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ISPRS Int. J. Geo-Inf., Volume 6, Issue 8 (August 2017)

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Cover Story (view full-size image) Pedestrian indoor localization systems often harness existing Wi-Fi infrastructures within any [...] Read more.
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Open AccessArticle Spatial-Spectral Graph Regularized Kernel Sparse Representation for Hyperspectral Image Classification
ISPRS Int. J. Geo-Inf. 2017, 6(8), 258; https://doi.org/10.3390/ijgi6080258
Received: 12 June 2017 / Revised: 13 August 2017 / Accepted: 18 August 2017 / Published: 22 August 2017
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Abstract
This paper presents a spatial-spectral method for hyperspectral image classification in the regularization framework of kernel sparse representation. First, two spatial-spectral constraint terms are appended to the sparse recovery model of kernel sparse representation. The first one is a graph-based spatially-smooth constraint which
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This paper presents a spatial-spectral method for hyperspectral image classification in the regularization framework of kernel sparse representation. First, two spatial-spectral constraint terms are appended to the sparse recovery model of kernel sparse representation. The first one is a graph-based spatially-smooth constraint which is utilized to describe the contextual information of hyperspectral images. The second one is a spatial location constraint, which is exploited to incorporate the prior knowledge of the location information of training pixels. Then, an efficient alternating direction method of multipliers is developed to solve the corresponding minimization problem. At last, the recovered sparse coefficient vectors are used to determine the labels of test pixels. Experimental results carried out on three real hyperspectral images point out the effectiveness of the proposed method. Full article
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Open AccessArticle An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities
ISPRS Int. J. Geo-Inf. 2017, 6(8), 257; https://doi.org/10.3390/ijgi6080257
Received: 29 June 2017 / Revised: 2 August 2017 / Accepted: 19 August 2017 / Published: 21 August 2017
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Abstract
Rapid urbanization in developing countries has been observed to be relatively high in the last two decades, especially in the Asian and African regions. Although many researchers have made efforts to improve the understanding of the urbanization trends of various cities in Asia
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Rapid urbanization in developing countries has been observed to be relatively high in the last two decades, especially in the Asian and African regions. Although many researchers have made efforts to improve the understanding of the urbanization trends of various cities in Asia and Africa, the absence of platforms where local stakeholders can visualize and obtain processed urbanization data for their specific needs or analysis, still remains a gap. In this paper, we present an Internet-based GIS platform called MEGA-WEB. The Platform was developed in view of the urban planning and management challenges in developing countries of Asia and Africa due to the limited availability of data resources, effective tools, and proficiency in data analysis. MEGA-WEB provides online access, visualization, spatial analysis, and data sharing services following a mashup framework of the MEGA-WEB Geo Web Services (GWS), with the third-party map services using HTML5/JavaScript techniques. Through the integration of GIS, remote sensing, geo-modelling, and Internet GIS, several indicators for analyzing urbanization are provided in MEGA-WEB to give diverse perspectives on the urbanization of not only the physical land surface condition, but also the relationships of population, energy use, and the environment. The design, architecture, system functions, and uses of MEGA-WEB are discussed in the paper. The MEGA-WEB project is aimed at contributing to sustainable urban development in developing countries of Asia and Africa. Full article
(This article belongs to the Special Issue Web and Mobile GIS)
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Open AccessFeature PaperArticle Spatiotemporal Assessment of Littoral Waterbirds for Establishing Ecological Indicators of Mediterranean Coastal Lagoons
ISPRS Int. J. Geo-Inf. 2017, 6(8), 256; https://doi.org/10.3390/ijgi6080256
Received: 24 July 2017 / Revised: 7 August 2017 / Accepted: 18 August 2017 / Published: 19 August 2017
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Abstract
Waterbirds are vital indicators of anthropogenic influence on the ecological status of Mediterranean coastal lagoons. Our study relates temporal waterbird data to key environmental gradients at catchment scale that have a structural or functional influence on littoral waterbird assemblages at different scales. During
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Waterbirds are vital indicators of anthropogenic influence on the ecological status of Mediterranean coastal lagoons. Our study relates temporal waterbird data to key environmental gradients at catchment scale that have a structural or functional influence on littoral waterbird assemblages at different scales. During two full-year cycles and two additional wintering seasons, the nearshore waterbird assemblages of the Mar Menor coastal lagoon (Murcia Region, SE Spain) were monitored monthly. Several biological indicator variables were related to the anthropogenic environmental gradient in the catchment area. Results showed that there was a strong dependence of waterbird assemblages on the distance to shore, emphasizing the importance of the first 100-m band, in which many species relevant to conservation converge on food resources. Well-preserved shoreline tracts therefore had a clear positive effect on community richness and diversity values, and were correlated with the occurrence of some species. These results clearly support the need for effective protection and restoration measures of such littoral habitats. Specific responses to local disturbing processes were nested within habitat and landscape preferences, supporting the value of aquatic birds as integrative ecological signals in semi-enclosed coastal systems. Moreover, waterbird-based indicators responded positively to environmental improvements both qualitatively and quantitatively. Full article
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Open AccessArticle Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment
ISPRS Int. J. Geo-Inf. 2017, 6(8), 255; https://doi.org/10.3390/ijgi6080255
Received: 15 June 2017 / Revised: 4 August 2017 / Accepted: 10 August 2017 / Published: 17 August 2017
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Abstract
A comprehensive strategy combining remote sensing and field data can be helpful for more effective agriculture management. Satellite data are suitable for monitoring large areas over time, while LiDAR provides specific and accurate data on height and relief. Both types of data can
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A comprehensive strategy combining remote sensing and field data can be helpful for more effective agriculture management. Satellite data are suitable for monitoring large areas over time, while LiDAR provides specific and accurate data on height and relief. Both types of data can be used for calibration and validation purposes, avoiding field visits and saving useful resources. In this paper, we propose a process for objective and automated identification of agricultural parcel features based on processing and combining Sentinel-2 data (to sense different types of irrigation patterns) and LiDAR data (to detect landscape elements). The proposed process was validated in several use cases in Spain, yielding high accuracy rates in the identification of irrigated areas and landscape elements. An important application example of the work reported in this paper is the European Union (EU) Common Agriculture Policy (CAP) funds assignment service, which would significantly benefit from a more objective and automated process for the identification of irrigated areas and landscape elements, thereby enabling the possibility for the EU to save significant amounts of money yearly. Full article
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Open AccessArticle Evolving Spatial Data Infrastructures and the Role of Adaptive Governance
ISPRS Int. J. Geo-Inf. 2017, 6(8), 254; https://doi.org/10.3390/ijgi6080254
Received: 9 June 2017 / Revised: 2 August 2017 / Accepted: 10 August 2017 / Published: 16 August 2017
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Abstract
Spatial data infrastructures (SDIs) are becoming more mature worldwide. However, despite this growing maturity, longitudinal research on the governance of SDIs is rare. The current research examines the governance history of two SDIs in the Netherlands and Flanders (Belgium). Both represent decades-long undertakings
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Spatial data infrastructures (SDIs) are becoming more mature worldwide. However, despite this growing maturity, longitudinal research on the governance of SDIs is rare. The current research examines the governance history of two SDIs in the Netherlands and Flanders (Belgium). Both represent decades-long undertakings to create a large-scale base map. During these processes, SDI governance changed, often quite radically. We analyse written accounts from geo-information industry magazines to determine if the SDI governance of these two base maps can be considered adaptive. We conclude that SDI governance was adaptive, as it changed considerably during the evolution of the two SDIs. However, we also find that most governance models did not hold up very long, as they were either not meeting their goals, were not satisfying all stakeholders or were not in alignment with new visions and ideas. In recent years, the policy instruments governing these base maps became increasingly diverse. In particular, more hierarchical instruments were introduced. Indeed, governance scholars increasingly agree that governance can better respond to changes when a broader mix of policy instruments is applied. Alas, this does not make SDI governance any less complex. Full article
(This article belongs to the Special Issue Innovative Geo-Information Tools for Governance)
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Open AccessArticle Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data
ISPRS Int. J. Geo-Inf. 2017, 6(8), 253; https://doi.org/10.3390/ijgi6080253
Received: 16 July 2017 / Revised: 5 August 2017 / Accepted: 10 August 2017 / Published: 16 August 2017
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Abstract
Travel times in urban road networks are highly stochastic. However, most existing travel time estimation methods only estimate the mean travel times, while ignoring travel time variances. To this end, this paper proposes a robust travel time distribution estimation method to estimate both
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Travel times in urban road networks are highly stochastic. However, most existing travel time estimation methods only estimate the mean travel times, while ignoring travel time variances. To this end, this paper proposes a robust travel time distribution estimation method to estimate both the mean and variance of travel times by using emerging low-frequency floating car data. Different from the existing studies, the path travel time distribution in this study is formulated as the sum of the deterministic link travel times and stochastic turning delays at intersections. Using this formulation, distinct travel time delays for different turning movements at the same intersection can be well captured. In this study, a speed estimation algorithm is developed to estimate the deterministic link travel times, and a distribution estimation algorithm is proposed to estimate the stochastic turning delays. Considering the low sampling rate of the floating car data, a weighted moving average algorithm is further developed for a robust estimation of the path travel time distribution. A real-world case study in Wuhan, China is carried out to validate the applicability of the proposed method. The results of the case study show that the proposed method can obtain a reliable and accurate estimation of path travel time distribution in congested urban road networks. Full article
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Open AccessArticle A Novel Approach for Publishing Linked Open Geodata from National Registries with the Use of Semantically Annotated Context Dependent Web Pages
ISPRS Int. J. Geo-Inf. 2017, 6(8), 252; https://doi.org/10.3390/ijgi6080252
Received: 31 May 2017 / Revised: 6 August 2017 / Accepted: 10 August 2017 / Published: 15 August 2017
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Abstract
Many of the standards used to build spatial data infrastructure (SDI), such as Web Map Service (WMS) or Web Feature Service (WFS), have become outdated. They do not follow current web technology development and do not fully exploit its capabilities. Spatial data often
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Many of the standards used to build spatial data infrastructure (SDI), such as Web Map Service (WMS) or Web Feature Service (WFS), have become outdated. They do not follow current web technology development and do not fully exploit its capabilities. Spatial data often remains available only through application programming interfaces (APIs), reflecting the persistence of organizational silos. The potential of the web for discovering knowledge hidden in data and discoverable through integration and fusion remains very difficult. This article presents a strategy to take advantage of these newer semantic web technologies for SDI. We describe the implementation of a public registry in the age of Web 3.0. Our goal is to convert existing geographic information systems (GIS) data into explicit knowledge that can be easily used for a variety of purposes. This turns SDI into a framework to utilize the many advantages of the web. In this paper we present the working prototype system developed for the province of Mazowieckie in Poland and describes the underlying concepts. Further development of this approach comes from using linked data (LD) with expert systems to support analysis functions and tasks. Full article
(This article belongs to the Special Issue Web/Cloud Based Mapping and Geoinformation)
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Open AccessArticle Event-Driven Distributed Information Resource-Focusing Service for Emergency Response in Smart City with Cyber-Physical Infrastructures
ISPRS Int. J. Geo-Inf. 2017, 6(8), 251; https://doi.org/10.3390/ijgi6080251
Received: 24 May 2017 / Revised: 17 July 2017 / Accepted: 3 August 2017 / Published: 15 August 2017
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Abstract
The smart city has become a popular topic of investigation. How to focus large amounts of distributed information resources to efficiently cope with public emergencies and provide support for personalized decision-making is a vitally important issue in the construction of smart cities. In
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The smart city has become a popular topic of investigation. How to focus large amounts of distributed information resources to efficiently cope with public emergencies and provide support for personalized decision-making is a vitally important issue in the construction of smart cities. In this paper, an event-driven focusing service (EDFS) method that uses cyber-physical infrastructures for emergency response in smart cities is proposed. The method consists of a focusing service model at the top level, an informational representation of the model and a focusing service process to operate the service model in emergency response. The focusing service method follows an event-driven mechanism that allows the focusing service process to be triggered by public emergencies sensed by wireless sensor networks (WSNs) and mobile crowd sensing, and it integrates the requirements of different societal entities with regard to response to emergencies and information resources, thereby providing comprehensive and personalized support for decision-making. Furthermore, an EDFS prototype system is designed and implemented based on the proposed method. An experiment using a real-world scenario—the gas leakage in August 2014 in Taiyuan, China—is presented demonstrating the feasibility of the proposed method for assisting various societal entities in coping with and efficiently responding to public emergencies. Full article
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Open AccessArticle Template Matching and Simplification Method for Building Features Based on Shape Cognition
ISPRS Int. J. Geo-Inf. 2017, 6(8), 250; https://doi.org/10.3390/ijgi6080250
Received: 13 June 2017 / Revised: 19 July 2017 / Accepted: 10 August 2017 / Published: 15 August 2017
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Abstract
This study proposes a template matching simplification method from the perspective of shape cognition based on the typical template characteristics of building distributions and representations. The method first formulates a series of templates to abstract the building shape by generalizing their polygons and
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This study proposes a template matching simplification method from the perspective of shape cognition based on the typical template characteristics of building distributions and representations. The method first formulates a series of templates to abstract the building shape by generalizing their polygons and analyzing their symbolic meanings, then conducts the simplification by searching and matching the most similar template that can be used later to replace the original building. On the premise of satisfying the individual geometric accuracy on a smaller scale, the proposed method can enhance the impression of well-known landmarks and reflect the pattern in mapping areas by the symbolic template. The turning function that describes shape by measuring the changes of the tangent-angle as a function of the arc-length is employed to obtain the similar distance between buildings and template polygons, and the least squares model is used to control the geometry matching of the candidate template. Experiments on real datasets are carried out to assess the usefulness of this method and compare it with two existing methods. The experiments suggest that our method can preserve the main structure of building shapes and geometric accuracy. Full article
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Open AccessArticle Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014
ISPRS Int. J. Geo-Inf. 2017, 6(8), 249; https://doi.org/10.3390/ijgi6080249
Received: 22 June 2017 / Revised: 28 July 2017 / Accepted: 10 August 2017 / Published: 14 August 2017
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Abstract
Many factors impact a city’s water consumption, including population distribution, average household income, water prices, water conservation programs, and climate. Of these, however, meteorological effects are considered to be the primary determinants of water consumption. In this study, the effects of climate on
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Many factors impact a city’s water consumption, including population distribution, average household income, water prices, water conservation programs, and climate. Of these, however, meteorological effects are considered to be the primary determinants of water consumption. In this study, the effects of climate on residential water consumption in Las Vegas, Nevada, were examined during the period from 1990 to 2014. The investigations found that climatic variables, including maximum temperature, minimum temperature, average temperature, precipitation, diurnal temperature, dew point depression, wind speed, wind direction, and percent of calm wind influenced water use. The multivariate autoregressive integrated moving average (ARIMAX) model found that the historical data of water consumption and dew point depression explain the highest percentage of variance (98.88%) in water use when dew point depression is used as an explanatory variable. Our results indicate that the ARIMAX model with dew point depression input, and average temperature, play a significant role in predicting long-term water consumption rates in Las Vegas. The sensitivity analysis results also show that the changes in average temperature impacted water demand three times more than dew point depression. The accuracy performance, specifically the mean average percentage error (MAPE), of the model’s forecasting is found to be about 2–3% from five years out. This study can be adapted and utilized for the long-term forecasting of water demand in other regions. By using one significant climate factor and historical water demand for the forecasting, the ARIMAX model gives a forecast with high accuracy and provides an effective technique for monitoring the effects of climate change on water demand in the area. Full article
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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Open AccessArticle A Generalized Additive Model Combining Principal Component Analysis for PM2.5 Concentration Estimation
ISPRS Int. J. Geo-Inf. 2017, 6(8), 248; https://doi.org/10.3390/ijgi6080248
Received: 25 June 2017 / Revised: 3 August 2017 / Accepted: 10 August 2017 / Published: 13 August 2017
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As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM2.5 concentrations and the factors impacting it. However, these studies did not consider the loss
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As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA–GAM) was proposed to estimate PM2.5 concentrations in this study. The reliability of PCA–GAM for estimating PM2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA–GAM outperforms traditional LUR modelling with relatively higher adjusted R2 (0.94) and lower RMSE (4.08 µg/m3). The CV-adjusted R2 (0.92) is high and close to the model-adjusted R2, proving the robustness of the PCA–GAM model. The PCA–GAM model enhances PM2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA–GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution. Full article
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Open AccessArticle GIS-Based Visibility Network and Defensibility Model to Reconstruct Defensive System of the Han Dynasty in Central Xinjiang, China
ISPRS Int. J. Geo-Inf. 2017, 6(8), 247; https://doi.org/10.3390/ijgi6080247
Received: 25 June 2017 / Revised: 31 July 2017 / Accepted: 10 August 2017 / Published: 13 August 2017
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Abstract
The Silk Road opened during the Han Dynasty, and is significant in global cultural communication. Along this route in the central part of Xinjiang, the archaeological sites with defensive characteristics once provided a safeguard for this area. Reconstructing the defensive system is an
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The Silk Road opened during the Han Dynasty, and is significant in global cultural communication. Along this route in the central part of Xinjiang, the archaeological sites with defensive characteristics once provided a safeguard for this area. Reconstructing the defensive system is an important way to explore the ancient culture’s propagation and the organizational structure of these sites. In this research, the compound visibility network with complex network analysis (CNA) and the least-cost paths based on the defensibility models from linear and logistic regression methods constitute the principle defensive structure. As possible transportation corridors, these paths are considered to be mostly fitted to each other in general, and are different from normal slope-based paths. The sites Kuhne Shahr and Agra play important roles for information control according to the CNA measures, while the sites Kuhne Shahr and Kuyux Shahr are considered to be crucial cities due to their positions and structural shapes. Some other sites, including Uzgen Bulak, Shah Kalandar, Chuck Castle, Caladar, and Qiuci, as well as some beacons, have important effects on defending the transportation corridors. This method is proven efficient for the study of the historical role of archaeological sites with defensive characteristics. Full article
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Open AccessArticle Spatial Variation Relationship between Floating Population and Residential Burglary: A Case Study from ZG, China
ISPRS Int. J. Geo-Inf. 2017, 6(8), 246; https://doi.org/10.3390/ijgi6080246
Received: 5 July 2017 / Revised: 3 August 2017 / Accepted: 3 August 2017 / Published: 12 August 2017
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Abstract
With the rapid development of China’s economy, the demand for labor in the coastal cities continues to grow. Due to restrictions imposed by China’s household registration system, a large number of floating populations have subsequently appeared. The relationship between floating populations and crime,
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With the rapid development of China’s economy, the demand for labor in the coastal cities continues to grow. Due to restrictions imposed by China’s household registration system, a large number of floating populations have subsequently appeared. The relationship between floating populations and crime, however, is not well understood. This paper investigates the impact of a floating population on residential burglary on a fine spatial scale. The floating population was divided into the floating population from other provinces (FPFOP) and the floating population from the same province as ZG city (FPFSP), because of the high heterogeneity. Univariate spatial patterns in residential burglary and the floating population in ZG were explored using Moran’s I and LISA (local indicators of spatial association) models. Furthermore, a geographically weighted Poisson regression model, which addressed the spatial effects in the data, was employed to explore the relationship between the floating population and residential burglary. The results revealed that the impact of the floating population on residential burglary is complex. The floating population from the same province did not have a significant impact on residential burglary in most parts of the city, while the floating population from other provinces had a significantly positive impact on residential burglary in most of the study areas and the magnitude of this impact varied across the study area. Full article
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Open AccessArticle High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field
ISPRS Int. J. Geo-Inf. 2017, 6(8), 245; https://doi.org/10.3390/ijgi6080245
Received: 5 May 2017 / Revised: 29 July 2017 / Accepted: 2 August 2017 / Published: 10 August 2017
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Abstract
As an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classifying
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As an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classifying high-resolution remote sensing data. Specifically, the presented method proceeds in three main stages: feature extraction, classification, and classified result refinement. In the feature extraction stage, both multispectral images and 3D geometry data are used, which utilizes the complementary information from multisource data. In the classification stage, to tackle the problems associated with too many training samples and take full advantage of the information in the large-scale dataset, a random forest (RF) ensemble learning strategy is proposed by combining several RF classifiers together. Finally, an improved fully connected conditional random field (FCCRF) graph model is employed to derive the contextual information to refine the classification results. Experiments on the ISPRS Semantic Labeling Contest dataset show that the presented 3-stage method achieves 86.9% overall accuracy, which is a new state-of-the-art non-CNN (convolutional neural networks)-based classification method. Full article
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Open AccessArticle Wicked Water Points: The Quest for an Error Free National Water Point Database
ISPRS Int. J. Geo-Inf. 2017, 6(8), 244; https://doi.org/10.3390/ijgi6080244
Received: 5 June 2017 / Revised: 2 August 2017 / Accepted: 3 August 2017 / Published: 8 August 2017
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Abstract
The Water Sector Development Programme (WSDP) of Tanzania aims to improve the performance of the water sector in general and rural water supply (RWS) in particular. During the first phase of the WSDP (2007 to 2014), implementing agencies developed information systems for attaining
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The Water Sector Development Programme (WSDP) of Tanzania aims to improve the performance of the water sector in general and rural water supply (RWS) in particular. During the first phase of the WSDP (2007 to 2014), implementing agencies developed information systems for attaining management efficiencies. One of these systems, the Water Point Mapping System (WPMS), has now been completed, and the database is openly available to the public, as part of the country’s commitment to the Open Government Partnership (OGP) initiative. The Tanzanian WPMS project was the first attempt to map “wall-to-wall” all rural public water points in an African nation. The complexity of the endeavor led to suboptimal results in the quality of the WPMS database, the baseline of the WPMS. The WPMS database was a means for the future monitoring of all rural water points, but its construction has become an end in itself. We trace the challenges of water point mapping in Tanzania and describe how the WPMS database was initially populated and to what effect. The paper conceptualizes errors found in the WPMS database as material, observational, conceptual and discursive, and characterizes them in terms of type, suspected origin and mitigation options. The discussion focuses on the consequences of open data scrutiny for the integrity of the WPMS database and the implications for monitoring wicked water point data. Full article
(This article belongs to the Special Issue Innovative Geo-Information Tools for Governance)
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