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ISPRS Int. J. Geo-Inf., Volume 7, Issue 12 (December 2018)

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Open AccessArticle Cartographic Line Generalization Based on Radius of Curvature Analysis
ISPRS Int. J. Geo-Inf. 2018, 7(12), 477; https://doi.org/10.3390/ijgi7120477 (registering DOI)
Received: 21 August 2018 / Revised: 28 November 2018 / Accepted: 6 December 2018 / Published: 12 December 2018
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Abstract
Cartographic generalization is one of the important processes of transforming the content of both analogue and digital maps. The process of reducing details on the map has to be conducted in a planned way in each case when the map scale is to
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Cartographic generalization is one of the important processes of transforming the content of both analogue and digital maps. The process of reducing details on the map has to be conducted in a planned way in each case when the map scale is to be reduced. As far as digital maps are concerned, numerous algorithms are used for the generalization of vector line elements. They are used if the scale of the map (on screen or printed) is changed, or in the process of smoothing vector lines (e.g., contours). The most popular method of reducing the number of vertices of a vector line is the Douglas-Peucker algorithm. An important feature of most algorithms is the fact that they do not take into account the cartographic properties of the transformed map element. Having analysed the existing methods of generalization, the authors developed a proprietary algorithm that is based on the analysis of the curvature of the vector line and fulfils the condition of objective generalization for elements of digital maps that may be used to transform open and closed vector lines. The paper discusses the operation of this algorithm, along with the graphic presentation of the generalization results for vector lines and the analysis of their accuracy. Treating the set of verification radii of a vector line as a statistical series, the authors propose applying statistical indices of position of these series, connected with the shape of the vector line, as the threshold parameters of generalization. The developed algorithm allows for linking the generalization parameters directly to the scale of the topographic map that was obtained after generalization. The results of the operation of the algorithm were compared to the results of the reduction of vertices with use of the Douglas-Peucker algorithm. The results demonstrated that the proposed algorithm not only reduced the number of vertices, but that it also smoothed the shape of physiographic lines, if applied to them. The authors demonstrated that the errors of smoothing and position of vertices did not exceed the acceptable values for the relevant scales of topographic maps. The developed algorithm allows for adjusting the surface of the generalized areas to their initial value more precisely. The advantage of the developed algorithm consists in the possibility to apply statistical indices that take the shape of lines into account to define the generalization parameters. Full article
(This article belongs to the Special Issue Smart Cartography for Big Data Solutions)
Open AccessArticle On the Statistical Distribution of the Nonzero Spatial Autocorrelation Parameter in a Simultaneous Autoregressive Model
ISPRS Int. J. Geo-Inf. 2018, 7(12), 476; https://doi.org/10.3390/ijgi7120476 (registering DOI)
Received: 27 October 2018 / Revised: 29 November 2018 / Accepted: 6 December 2018 / Published: 12 December 2018
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Abstract
This paper focuses on the spatial autocorrelation parameter ρ of the simultaneous autoregressive model, and furnishes its sampling distribution for nonzero values, for two regular square (rook and queen) tessellations as well as a hexagonal case with rook connectivity, using Monte Carlo simulation
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This paper focuses on the spatial autocorrelation parameter ρ of the simultaneous autoregressive model, and furnishes its sampling distribution for nonzero values, for two regular square (rook and queen) tessellations as well as a hexagonal case with rook connectivity, using Monte Carlo simulation experiments with a large sample size. The regular square lattice directly relates to increasingly used, remotely sensed images, whereas the regular hexagonal configuration is frequently used in sampling and aggregation situations. Results suggest an asymptotic normal distribution for estimated ρ. More specifically, this paper posits functions between ρ and its variance for three adjacency structures, which makes hypothesis testing implementable and furnishes an easily-computed version of the asymptotic variance for ρ at zero for each configuration. In addition, it also presents three examples, where the first employed a simulated dataset for a zero spatial autocorrelation case, and the other two used two empirical datasets—of these, one is a census block dataset for Wuhan (with a Moran coefficient of 0.53, allowing a null hypothesis of, e.g., ρ=0.7) to illustrate a moderate spatial autocorrelation case, and the other is a remotely sensed image of the Yellow Mountain region, China (with a Moran coefficient of 0.91, allowing a null hypothesis of, e.g., ρ=0.95) to illustrate a high spatial autocorrelation case. Full article
Open AccessArticle A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams
ISPRS Int. J. Geo-Inf. 2018, 7(12), 475; https://doi.org/10.3390/ijgi7120475
Received: 30 September 2018 / Revised: 28 November 2018 / Accepted: 6 December 2018 / Published: 11 December 2018
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Abstract
Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge from the underlying observations. This paper presents
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Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge from the underlying observations. This paper presents (1) a state of the art review into visual analytics of geospatial, spatio-temporal streaming data, and (2) proposes a framework based on the identified gaps from the review. The framework consists of (1) the data model that characterizes the sensor observation data, (2) the user model, which addresses the user queries and manages domain knowledge, (3) the design model, which handles the patterns that can be uncovered from the data and corresponding visualizations, and (4) the visualization model, which handles the rendering of the data. The conclusion from the visualization model is that streaming sensor observations require tools that can handle multivariate, multiscale, and time series displays. The design model reveals that the most useful patterns are those that show relationships, anomalies, and aggregations of the data. The user model highlights the need for handling missing data, dealing with high frequency changes, as well as the ability to review retrospective changes. Full article
(This article belongs to the Special Issue Spatial Stream Processing )
Open AccessEditorial Geoinformatics in Citizen Science
ISPRS Int. J. Geo-Inf. 2018, 7(12), 474; https://doi.org/10.3390/ijgi7120474
Received: 29 November 2018 / Accepted: 6 December 2018 / Published: 11 December 2018
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Abstract
This editorial introduces the special issue entitled “Geoinformatics in Citizen Science” of the ISPRS International Journal of Geo-Information. The issue includes papers dealing with three main topics. (1) Key tasks of citizen science (CS) in leveraging geoinformatics. This comprises descriptions of citizen
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This editorial introduces the special issue entitled “Geoinformatics in Citizen Science” of the ISPRS International Journal of Geo-Information. The issue includes papers dealing with three main topics. (1) Key tasks of citizen science (CS) in leveraging geoinformatics. This comprises descriptions of citizen science initiatives where geoinformation management and processing is the key means for discovering new knowledge, and it includes: (i) “hackAIR: Towards Raising Awareness about Air Quality in Europe by Developing a Collective Online Platform” by Kosmidis et al., (ii) “Coupling Traditional Monitoring and Citizen Science to Disentangle the Invasion of Halyomorpha halys” by Malek et al., and (iii) “Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data” by Foody et al. (2) Evaluations of approaches to handle geoinformation in CS. This examines citizen science initiatives which critically analyze approaches to acquire and handle geoinformation, and it includes: (iv) “CS Projects Involving Geoinformatics: A Survey of Implementation Approaches” by Criscuolo et al., (v) “Obstacles and Opportunities of Using a Mobile App for Marine Mammal Research” by Hann et al., (vi) “OSM Data Import as an Outreach Tool to Trigger Community Growth? A Case Study in Miami” by Juhász and Hochmair, and (vii) “Experiences with Citizen-Sourced VGI in Challenging Circumstances“ by Hameed et al. (3) Novel geoinformatics research issues: (viii) “A New Method for the Assessment of Spatial Accuracy and Completeness of OpenStreetMap Building Footprints” by Brovelli and Zamboni, (ix) “A Citizen Science Approach for Collecting Toponyms” by Perdana and Ostermann, and (x) “An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks” by Khazaei and Alimohammadi. Full article
(This article belongs to the Special Issue Geoinformatics in Citizen Science)
Open AccessArticle Assessment of Displacements of Linestrings Based on Homologous Vertexes
ISPRS Int. J. Geo-Inf. 2018, 7(12), 473; https://doi.org/10.3390/ijgi7120473
Received: 6 November 2018 / Revised: 29 November 2018 / Accepted: 6 December 2018 / Published: 9 December 2018
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Abstract
This study describes a new method that was developed in order to assess the displacements between two linestrings that represent the same element in two datasets based on their shape. Until now, all existing line-based methods have been focused on the calculation of
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This study describes a new method that was developed in order to assess the displacements between two linestrings that represent the same element in two datasets based on their shape. Until now, all existing line-based methods have been focused on the calculation of distances or buffer inclusions between the two linestrings. However, these approaches assess a spatial difference between two linestrings, but they can hide the displacements that were suffered because of the geometry of the linestrings themselves. In our approach, the shapes of the linestrings are taken into account in order to identify homologous vertexes and estimate real displacements. Between two lines a pair of homologous vertices are defined as those that represent in reality the same characteristic feature of the line. Homologous vertexes can be detected by means of any appropriate algorithm. In order to test this method, we developed a design of experiment that was based on its application to a large dataset of lines classified into five sinuosity classes. These datasets were obtained from an external source that contains perturbed linestrings with several known random and systematic disturbances. 496 linestrings and 59 configurations were used in this experiment. The results have demonstrated the viability of the proposed method in estimating the real displacement of the lines, and consequently assessing their positional accuracy. Full article
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Open AccessArticle A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture
ISPRS Int. J. Geo-Inf. 2018, 7(12), 472; https://doi.org/10.3390/ijgi7120472
Received: 2 October 2018 / Revised: 3 December 2018 / Accepted: 5 December 2018 / Published: 7 December 2018
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Abstract
The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate
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The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks. Full article
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Open AccessFeature PaperArticle Identification of Experimental and Control Areas for CCTV Effectiveness Assessment—The Issue of Spatially Aggregated Data
ISPRS Int. J. Geo-Inf. 2018, 7(12), 471; https://doi.org/10.3390/ijgi7120471
Received: 21 October 2018 / Revised: 1 December 2018 / Accepted: 5 December 2018 / Published: 7 December 2018
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Abstract
Progress in surveillance technology has led to the development of Closed-Circuit Television (CCTV) systems in cities around the world. Cameras are considered instrumental in crime reduction, yet existing research does not unambiguously answer the question whether installing them affects the number of crimes
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Progress in surveillance technology has led to the development of Closed-Circuit Television (CCTV) systems in cities around the world. Cameras are considered instrumental in crime reduction, yet existing research does not unambiguously answer the question whether installing them affects the number of crimes committed. The quasi-experimental method usually applied to evaluate CCTV systems’ effectiveness faces difficulties with data quantity and quality. Data quantity has a bearing on the number of crimes that can be conclusively inferred using the experimental procedure. Data quality affects the level of crime data aggregation. The lack of the exact location of a crime incident in the form of a street address or geographic coordinates hinders the selection procedure of experimental and control areas. In this paper we propose an innovative method of dealing with data limitations in a quasi-experimental study on the effectiveness of CCTV systems in Poland. As police data on crime incidents are geocoded onto a neighborhood or a street, we designed a method to overcome this drawback by applying similarity measures to time series and landscape metrics. The method makes it possible to determine experimental (test) and control areas which are necessary to conduct the study. Full article
(This article belongs to the Special Issue GIS for Safety & Security Management)
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Open AccessEditorial Geospatial Methods and Tools for Natural Risk Management and Communications
ISPRS Int. J. Geo-Inf. 2018, 7(12), 470; https://doi.org/10.3390/ijgi7120470
Received: 28 November 2018 / Accepted: 29 November 2018 / Published: 2 December 2018
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Abstract
In the last decade, real-time access to data and the use of high-resolution spatial information have provided scientists and engineers with valuable information to help them understand risk. At the same time, there has been a rapid growth of novel and cutting-edge information
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In the last decade, real-time access to data and the use of high-resolution spatial information have provided scientists and engineers with valuable information to help them understand risk. At the same time, there has been a rapid growth of novel and cutting-edge information and communication technologies for the collection, analysis and dissemination of data, re-inventing the way in which risk management is carried out throughout its cycle (risk identification and reduction, preparedness, disaster relief and recovery). The applications of those geospatial technologies are expected to enable better mitigation of, and adaptation to, the disastrous impact of natural hazards. The description of risks may particularly benefit from the integrated use of new algorithms and monitoring techniques. The ability of new tools to carry out intensive analyses over huge datasets makes it possible to perform future risk assessments, keeping abreast of temporal and spatial changes in hazard, exposure, and vulnerability. The present special issue aims to describe the state-of-the-art of natural risk assessment, management, and communication using new geospatial models and Earth Observation (EO)architecture. More specifically, we have collected a number of contributions dealing with: (1) applications of EO data and machine learning techniques for hazard, vulnerability and risk mapping; (2) natural hazards monitoring and forecasting geospatial systems; (3) modeling of spatiotemporal resource optimization for emergency management in the post-disaster phase; and (4) development of tools and platforms for risk projection assessment and communication of inherent uncertainties. Full article
Open AccessArticle Reduction of Map Information Regulates Visual Attention without Affecting Route Recognition Performance
ISPRS Int. J. Geo-Inf. 2018, 7(12), 469; https://doi.org/10.3390/ijgi7120469
Received: 27 September 2018 / Revised: 24 November 2018 / Accepted: 28 November 2018 / Published: 30 November 2018
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Abstract
Map-based navigation is a diverse task that stands in contradiction to the goal of completeness of web mapping services. As each navigation task is different, it also requires and can dispense with different map information to support effective and efficient wayfinding. Task-oriented reduction
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Map-based navigation is a diverse task that stands in contradiction to the goal of completeness of web mapping services. As each navigation task is different, it also requires and can dispense with different map information to support effective and efficient wayfinding. Task-oriented reduction of the elements displayed in a map may therefore support navigation. In order to investigate effects of map reduction on route recognition and visual attention towards specific map elements, we created maps in which areas offside an inserted route were displayed as transparent. In a route memory experiment, where participants had to memorize routes and match them to routes displayed in following stimuli, these maps were compared to unmodified maps. Eye movement analyses revealed that in the reduced maps, areas offside the route were fixated less often. Route recognition performance was not affected by the map reduction. Our results indicate that task-oriented map reduction may direct visual attention towards relevant map elements at no cost for route recognition. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
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Open AccessArticle Optimising Citizen-Driven Air Quality Monitoring Networks for Cities
ISPRS Int. J. Geo-Inf. 2018, 7(12), 468; https://doi.org/10.3390/ijgi7120468
Received: 31 August 2018 / Revised: 23 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
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Abstract
Air quality has had a significant impact on public health, the environment and eventually on the economy of countries for decades. Effectively mitigating air pollution in urban areas necessitates accurate air quality exposure information. Recent advancements in sensor technology and the increasing popularity
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Air quality has had a significant impact on public health, the environment and eventually on the economy of countries for decades. Effectively mitigating air pollution in urban areas necessitates accurate air quality exposure information. Recent advancements in sensor technology and the increasing popularity of volunteered geographic information (VGI) open up new possibilities for air quality exposure assessment in cities. However, citizens and their sensors are put in areas deemed to be subjectively of interest (e.g., where citizens live, school of their kids or working spaces), and this leads to missed opportunities when it comes to optimal air quality exposure assessment. In addition, while the current literature on VGI has extensively discussed data quality and citizen engagement issues, few works, if any, offer techniques to fine-tune VGI contributions for an optimal air quality exposure assessment. This article presents and tests an approach to minimise land use regression prediction errors on citizen-contributed data. The approach was evaluated using a dataset (N = 116 sensors) from the city of Stuttgart, Germany. The comparison between the existing network design and the combination of locations selected by the optimisation method has shown a drop in spatial mean prediction error by 52%. The ideas presented in this article are useful for the systematic deployment of VGI air quality sensors, and can aid in the creation of higher resolution, more realistic maps for air quality monitoring in cities. Full article
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Open AccessArticle HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time
ISPRS Int. J. Geo-Inf. 2018, 7(12), 467; https://doi.org/10.3390/ijgi7120467
Received: 30 October 2018 / Revised: 24 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
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Abstract
Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly
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Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data. Full article
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
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Open AccessArticle A Remote Sensing Algorithm of Column-Integrated Algal Biomass Covering Algal Bloom Conditions in a Shallow Eutrophic Lake
ISPRS Int. J. Geo-Inf. 2018, 7(12), 466; https://doi.org/10.3390/ijgi7120466
Received: 31 October 2018 / Revised: 21 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
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Abstract
Column integrated algal biomass provides a robust indicator for eutrophication evaluation because it considers the vertical variability of phytoplankton. However, most remote sensing-based inversion algorithms of column algal biomass assume a homogenous distribution of phytoplankton within the water column. This study proposes a
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Column integrated algal biomass provides a robust indicator for eutrophication evaluation because it considers the vertical variability of phytoplankton. However, most remote sensing-based inversion algorithms of column algal biomass assume a homogenous distribution of phytoplankton within the water column. This study proposes a new remote sensing-based algorithm to estimate column integrated algal biomass incorporating different possible vertical profiles. The field sampling was based on five surveys in Lake Chaohu, a large eutrophic shallow lake in China. Field measurements revealed a significant variation in phytoplankton profiles in the water column during algal bloom conditions. The column integrated algal biomass retrieval algorithm developed in the present study is shown to effectively describe the vertical variation of algal biomass in shallow eutrophic water. The Baseline Normalized Difference Bloom Index (BNDBI) was adopted to estimate algal biomass integrated from the water surface to 40 cm. Then the relationship between 40 cm integrated algal biomass and the whole column algal biomass at various depths was built taking into consideration the hydrological and bathymetry data of each site. The algorithm was able to accurately estimate integrated algal biomass with R2 = 0.89, RMSE = 45.94 and URMSE = 28.58%. High accuracy was observed in the temporal consistency of satellite images (with the maximum MAPE = 7.41%). Sensitivity analysis demonstrated that the estimated algal biomass integrated from the water surface to 40 cm has the greatest influence on the estimated column integrated algal biomass. This algorithm can be used to explore the long-term variation of algal biomass to improve long-term analysis and management of eutrophic lakes. Full article
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Open AccessArticle Measuring Inequality of Opportunity in Access to Quality Basic Education: A Case Study in Florida, US
ISPRS Int. J. Geo-Inf. 2018, 7(12), 465; https://doi.org/10.3390/ijgi7120465
Received: 24 September 2018 / Revised: 21 November 2018 / Accepted: 26 November 2018 / Published: 29 November 2018
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Abstract
Providing all children equal access to essential services, such as primary education, has been set as a priority in the Sustainable Development Goals (SDG)’ agenda during the last two decades. Yet the Global Education Monitoring report in 2016 reveals that wide disparities between
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Providing all children equal access to essential services, such as primary education, has been set as a priority in the Sustainable Development Goals (SDG)’ agenda during the last two decades. Yet the Global Education Monitoring report in 2016 reveals that wide disparities between the rich and the poor persist in access to education of high quality. This study uses the Human Opportunity Index (HOI) to examine the equality of opportunity in access to basic education of high quality. By using enrollment and admission data from a case study in a large school district in the US in 2015/2016, this research evaluates the capacity of the HOI, in order to reveal disparities in access to school opportunities and examines how much of this inequality is explained by families’ pre-determined circumstances. The way of analyzing equality is by disaggregating applications’ data into circumstance groups, according to gender, geography, race/ethnicity, and other criteria. To capture the contribution of each circumstance to inequality of opportunity, the Shapley decomposition method is used. Findings show that the HOI is capable of systematically monitoring and examining existing admission policies and identifying inequality problems. Furthermore, the analysis of the contribution of each circumstance group can reveal admission criteria that have the potential to harm the educational opportunities for children. This assessment should provide valuable insights into the capability of the indicators to reveal where policy intervention is necessary and supply points of view on how policy can be improved. Full article
(This article belongs to the Special Issue Geo-Information and the Sustainable Development Goals (SDGs))
Open AccessCommunication How to Contextualize SDG 11? Looking at Indicators for Sustainable Urban Development in Germany
ISPRS Int. J. Geo-Inf. 2018, 7(12), 464; https://doi.org/10.3390/ijgi7120464
Received: 29 August 2018 / Revised: 22 November 2018 / Accepted: 26 November 2018 / Published: 29 November 2018
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Abstract
Agenda 2030 pursues a universal approach and identifies countries in the Global South and in the Global North that are in need of transformation toward sustainability. Therefore, countries of the Global North such as Germany have signed the commitment to implement the Sustainable
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Agenda 2030 pursues a universal approach and identifies countries in the Global South and in the Global North that are in need of transformation toward sustainability. Therefore, countries of the Global North such as Germany have signed the commitment to implement the Sustainable Development Goals (SDGs). However, the SDGs need to be “translated” to the specific national context. Existing sustainability indicators and monitoring and reporting systems need to be adjusted as well. Our paper evaluates how three different initiatives translated SDG 11 (“Make cities and human settlements inclusive, safe, resilient, and sustainable”) to the German context, given the specific role of cities in contributing to sustainable development. These initiatives included the official ‘National Sustainable Development Strategy’ of the German Government, a scientific initiative led by the ‘German Institute for Urban Affairs’, and a project carried out by the ‘Open Knowledge Foundation’, a non-governmental organization (NGO). This article aims to analyze how global goals addressing urban developments are contextualized on a national level. Our findings demonstrate that only a few of the original targets and indicators for SDG 11 are used in the German context; thus, major adjustments have been made according to the main sustainability challenges identified for Germany. Furthermore, our results show that the current contextualization of SDG 11 and sustainable urban development in Germany are still ongoing, and more changes and commitments need to be made. Full article
(This article belongs to the Special Issue Geo-Information and the Sustainable Development Goals (SDGs))
Open AccessArticle An Architecture for Mobile Outdoors Augmented Reality for Cultural Heritage
ISPRS Int. J. Geo-Inf. 2018, 7(12), 463; https://doi.org/10.3390/ijgi7120463
Received: 10 October 2018 / Revised: 19 November 2018 / Accepted: 26 November 2018 / Published: 29 November 2018
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Abstract
In this paper, we present the software architecture of a complete mobile tourist guide for cultural heritage sites located in the old town of Chania, Crete, Greece. This includes gamified components that motivate the user to traverse the suggested interest points, as well
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In this paper, we present the software architecture of a complete mobile tourist guide for cultural heritage sites located in the old town of Chania, Crete, Greece. This includes gamified components that motivate the user to traverse the suggested interest points, as well as technically challenging outdoors augmented reality (AR) visualization features. The main focus of the AR feature is to superimpose 3D models of historical buildings in their past state onto the real world, while users walk around the Venetian part of Chania’s city, exploring historical information in the form of text and images. We examined and tested registration and tracking mechanisms based on commercial AR frameworks in the challenging outdoor, sunny environment of a Mediterranean town, addressing relevant technical challenges. Upon visiting one of three significant monuments, a 3D model displaying the monument in its past state is visualized onto the mobile phone’s screen at the exact location of the real-world monument, while the user is exploring the area. A location-based experience was designed and integrated into the application, enveloping the 3D model with real-world information at the same time. The users are urged to explore interest areas and unlock historical information, while earning points following a gamified experience. By combining AR technologies with location-aware and gamified elements, we aim to promote the technologically enhanced public appreciation of cultural heritage sites and showcase the cultural depth of the city of Chania. Full article
(This article belongs to the Special Issue Data Acquisition and Processing in Cultural Heritage)
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Open AccessFeature PaperArticle Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops
ISPRS Int. J. Geo-Inf. 2018, 7(12), 462; https://doi.org/10.3390/ijgi7120462
Received: 19 September 2018 / Revised: 13 November 2018 / Accepted: 26 November 2018 / Published: 29 November 2018
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Abstract
The objective of this study is to evaluate operational methods for creating a particular type of urban vegetation map—one focused on vegetation over rooftops (VOR), specifically trees that extend over urban residential buildings. A key constraint was the use of passive remote sensing
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The objective of this study is to evaluate operational methods for creating a particular type of urban vegetation map—one focused on vegetation over rooftops (VOR), specifically trees that extend over urban residential buildings. A key constraint was the use of passive remote sensing data only. To achieve this, we (1) conduct a review of the urban remote sensing vegetation classification literature, and we then (2) discuss methods to derive a detailed map of VOR for a study area in Calgary, Alberta, Canada from a late season, high-resolution airborne orthomosaic based on an integration of Geographic Object-Based Image Analysis (GEOBIA), pre-classification filtering of image-objects using Volunteered Geographic Information (VGI), and a machine learning classifier. Pre-classification filtering lowered the computational burden of classification by reducing the number of input objects by 14%. Accuracy assessment results show that, despite the presence of senescing vegetation with low vegetation index values and deep shadows, classification using a small number of image-object spectral attributes as classification features (n = 9) had similar overall accuracy (88.5%) to a much more complex classification (91.8%) comprising a comprehensive set of spectral, texture, and spatial attributes as classification features (n = 86). This research provides an example of the very specific questions answerable about precise urban locations using a combination of high-resolution passive imagery and freely available VGI data. It highlights the benefits of pre-classification filtering and the judicious selection of features from image-object attributes to reduce processing load without sacrificing classification accuracy. Full article
(This article belongs to the Special Issue GEOBIA in a Changing World)
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Open AccessArticle Combining the Stock Unearthing Method and Structure-from-Motion Photogrammetry for a Gapless Estimation of Soil Mobilisation in Vineyards
ISPRS Int. J. Geo-Inf. 2018, 7(12), 461; https://doi.org/10.3390/ijgi7120461
Received: 13 September 2018 / Revised: 9 November 2018 / Accepted: 21 November 2018 / Published: 27 November 2018
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Abstract
In vineyards, especially on steep slopes like the Ruwer-Mosel Valley, Germany, soil erosion is a well-known environmental problem. Unfortunately, some enterprises and farmers are not aware of how much soil is being lost and the long-term negative impacts of soil erosion. The non-invasive
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In vineyards, especially on steep slopes like the Ruwer-Mosel Valley, Germany, soil erosion is a well-known environmental problem. Unfortunately, some enterprises and farmers are not aware of how much soil is being lost and the long-term negative impacts of soil erosion. The non-invasive technique of the stock unearthing method (SUM) can be used for a quick assessment of soil erosion in vineyards. SUM uses the graft union as a reference elevation for soil surface changes since the time of plantation commencement, which is modelled with the help of a geographic information system. A shortcoming of SUM is that the areas between the pair-vine cross sections are not surveyed, hence it is not accurate enough to identify erosion hot-spots. A structure-from-motion (SfM) photogrammetric technique is adopted to complement SUM to fill this data gap. Combining SUM (only measuring the graft unions) and SfM techniques could lead to an improved, easy and low-cost method with a higher accuracy for estimation of soil erosion based on interpolation by projection, and contact and gapless measuring. Thus, the main aim of this paper was to map the current soil surface level and to improve the accuracy of estimation of long-term soil mobilisation rates in vineyards. To achieve this goal, the TEPHOS (TErrestrial PHOtogrammetric Scanner), a static five camera array, was developed on a 20 m2 plot located in a steeply sloping vineyard of the Ruwer-Mosel Valley, Trier, Germany. A total soil mobilisation of 0.52 m3 (9.14 Mg ha yr−1) with soil surface level differences in excess of 30 cm in the 40 years since plantation commencement were recorded. Further research is, however, needed to reduce the number of photos used for the point cloud without loss of accuracy. This method can be useful for the observation of the impacts of other factors in vineyards, such as tillage erosion, runoff pathway detection or the trampling effect on soil erosion in vineyards. Full article
(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)
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Open AccessArticle Real-Time Visualization of Geo-Sensor Data Based on the Protocol-Coupling Symbol Construction Method
ISPRS Int. J. Geo-Inf. 2018, 7(12), 460; https://doi.org/10.3390/ijgi7120460
Received: 20 September 2018 / Revised: 16 November 2018 / Accepted: 26 November 2018 / Published: 27 November 2018
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Abstract
Obtaining and visualizing the internal state and position information of the remote device using sensors are important aspects of industrial manufacturing. For large-scale geo-sensors that have been recently used, map-based management and visualization of the geo-sensor devices have become ubiquitous. Users often build
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Obtaining and visualizing the internal state and position information of the remote device using sensors are important aspects of industrial manufacturing. For large-scale geo-sensors that have been recently used, map-based management and visualization of the geo-sensor devices have become ubiquitous. Users often build multiple map symbols to represent the multiple states of a device based on traditional map symbols. Visualizing multiple geo-sensor data in real time with one map symbol is difficult. In this paper, a protocol-coupling map symbol and a construction method for real-time data visualization is introduced where different sensor states of the geo-sensor are expressed with one symbol. The sensor data visualization method in supervisory control and data acquisition systems (SCADA) was introduced and applied to the construction and visualization process of map symbols. First, based on the traditional vector map symbols and the communication protocol parsing interface, the mapping relationship between the sensor data item and the graphic element is defined in the map symbol construction process. Second, by referring to the streaming services method in ArcGIS GeoEvent, geo-sensor data acquisition and a transfer broker in a GIS server is built, through which the real-time sensor data can be transferred from the remote side to the map client and used for map symbol rendering. Finally, the new map symbols are used for real-time geo-sensor data visualization in applications. In the application of the real-time monitoring of geo-sensor devices, remote device information was acquired by sensor and transmitted to the broker then cached on the server side. If the cached sensor data has changed compared to the previous, the changed data will be pushed to map client by broker. The communication module in the map client that communicates with the broker receives changed geo-sensor data and triggers a refresh of the map. Then the protocol-coupling map symbol is rendered according to the mapping profile and the status of the geo-sensor device will be displayed on the map in real time. All the methods and processes were verified in client-server and browser-server GIS architecture. Full article
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Open AccessArticle Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data
ISPRS Int. J. Geo-Inf. 2018, 7(12), 459; https://doi.org/10.3390/ijgi7120459
Received: 25 September 2018 / Revised: 12 November 2018 / Accepted: 22 November 2018 / Published: 27 November 2018
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Abstract
Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However,
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Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However, very few works have leveraged public bicycle-sharing data, which provides a useful feature in depicting people’s short-trip transportation within a city, in the studies of urban functions and structure. Because of its convenience, bicycle usage tends to be close to point-of-interest (POI) features, the combination of which will no doubt enhance the understanding of the trip purpose for characterizing different functional zones. In our study, we propose a data-driven approach that uses station-based public bicycle rental records together with POI data in Hangzhou, China to identify urban functional zones. Topic modelling, unsupervised clustering, and visual analytics are employed to delineate the function matrix, aggregate functional zones, and present mixed land uses. Our result shows that business areas, industrial areas, and residential areas can be well detected, which validates the effectiveness of data generated from this new transportation mode. The word cloud of function labels reveals the mixed land use of different types of urban functions and improves the understanding of city structures. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessArticle Towards HD Maps from Aerial Imagery: Robust Lane Marking Segmentation Using Country-Scale Imagery
ISPRS Int. J. Geo-Inf. 2018, 7(12), 458; https://doi.org/10.3390/ijgi7120458
Received: 7 October 2018 / Revised: 15 November 2018 / Accepted: 22 November 2018 / Published: 26 November 2018
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Abstract
The upraise of autonomous driving technologies asks for maps characterized bya broad range of features and quality parameters, in contrast to traditional navigation maps which in most cases are enriched graph-based models. This paper tackles several uncertainties within the domain of HD Maps.
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The upraise of autonomous driving technologies asks for maps characterized bya broad range of features and quality parameters, in contrast to traditional navigation maps which in most cases are enriched graph-based models. This paper tackles several uncertainties within the domain of HD Maps. The authors give an overview about the current state in extracting road features from aerial imagery for creating HD maps, before shifting the focus of the paper towards remote sensing technology. Possible data sources and their relevant parameters are listed. A random forest classifier is used, showing how these data can deliver HD Maps on a country-scale, meeting specific quality parameters. Full article
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Open AccessArticle AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context
ISPRS Int. J. Geo-Inf. 2018, 7(12), 457; https://doi.org/10.3390/ijgi7120457
Received: 16 August 2018 / Revised: 8 October 2018 / Accepted: 4 November 2018 / Published: 26 November 2018
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Abstract
The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes
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The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloud–shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are both conceptual and pragmatic in the definition of remote sensing best practices, which is the focus of efforts made by intergovernmental organizations such as the Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS). First, the ESA EO Level 2 product definition is recommended for consideration as state-of-the-art EO Analysis Ready Data (ARD) format. Second, systematic multi-sensor ESA EO Level 2 information product generation is regarded as: (a) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (b) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2 (proposed as Supplementary Materials), the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed. Full article
(This article belongs to the Special Issue GEOBIA in a Changing World)
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Open AccessArticle Characterizing Data Ecosystems to Support Official Statistics with Open Mapping Data for Reporting on Sustainable Development Goals
ISPRS Int. J. Geo-Inf. 2018, 7(12), 456; https://doi.org/10.3390/ijgi7120456
Received: 9 September 2018 / Revised: 30 October 2018 / Accepted: 12 November 2018 / Published: 24 November 2018
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Abstract
Reporting on the Sustainable Development Goals (SDGs) is complex given the wide variety of governmental and NGO actors involved in development projects as well as the increased number of targets and indicators. However, data on the wide variety of indicators must be collected
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Reporting on the Sustainable Development Goals (SDGs) is complex given the wide variety of governmental and NGO actors involved in development projects as well as the increased number of targets and indicators. However, data on the wide variety of indicators must be collected regularly, in a robust manner, comparable across but also within countries and at different administrative and disaggregated levels for adequate decision making to take place. Traditional census and household survey data is not enough. The increase in Small and Big Data streams have the potential to complement official statistics. The purpose of this research is to develop and evaluate a framework to characterize a data ecosystem in a developing country in its totality and to show how this can be used to identify data, outside the official statistics realm, that enriches the reporting on SDG indicators. Our method consisted of a literature study and an interpretative case study (two workshops with 60 and 35 participants and including two questionnaires, over 20 consultations and desk research). We focused on SDG 6.1.1. (Proportion of population using safely managed drinking water services) in rural Malawi. We propose a framework with five dimensions (actors, data supply, data infrastructure, data demand and data ecosystem governance). Results showed that many governmental and NGO actors are involved in water supply projects with different funding sources and little overall governance. There is a large variety of geospatial data sharing platforms and online accessible information management systems with however a low adoption due to limited internet connectivity and low data literacy. Lots of data is still not open. All this results in an immature data ecosystem. The characterization of the data ecosystem using the framework proves useful as it unveils gaps in data at geographical level and in terms of dimensionality (attributes per water point) as well as collaboration gaps. The data supply dimension of the framework allows identification of those datasets that have the right quality and lowest cost of data extraction to enrich official statistics. Overall, our analysis of the Malawian case study illustrated the complexities involved in achieving self-regulation through interaction, feedback and networked relationships. Additional complexities, typical for developing countries, include fragmentation, divide between governmental and non-governmental data activities, complex funding relationships and a data poor context. Full article
(This article belongs to the Special Issue Geo-Information and the Sustainable Development Goals (SDGs))
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Open AccessArticle From a Vegetation Index to a Sustainable Development Goal Indicator: Forest Trend Monitoring Using Three Decades of Earth Observations across Switzerland
ISPRS Int. J. Geo-Inf. 2018, 7(12), 455; https://doi.org/10.3390/ijgi7120455
Received: 29 August 2018 / Revised: 16 November 2018 / Accepted: 21 November 2018 / Published: 24 November 2018
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Abstract
Forests represent important habitats for species and provide multiple ecosystem services for human well-being. Preserving forests and other terrestrial ecosystems has become crucial to halt desertification, land degradation, and biodiversity loss worldwide, and is also one of the Sustainable Development Goals (SDGs) to
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Forests represent important habitats for species and provide multiple ecosystem services for human well-being. Preserving forests and other terrestrial ecosystems has become crucial to halt desertification, land degradation, and biodiversity loss worldwide, and is also one of the Sustainable Development Goals (SDGs) to be achieved by 2030. Remote sensing could greatly contribute to measuring progress toward SDGs by providing consistent and repetitive coverage of large areas, as well as information in various wavelengths, which facilitates the monitoring of environmental trends at various scales. This paper focuses on SDG indicator 15.1.1—“Forest area as a percentage of total land area” to demonstrate the potential of Earth Observation Data Cubes for SDGs. The approach presented here uses Landsat Analysis Ready Data (ARD) stored in the Swiss Data Cube, and offers a complementary method to ground-based approaches to monitor Switzerland’s forest extent based on the Normalized Difference Vegetation Index (NDVI). The proposed method performs time-series analyses to extract a forest/non-forest map and a graph representing the trend of SDG 15.1.1 indicator over time. Preliminary results suggest that this approach can identify similar forest extent and growth patterns to observed trends, and can therefore help monitor progress toward the selected SDG indicator more effectively. Full article
(This article belongs to the Special Issue Geo-Information and the Sustainable Development Goals (SDGs))
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Open AccessArticle Public Participation Using 3D Web-Based City Models: Opportunities for E-Participation in Kisumu, Kenya
ISPRS Int. J. Geo-Inf. 2018, 7(12), 454; https://doi.org/10.3390/ijgi7120454
Received: 30 August 2018 / Revised: 6 November 2018 / Accepted: 12 November 2018 / Published: 23 November 2018
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Abstract
Public participation is significant for the success of any urban planning project. However, most members of the general public are not planning professionals and may not understand the technical details of a 2D paper-based plan, which might hamper their participation. One way to
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Public participation is significant for the success of any urban planning project. However, most members of the general public are not planning professionals and may not understand the technical details of a 2D paper-based plan, which might hamper their participation. One way to expand the participation of citizens is to present plans in well-designed, user-friendly and interactive platforms that allow participation regardless of the technical skills of the participants. This paper investigates the impacts of the combined use of 3D visualization and e-participation on public participation in Kisumu, Kenya. A 3D city model, created with CityEngine2016, was exported into a web-based geoportal and used as a Planning Support System in two stakeholder workshops in order to evaluate its usability. In order to assess the workshops 300 questionnaires were given out to planning practitioners and interview were done with key informants. Five indicators were developed for evaluating the usability of the 3D model while the usability of e-participation was evaluated using communication, collaboration and learning as indicators. Results showed that effectiveness and efficiency varied within different professional groups while the questionnaires showed strong preference for e-participation methods, especially Short Message Servicess/Unstructured Supplementary Service Data and emails. The study concludes that the use of 3D visualization and e-participation has the potential to improve the quality and quantity of public participation and recommends further research on the subject. Full article
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Open AccessArticle Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices
ISPRS Int. J. Geo-Inf. 2018, 7(12), 453; https://doi.org/10.3390/ijgi7120453
Received: 7 September 2018 / Revised: 8 November 2018 / Accepted: 21 November 2018 / Published: 22 November 2018
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Abstract
Urban land cover classification and mapping is an important and ongoing research field in monitoring and managing urban sprawl and terrestrial ecosystems. The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological
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Urban land cover classification and mapping is an important and ongoing research field in monitoring and managing urban sprawl and terrestrial ecosystems. The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban areas is challenging due to their diversified activities and large spatial and temporal variations. To improve urban land cover classification and mapping, this study presents three new spectral indices and an automated approach to classifying four major urban land types: impervious, bare land, vegetation, and water. A modified normalized difference bare-land index (MNDBI) is proposed to enhance the separation of impervious and bare land. A tasseled cap water and vegetation index (TCWVI) is proposed to enhance the detection of vegetation and water areas. A shadow index (ShDI) is proposed to further improve water detection by separating water from shadows. An approach for optimizing the thresholds of the new indices is also developed. Finally, the optimized thresholds are used to classify land covers using a decision tree algorithm. Using Landsat-8 Operational Land Imager (OLI) data from two study sites (Hong Kong and Dhaka City, Bangladesh) with different urban characteristics, the proposed approach is systematically evaluated. Spectral separability analysis of the new indices is performed and compared with other common indices. The urban land cover classifications achieved by the proposed approach are compared with those of the classic support vector machine (SVM) algorithm. The proposed approach achieves an overall classification accuracy of 94–96%, which is superior to the accuracy of the SVM algorithm. Full article
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