of 3D models obtained from images captured by an unmanned aerial vehicle (UAV). In particular, we wanted to study the accuracy gains achieved in the surveying and the measurement, such as height, area, and volume, of the dimensions of the buildings in the 3D models obtained with both nadir and oblique UAV flights. These latter types of flights are particularly suitable for the 3D modeling of cities or urban agglomerations, where it is important to achieve a complete building reconstruction, including façades and footprints of buildings. For this purpose, several UAV surveys with both nadir and oblique axes were performed. The nadir flight acquired images over an area of about 3.5 hectares containing 30 buildings, while the second flight, performed with both a nadir camera and an oblique camera, was conducted on a single building. The images from the flights were processed with Photoscan software by Agisoft and with Pix4D, studying their different potentialities and functionality. The results were compared with the data from the 1:2000 scale Geotopographic Database (DBGT), with the results of a Global Navigation Satellite System (GNSS) survey and with 3D model from the Terrestrial Laser Scanner (TLS) survey. The obtained results have shown that oblique UAV flights increase the achievable accuracy both in terms of the number of points in a point cloud, and in the in measurements taken on the 3D models, with respect to the limited cost, and at the increase in time for surveying and image processing. Full article
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Open AccessArticle
Transdisciplinary Foundations of Geospatial Data Science
ISPRS Int. J. Geo-Inf. 2017, 6(12), 395; doi:10.3390/ijgi6120395 -
Abstract
Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness
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Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement. Full article
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Open AccessArticle
An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density
ISPRS Int. J. Geo-Inf. 2017, 6(12), 392; doi:10.3390/ijgi6120392 -
Abstract
Rapidly growing Global Positioning System (GPS) data plays an important role in trajectory and their applications (e.g., GPS-enabled smart devices). In order to employ K-means to mine the better origins and destinations (OD) behind the GPS data and overcome its shortcomings including slowness
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Rapidly growing Global Positioning System (GPS) data plays an important role in trajectory and their applications (e.g., GPS-enabled smart devices). In order to employ K-means to mine the better origins and destinations (OD) behind the GPS data and overcome its shortcomings including slowness of convergence, sensitivity to initial seeds selection, and getting stuck in a local optimum, this paper proposes and focuses on a novel niche genetic algorithm (NGA) with density and noise for K-means clustering (NoiseClust). In NoiseClust, an improved noise method and K-means++ are proposed to produce the initial population and capture higher quality seeds that can automatically determine the proper number of clusters, and also handle the different sizes and shapes of genes. A density-based method is presented to divide the number of niches, with its aim to maintain population diversity. Adaptive probabilities of crossover and mutation are also employed to prevent the convergence to a local optimum. Finally, the centers (the best chromosome) are obtained and then fed into the K-means as initial seeds to generate even higher quality clustering results by allowing the initial seeds to readjust as needed. Experimental results based on taxi GPS data sets demonstrate that NoiseClust has high performance and effectiveness, and easily mine the city’s situations in four taxi GPS data sets. Full article
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Open AccessFeature PaperArticle
A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods
ISPRS Int. J. Geo-Inf. 2017, 6(12), 389; doi:10.3390/ijgi6120389 -
Abstract
The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a
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The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data. Full article
Open AccessArticle
Implementing Data-Dependent Triangulations with Higher Order Delaunay Triangulations
ISPRS Int. J. Geo-Inf. 2017, 6(12), 390; doi:10.3390/ijgi6120390 -
Abstract
The Delaunay triangulation is the standard choice for building triangulated irregular networks (TINs) to represent terrain surfaces. However, the Delaunay triangulation is based only on the 2D coordinates of the data points, ignoring their elevation. This can affect the quality of the approximating
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The Delaunay triangulation is the standard choice for building triangulated irregular networks (TINs) to represent terrain surfaces. However, the Delaunay triangulation is based only on the 2D coordinates of the data points, ignoring their elevation. This can affect the quality of the approximating surface. In fact, it has long been recognized that sometimes it may be beneficial to use other, non-Delaunay, criteria that take elevation into account to build TINs. Data-dependent triangulations were introduced decades ago to address this exact issue. However, data-dependent trianguations are rarely used in practice, mostly because the optimization of data-dependent criteria often results in triangulations with many slivers (i.e., thin and elongated triangles), which can cause several types of problems. More recently, in the field of computational geometry, higher order Delaunay triangulations (HODTs) were introduced, trying to tackle both issues at the same time—data-dependent criteria and good triangle shape—by combining data-dependent criteria with a relaxation of the Delaunay criterion. In this paper, we present the first extensive experimental study on the practical use of HODTs, as a tool to build data-dependent TINs. We present experiments with two USGS 30m digital elevation models that show that the use of HODTs can give significant improvements over the Delaunay triangulation for the criteria previously identified as most important for data-dependent triangulations, often with only a minor increase in running times. The triangulations produced have measure values comparable to those obtained with pure data-dependent approaches, without compromising the shape of the triangles, and can be computed much faster. Full article
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Open AccessArticle
Hierarchical Model for the Similarity Measurement of a Complex Holed-Region Entity Scene
ISPRS Int. J. Geo-Inf. 2017, 6(12), 388; doi:10.3390/ijgi6120388 -
Abstract
Complex multi-holed-region entity scenes (i.e., sets of random region with holes) are common in spatial database systems, spatial query languages, and the Geographic Information System (GIS). A multi-holed-region (region with an arbitrary number of holes) is an abstraction of the real world that
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Complex multi-holed-region entity scenes (i.e., sets of random region with holes) are common in spatial database systems, spatial query languages, and the Geographic Information System (GIS). A multi-holed-region (region with an arbitrary number of holes) is an abstraction of the real world that primarily represents geographic objects that have more than one interior boundary, such as areas that contain several lakes or lakes that contain islands. When the similarity of the two complex holed-region entity scenes is measured, the number of regions in the scenes and the number of holes in the regions are usually different between the two scenes, which complicates the matching relationships of holed-regions and holes. The aim of this research is to develop several holed-region similarity metrics and propose a hierarchical model to measure comprehensively the similarity between two complex holed-region entity scenes. The procedure first divides a complex entity scene into three layers: a complex scene, a micro-spatial-scene, and a simple entity (hole). The relationships between the adjacent layers are considered to be sets of relationships, and each level of similarity measurements is nested with the adjacent one. Next, entity matching is performed from top to bottom, while the similarity results are calculated from local to global. In addition, we utilize position graphs to describe the distribution of the holed-regions and subsequently describe the directions between the holes using a feature matrix. A case study that uses the Great Lakes in North America in 1986 and 2015 as experimental data illustrates the entire similarity measurement process between two complex holed-region entity scenes. The experimental results show that the hierarchical model accounts for the relationships of the different layers in the entire complex holed-region entity scene. The model can effectively calculate the similarity of complex holed-region entity scenes, even if the two scenes comprise different regions and have different holes in each region. Full article
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Open AccessFeature PaperArticle
Machine Learning Techniques for Modelling Short Term Land-Use Change
ISPRS Int. J. Geo-Inf. 2017, 6(12), 387; doi:10.3390/ijgi6120387 -
Abstract
The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN), and Support Vector Machines
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The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN), and Support Vector Machines (SVM) for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes. Full article
Open AccessArticle
Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis
ISPRS Int. J. Geo-Inf. 2017, 6(12), 386; doi:10.3390/ijgi6120386 -
Abstract
In Geographic Object-based Image Analysis (GEOBIA), identification of image objects is normally achieved using rule-based classification techniques supported by appropriate domain knowledge. However, GEOBIA currently lacks a systematic method to formalise the domain knowledge required for image object identification. Ontology provides a representation
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In Geographic Object-based Image Analysis (GEOBIA), identification of image objects is normally achieved using rule-based classification techniques supported by appropriate domain knowledge. However, GEOBIA currently lacks a systematic method to formalise the domain knowledge required for image object identification. Ontology provides a representation vocabulary for characterising domain-specific classes. This study proposes an ontological framework that conceptualises domain knowledge in order to support the application of rule-based classifications. The proposed ontological framework is tested with a landslide case study. The Web Ontology Language (OWL) is used to construct an ontology in the landslide domain. The segmented image objects with extracted features are incorporated into the ontology as instances. The classification rules are written in Semantic Web Rule Language (SWRL) and executed using a semantic reasoner to assign instances to appropriate landslide classes. Machine learning techniques are used to predict new threshold values for feature attributes in the rules. Our framework is compared with published work on landslide detection where ontology was not used for the image classification. Our results demonstrate that a classification derived from the ontological framework accords with non-ontological methods. This study benchmarks the ontological method providing an alternative approach for image classification in the case study of landslides. Full article
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