ISPRS Int. J. Geo-Inf., Volume 7, Issue 10 (October 2018) – 32 articles

Multi-dimensional representation of urban settings has received a great deal of attention among urban planners, policy makers, and urban scholars. This is due to the fact that cities grow vertically and new urbanism strategies encourage higher density and compact city development. Advancements in computer technology and multi-dimensional geospatial data integration, analysis and visualisation play a pivotal role in supporting urban planning and design. However, due to the complexity of the models and technical requirements of the multi-dimensional city models, planners are yet to fully exploit such technologies in their activities. This paper proposes a workflow to support non-experts in using three-dimensional city modelling tools to carry out planning control amendments and assess their implications. The paper focuses on using a parametric three-dimensional (3D) city model to enable planners to measure the physical (e.g., building height, shadow, setback) and functional (e.g., mix of land uses) impacts of new planning controls. The workflow is then implemented in an inner suburb of Metropolitan Melbourne, where urban intensification strategies require the planners to carry out radical changes in regulations. This study demonstrates the power of the proposed 3D visualisation tool for urban planners at taking two-dimensional (2D) Geographic Information System (GIS) procedural modelling to construct a 3D model. Full article

Known to be structured in several patterns at the same time, the prior image of interest is always modeled with the idea of enforcing multiple constraints on unknown signals. For instance, when dealing with a hyperspectral restoration problem, the combination of constraints with piece-wise smoothness and low rank has yielded promising reconstruction results. In this paper, we propose a novel mixed-noise removal method by employing 3D anisotropic total variation and low rank constraints simultaneously for the problem of hyperspectral image (HSI) restoration. The main idea of the proposed method is based on the assumption that the spectra in an HSI lies in the same low rank subspace and both spatial and spectral domains exhibit the property of piecewise smoothness. The low rankness of an HSI is approximately exploited by the nuclear norm, while the spectral-spatial smoothness is explored using 3D anisotropic total variation (3DATV), which is defined as a combination of 2D spatial TV and 1D spectral TV of the HSI cube. Finally, the proposed restoration model is effectively solved by the alternating direction method of multipliers (ADMM). Experimental results of both simulated and real HSI datasets validate the superior performance of the proposed method in terms of quantitative assessment and visual quality. Full article

As wetlands are one of the world’s most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, their classification depends on many different parameters. However, considering their complex structure; wetlands tend to be challenging land cover for classification, which sometimes requires the use of multi-sensor remote sensing techniques. The objectives of this study were: (i) to investigate the monthly dynamics of several wetland classes using multi-sensor parameters; (ii) to find correlations between the investigated parameters. Thus, we extracted the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8, and extracted dual polarization backscatter values (VH-VV) from the Sentinel-1 satellite at a monthly period over a year. The results showed strong correlation between the LST and the NDVI values of 0.94, and strong correlation between the microwave (VH) and both thermal and optical parameters with a 0.81 correlation coefficient, while there was weak or no correlation between the VV and the other investigated parameters. We strongly recommend that future studies clarify the Sentinel-1 backscatter values in wetland areas, by taking multiple field measurements close to the image acquisition time. Full article

Buildings represent the most relevant features of human activity in urban regions, but their change detection using very-high-resolution (VHR) remote sensing imagery is still a major challenge. Effective representation of the building is the key point in building change detection. The linear feature can indirectly represent the structure and distribution of man-made objects. Thus, this study proposes a shape feature-based building change detection method. Specifically, a line-constrained shape (LCS) feature is developed to capture the shape characteristics of buildings. This feature improves the discriminability between buildings and other ground objects by integrating the pixel shape feature and line segments. The building candidate area (BCA) is created in accordance with the distribution of the line segments in two-phase images. The problem space is constrained in a high-likelihood region of buildings because of the BCA. Comparative experimental results demonstrate that the combination of the spectral feature and the developed LCS feature achieves the best performance in object-based building change detection in VHR imagery. Full article

The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance. Full article

This research aims to investigate how different landscape metrics are affected by the enhancement of the thematic classes in land cover/land use (LC/LU) maps. For this aim, three different LC/LU maps based on three different levels of CORINE (Coordination of Information on The Environment) nomenclature were created for the selected study area using GEOBIA (Geographic Object Based Image Analysis) techniques. First, second and third level LC/LU maps of the study area have five, thirteen and twenty-seven hierarchical thematic classes, respectively. High-resolution Spot 7 images with 1.5 m spatial resolution were used as the main Earth Observation data to create LC/LU maps. Additional geospatial data from open sources (OpenStreetMap and Wikimapia) were also integrated to the classification in order to identify some of the 2nd and 3rd level LC/LU classes. Classification procedure was initially conducted for Level 3 classes in which we developed decision trees to be used in object-based classification. Afterwards, Level 3 classes were merged to create Level 2 LC/LU map and then Level 2 classes were merged to create the Level 1 LC/LU map according to CORINE nomenclature. The accuracy of Level 1, Level 2, Level 3 maps are calculated as; 93.50%, 89.00%, 85.50% respectively. At the last stage, several landscape metrics such as Number of Patch (NP), Edge Density (ED), Largest Patch Index (LPI), Euclidean Nearest Neighbor Distance (ENN), Splitting Index (SPLIT) and Aggregation Index (AI) metrics and others were calculated for different level LC/LU maps and landscape metrics values were compared to analyze the impact of changing thematic details on landscape metrics. Our results show that, increasing the thematic detail allows landscape characteristics to be defined more precisely and ensure comprehensive assessment of cause and effect relationships between classes. Full article

Vegetation plays an irreplaceable role for urban ecosystem services. Urban greenness represents all vegetation cover in and around cities. Understanding spatiotemporal patterns of the changes in urban greenness (CUG) provides fundamental clues for urban planning. The impact on CUG can be roughly categorized as being climate-induced and human-induced. Methods for mapping human-induced CUG (H-CUG) are rare. In this paper, a new framework, known as Localized Spatial Association Analysis under Temporal Context (LSAA-TC), was proposed to explore H-CUG. Localized spatial association analysis (LSAA) was performed first to extract local spatial outliers (LSOs), or locations that differ significantly in urban greenness from those located in the neighborhood. LSOs were then analyzed under the temporal context to map their intertemporal variations known as spatiotemporal outliers. We applied LSAA-TC to mapping H-CUG in the Wuhan Metropolitan Area, China during 2000–2015 using the vegetation index from Moderate-resolution Imaging Spectroradiometer (MODIS) 13Q1 as the proxy for urban greenness. The computed H-CUG demonstrated apparent spatiotemporal patterns. The result is consistent with the fact that the traditional downtown area presents the lowest H-CUG, while it is found that the peripheral area in the circular belt within 14–20 km from the urban center demonstrates the most significant H-CUG. We conclude that LSAA-TC can be a widely applicable framework to understand H-CUG patterns and is a promising tool for informative urban planning. Full article

Crowd-sourced geographic information is becoming increasingly available, providing diverse and timely sources for updating existing spatial databases to facilitate urban studies, geoinformatics, and real estate practices. However, the discrepancies between heterogeneous datasets present challenges for automated change detection. In this paper, we identify important measurable factors to account for issues like boundary mismatch, large offset, and discrepancies in the levels of detail between the more current and to-be-updated datasets. These factors are organized into rule sets that include data matching, merge of the many-to-many correspondence, controlled displacement, shape similarity, morphology of difference parts, and the building pattern constraint. We tested our approach against OpenStreetMap and a Dutch topographic dataset (TOP10NL). By removing or adding some components, the results show that our approach (accuracy = 0.90) significantly outperformed a basic geometric method (0.77), commonly used in previous studies, implying a more reliable change detection in realistic update scenarios. We further found that distinguishing between small and large buildings was a useful heuristic in creating the rules. Full article

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase. Full article

Automatic composition of geospatial web services increases the possibility of taking full advantage of spatial data and processing capabilities that have been published over the internet. In this paper, a multi-agent artificial intelligence (AI) planning solution was proposed, which works within the geoportal architecture and enables the geoportal to compose semantically annotated Open Geospatial Consortium (OGC) Web Services based on users’ requirements. In this solution, the registered Catalogue Service for Web (CSW) services in the geoportal along with a composition coordinator component interact together to synthesize Open Geospatial Consortium Web Services (OWSs) and generate the composition workflow. A prototype geoportal was developed, a case study of evacuation sheltering was implemented to illustrate the functionality of the algorithm, and a simulation environment, including one hundred simulated OWSs and five CSW services, was used to test the performance of the solution in a more complex circumstance. The prototype geoportal was able to generate the composite web service, based on the requested goals of the user. Additionally, in the simulation environment, while the execution time of the composition with two CSW service nodes was 20 s, the addition of new CSW nodes reduced the composition time exponentially, so that with five CSW nodes the execution time reduced to 0.3 s. Results showed that due to the utilization of the computational power of CSW services, the solution was fast, horizontally scalable, and less vulnerable to the exponential growth in the search space of the AI planning problem. Full article

This paper introduces an urban growth simulation model applied to the full scope of China. The model uses a multicriteria decision analysis to calculate the land conversion probability and then integrates it with a cellular automata model. A nonlinear relationship is incorporated in to the model to interpret the impacts of different Land Use and Cover Change driving forces. The Analytical Hierarchical Process is also implemented to compute the variance between weights of different factors. Multiple sizes of neighborhood and different urban ratios in the model rules are tested, and a 5 × 5 neighborhood and an urban threshold of 0.33 are chosen. The study demonstrates the importance of spatial analysis on socioeconomic factors, population, and Gross Domestic Product in land use change simulation modeling. The model fills the gap between the purely economic theory simulation model and the geographic simulation model. The nationwide urban simulation is an example that addresses the lack of urban simulation studies in China and among large-scale simulation models. Full article

Extracting the split line of narrow and long patches is important for the generalization of land-use thematic data. There are two commonly used methods for extracting the split lines: One is based on Delaunay triangulation and the other is based on straight skeletons. However, it is difficult for the straight skeleton method to preserve geometric structure and topological consistency with the original data when dealing with polygons that have irregularity and complexity of junctions. Therefore, we propose an improved jitter elimination and topology correction method for split lines based on a constrained Delaunay triangulation. First, a split line adjustment algorithm based on the geometric structure of the polygon is proposed to eliminate the jitters. Second, a split line topology correction algorithm is proposed for nodes with degree 1 or degree 2, considering the boundary topological constraint. The reliability of the proposed method is verified by comparing it with the straight skeleton method using sample data and the superiority of the proposed method is verified by using actual data from China’s geographical conditions census in the Guizhou province. Full article

In order to overcome the insufficiency of single remote sensing data in change detection, synthetic aperture radar (SAR) and optical image data can be used together for supplementation. However, conventional image fusion methods fail to address the differences in imaging mechanisms and cannot overcome some practical limitations such as usage in change detection or temporal requirement of the optical image. This study proposes a new method to fuse SAR and optical images, which is expected to be visually helpful and minimize the differences between two imaging mechanisms. The algorithm performs the fusion by establishing relationships between SAR and multispectral (MS) images by using a random forest (RF) regression, which creates a fused SAR image containing the surface roughness characteristics of the SAR image and the spectral characteristics of the MS image. The fused SAR image is evaluated by comparing it to those obtained using conventional image fusion methods and the proposed method shows that the spectral qualities and spatial qualities are improved significantly. Furthermore, for verification, other ensemble approaches such as stochastic gradient boosting regression and adaptive boosting regression are compared and overall it is confirmed that the performance of RF regression is superior. Then, change detection between the fused SAR and MS images is performed and compared with the results of change detection between MS images and between SAR images and the result using fused SAR images is similar to the result with MS images and is improved when compared to the result between SAR images. Lastly, the proposed method is confirmed to be applicable to change detection. Full article

The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and quality of OSM data, but many of them have constraints on evaluating the profiles of contributors. In this paper, OSM road data have been analyzed with the aim of characterizing the behavior of OSM contributors. The study area, Ankara, the capital city of Turkey, was evaluated with several network analysis methods, such as completeness, degree of centrality, betweenness, closeness, PageRank, and a proposed method measuring the activation of contributors in a bounded area from 2007–2017. An evaluation of the results was also discussed in this paper by taking into account the following indicators for each year: number of nodes, ways, contributors, mean lengths, and sinuosity values of roads. The results show that the experience levels of the contributors determine the contribution type. Essentially, more experience makes for more detailed contributions. Full article

With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of spatial data make the handling of geospatial big data even more complicated. To facilitate users implementing geospatial big data applications in a MapReduce framework, several big data processing systems have extended the original Hadoop to support spatial properties. Most of those platforms, however, have included spatial functionalities by embedding them as a form of plug-in. Although offering a convenient way to add new features to an existing system, the plug-in has several limitations. In particular, while executing spatial and nonspatial operations by alternating between the existing system and the plug-in, additional read and write overheads have to be added to the workflow, significantly reducing performance efficiency. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. This paper explains the overall architecture and data model of Marmot as well as the main algorithm for automatic construction of MapReduce jobs from a given spatial analysis task. To illustrate how Marmot transforms a sequence of operators for spatial analysis to map and reduce functions in a way to achieve better performance, this paper presents an example of spatial analysis retrieving the number of subway stations per city in Korea. This paper also experimentally demonstrates that Marmot generally outperforms SpatialHadoop, one of the top plug-in based spatial big data frameworks, particularly in dealing with complex and time-intensive queries involving spatial index. Full article

Warming, i.e., increments of temperature, is evident at the global, regional, and local level. However, understanding the dynamics of local warming at high spatial resolution remains challenging. In fact, it is very common to see extremely variable land cover/land use within built-up environments that create micro-climatic conditions. To address this issue, our overall goal was to generate a local warming map for the period 1961–2010 at 15 m spatial resolution over the southern part of the Canadian province of Alberta. Our proposed methods consisted of three distinct steps. These were the: (i) construction of high spatial resolution enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) maps; (ii) conversion of air temperature (T_a) normal (i.e., 30 years average) at higher spatial resolution using vegetation indices (VI); and (iii) generation of a local warming map at 15m spatial resolution. In order to execute this study, we employed MODIS-driven air temperature data, EVI and NDVI data, and Landsat-driven vegetation indices. The study uncovered that around 58% (up to positive 1 °C) of areas in the considered study region were experiencing increased temperature; whereas only about 4% of areas underwent a cooling trend (more than negative 0.25 °C). The remaining 38% did not exhibit significant change in temperature. We concluded that remote sensing technology could be useful to enhance the spatial resolution of local warming maps, which would be useful for decision-makers considering efficient decisions in the face of increments in local temperature. Full article

Here we present “CO-RIP”, a novel spatial dataset delineating riparian corridors and riparian vegetation along large streams and rivers in the United States (US) portion of the Colorado River Basin. The consistent delineation of riparian areas across large areas using remote sensing has been a historically complicated process partially due to differing definitions in the scientific and management communities regarding what a “riparian corridor” or “riparian vegetation” represents. We use valley-bottoms to define the riparian corridor and establish a riparian vegetation definition interpretable from aerial imagery for efficient, consistent, and broad-scale mapping. Riparian vegetation presence and absence data were collected using a systematic, flexible image interpretation process applicable wherever high resolution imagery is available. We implemented a two-step approach using existing valley bottom delineation methods and random forests classification models that integrate Landsat spectral information to delineate riparian corridors and vegetation across the 12 ecoregions of the Colorado River Basin. Riparian vegetation model accuracy was generally strong (median kappa of 0.80), however it varied across ecoregions (kappa range of 0.42–0.90). We offer suggestions for improvement in our current image interpretation and modelling frameworks, particularly encouraging additional research in mapping riparian vegetation in moist coniferous forest and deep canyon environments. The CO-RIP dataset created through this research is publicly available and can be utilized in a wide range of ecological applications. Full article

Urban cycling is a sustainable transport mode that many cities are promoting. However, few cities are taking advantage of geospatial technologies to represent and analyse cycling mobility based on the behavioural patterns and difficulties faced by cyclists. This study analyses a geospatial dataset crowdsourced by urban cyclists using an experimental, mobile geo-game. Fifty-seven participants recorded bicycle trips during one week periods in three cities. By aggregating them, we extracted not only the cyclists’ preferred streets but also the frictions faced during cycling. We successfully identified 284 places potentially having frictions: 71 in Münster, Germany; 70 in Castelló, Spain; and 143 in Valletta, Malta. At such places, participants recorded bicycle segments at lower speeds indicating a deviation from an ideal cycling scenario. We describe the potential frictions inhibiting bicycle commuting with regard to the distance to bicycle paths, surrounding infrastructure, and location in the urban area. Full article

The Canadian Hydrographic Service (CHS) supports safe navigation within Canadian waters through approximately 1000 navigational charts as well as hundreds of publications. One of the greatest challenges faced by the CHS is removing gaps in bathymetric survey data, particularly in the Canadian Arctic where only 6% of navigational water is surveyed to modern standards. Therefore, the CHS has initiated a research project to explore remote sensing methods to improve Canadian navigational charts. The major components of this project explore satellite derived bathymetry (SDB), coastline change detection and coastline extraction. This paper focuses on the potential of two stereo satellite techniques for deriving SDB: (i) automatic digital elevation model (DEM) extraction using a semi-global matching method, and (ii) 3D manual delineation of depth contours using visual stereoscopic interpretation. Analysis focused on quantitative assessment which compared estimated depths from both automatic and 3D manual photogrammetric approaches against available in situ survey depths. The results indicate that the 3D manual approach provides an accuracy of <2 m up to a depth of 15 m. Comparable results were obtained from the automatic approach to a depth of 12 m. For almost all investigated depth ranges for both techniques, uncertainties were found to be within the required vertical accuracies for the International Hydrographic Organization category zone of confidence (CATZOC) level C classification for hydrographic surveys. This indicates that both techniques can be used to derive navigational quality bathymetric information within the investigated study site. While encouraging, neither technique was found to offer a single solution for the complete estimation of depth within the study area. As a result of these findings, the CHS envisions a hybrid approach where stereo- and reflectance-based bathymetry estimation techniques are implemented to provide the greatest understanding of depth possible from satellite imagery. Overall, stereo photogrammetry techniques will likely allow for new potential for supporting the improvement of CHS charts in areas where modern surveys have not yet been obtained. Full article

Landslide identification is an increasingly important research topic in remote sensing and the study of natural hazards. It is essential for hazard prevention, mitigation, and vulnerability assessments. Despite great efforts over the past few years, its accuracy and efficiency can be further improved. Thus, this study combines the two most popular approaches: susceptibility analysis and change detection thresholding, to derive a landslide identification method employing novel identification criteria. Through a quantitative evaluation of the proposed method and masked change detection thresholding method, the proposed method exhibits improved accuracy to some extent. Our susceptibility-based change detection thresholding method has the following benefits: (1) it is a semi-automatic landslide identification method that effectively integrates a pixel-based approach with an object-oriented image analysis approach to achieve more precise landslide identification; (2) integration of the change detection result with the susceptibility analysis result represents a novel approach in the landslide identification research field. Full article

The 3D Tiles specification, created by Cesium, is designed for streaming massive heterogeneous three-dimensional (3D) geospatial datasets online using WebGL technology. The program has prevailed in the WebGIS community due to its ability to visualise, interact, and style 3D objects for various scenarios, such as 3D cities, indoor environments, and point clouds. It offers a new opportunity to integrate Building Information Models (BIM) in the Industry Foundation Classes (IFC) data format with existing geospatial data in a 3D WebGIS platform with open-source implementation. As no open-source solution for converting IFC models into 3D Tiles for online visualization had yet been found, this paper explores feasible approaches and integrates a range of tools and libraries as an open-source solution for the community. Full article

Timely and accurate identification of change detection for areas depicted on nautical charts constitutes a key task for marine cartographic agencies in supporting maritime safety. Such a task is usually achieved through manual or semi-automated processes, based on best practices developed over the years requiring a substantial level of human commitment (i.e., to visually compare the chart with the new collected data or to analyze the result of intermediate products). This work describes an algorithm that aims to largely automate the change identification process as well as to reduce its subjective component. Through the selective derivation of a set of depth points from a nautical chart, a triangulated irregular network is created to apply a preliminary tilted-triangle test to all the input survey soundings. Given the complexity of a modern nautical chart, a set of feature-specific, point-in-polygon tests are then performed. As output, the algorithm provides danger-to-navigation candidates, chart discrepancies, and a subset of features that requires human evaluation. The algorithm has been successfully tested with real-world electronic navigational charts and survey datasets. In parallel to the research development, a prototype application implementing the algorithm was created and made publicly available. Full article

Vectors are a key type of geospatial data, and their discretization, which involves solving the problem of generating a discrete line, is particularly important. In this study, we propose a method for constructing a discrete line mathematical model for a triangular grid based on a “weak duality” hexagonal grid, to overcome the drawbacks of existing discrete line generation algorithms for a triangular grid. First, a weak duality relationship between triangular and hexagonal grids is explored. Second, an equivalent triangular grid model is established based on the hexagonal grid, using this weak duality relationship. Third, the two-dimensional discrete line model is solved by transforming it into a one-dimensional optimal wandering path model. Finally, we design and implement the dimensionality reduction generation algorithm for a discrete line in a triangular grid. The results of our comparative experiment indicate that the proposed algorithm has a computation speed that is approximately 10 times that of similar existing algorithms; in addition, it has better fitting effectiveness. Our proposed algorithm has broad applications, and it can be used for real-time grid transformation of vector data, discrete global grid system (DGGS), and other similar applications. Full article

Multiple land use and land cover (LUC) datasets are available for the analysis of LUC changes (LUCC) in distinct territories. Sometimes, different LUCC results are produced to characterize these changes for the same territory and the same period. These differences reflect: (1) The different properties of LUC geoinformation (GI) used in the LUCC assessment, and (2) different criteria used for vector-to-raster conversion, namely, those deriving from outputs with different spatial resolutions. In this research, we analyze LUCC in mainland Portugal using two LUC datasets with different properties: Corine Land Cover (CLC 2006 and 2012) and LUC official maps of Portugal (Carta de Ocupação do Solo, COS 2007 and 2010) provided by the European Environment Agency (EEA) and the General Directorate for Territorial Development (DGT). Each LUC dataset has undergone vector-to-raster conversion, with different resolutions (10, 25, 50, 100, and 200 m). LUCC were analyzed based on the vector GI of each LUC dataset, and with LUC raster outputs using different resolutions. Initially, it was observed that the areas with different LUC types in two LUC datasets in vector format were not similar—a fact explained by the different properties of this type of GI. When using raster GI to perform the analysis of LUCC, it was observed that at high resolutions, the results are identical to the results obtained when using vector GI, but this ratio decreases with increased cell size. In the analysis of LUCC results obtained with raster LUC GI, the outputs with pixel size greater than 100 m do not follow the same trend of LUCC obtained with high raster resolutions or using LUCC obtained with vector GI. These results point out the importance of the factor form and the area of the polygons, and different effects of amalgamation and dilation in the vector-to-raster conversion process, more evident at low resolutions. These findings are important for future evaluations of LUCC that integrate raster GI and vector/raster conversions, because the different LUC GI resolution in line with accuracy can explain the different results obtained in the evaluation of LUCC. The present work demonstrates this fact, i.e., the effects of vector-to-raster conversions using various resolutions culminated in different results of LUCC. Full article

In this paper, we present the optical image simulation from synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SAR-optical data, are proposed to testify the possibilities. The deep learning based methods that we chose to achieve the models are a convolutional neural network (CNN) with a residual architecture and a conditional generative adversarial network (cGAN). We validate our models using the Sentinel-1 and -2 datasets. The experiments demonstrate that the model with multi-temporal SAR-optical data can successfully simulate the optical image; meanwhile, the state-of-the-art model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SAR-optical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal super-resolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions. Full article

Neighborhoods are vaguely defined, localized regions that share similar characteristics. They are most often defined, delineated and named by the citizens that inhabit them rather than municipal government or commercial agencies. The names of these neighborhoods play an important role as a basis for community and sociodemographic identity, geographic communication and historical context. In this work, we take a data-driven approach to identifying neighborhood names based on the geospatial properties of user-contributed rental listings. Through a random forest ensemble learning model applied to a set of spatial statistics for all n-grams in listing descriptions, we show that neighborhood names can be uniquely identified within urban settings. We train a model based on data from Washington, DC, and test it on listings in Seattle, WA, and Montréal, QC. The results indicate that a model trained on housing data from one city can successfully identify neighborhood names in another. In addition, our approach identifies less common neighborhood names and suggestions of alternative or potentially new names in each city. These findings represent a first step in the process of urban neighborhood identification and delineation. Full article

Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005–2015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today’s urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas. Full article

Knowledge discovery about people and cities from emerging location data has been an active research field but is still relatively unexplored. In recent years, a considerable amount of work has been developed around the use of social media data, most of which focusses on mining the content, with comparatively less attention given to the location information. Furthermore, what aggregated scale spatial patterns show still needs extensive discussion. This paper proposes a tweet-topic-function-structure framework to reveal spatial patterns from individual tweets at aggregated spatial levels, combining an unsupervised learning algorithm with spatial measures. Two-year geo-tweets collected in Greater London were analyzed as a demonstrator of the framework and as a case study. The results indicate, at a disaggregated level, that the distribution of topics possess a fair degree of spatial randomness related to tweeting behavior. When aggregating tweets by zones, the areas with the same topics form spatial clusters but of entangled urban functions. Furthermore, hierarchical clustering generates a clear spatial structure with orders of centers. Our work demonstrates that although uncertainties exist, geo-tweets should still be a useful resource for informing spatial planning, especially for the strategic planning of economic clusters. Full article

The nature of contemporary spatial data infrastructures lies in the provision of geospatial information in an on-demand fashion. Although recent applications identified the need to react to real-time information in a time-critical way, research efforts in the field of geospatial Internet of Things in particular have identified substantial gaps in this context, ranging from a lack of standardisation for event-based architectures to the meaningful handling of real-time information as “events”. This manuscript presents work in the field of event-driven architectures as part of spatial data infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of spatial data infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches—developed in different research projects—to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall event-driven spatial data infrastructure. Full article

The massive property development of high-rises and complex structures above and below the ground surface in cities indicates the lack of land and high demand to use spaces. However, the existing land and property administration systems are mainly two dimensional and not capable of efficiently managing these complex spaces. As ownership rights on plans are recorded in paper or PDF, understanding these rights and making effective decisions and analyses can be difficult without having experience in the art of reading and interpreting plan information. This paper attempts to address these issues by presenting a prototype for visualizing three-dimensional land and property information. The aim of this prototype is to illustrate and communicate the requirements and benefits of a 3D digital cadastre platform. The prototype is a web-based application and includes functionality to display both legal and physical data, interact with 3D models, display administrative data, identify objects and search objects, visualize cross-sections, and undertake measurements in 3D. For this prototype, a multi-story building was selected as a case study and its 3D model was imported into the prototype to display ownership rights. The prototype was then evaluated by various stakeholders and their feedback was considered for future enhancement. Full article