ISPRS International Journal of Geo-Information

21 pages, 1467 KiB

Open AccessArticle

Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation

by Azelle Courtial, Achraf El Ayedi, Guillaume Touya and Xiang Zhang

ISPRS Int. J. Geo-Inf. 2020, 9(5), 338; https://doi.org/10.3390/ijgi9050338 - 25 May 2020

Cited by 47 | Viewed by 4548

Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. [...] Read more.

Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. This paper explores this potential on the popular mountain road generalisation problem, which requires smoothing the road, enlarging the bend summits, and schematising the bend series by removing some of the bends. We modelled the mountain road generalisation as a deep learning problem by generating an image from input vector road data, and tried to generate it as an output of the model a new image of the generalised roads. Similarly to previous studies on building generalisation, we used a U-Net architecture to generate the generalised image from the ungeneralised image. The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come. Full article

(This article belongs to the Special Issue Map Generalization)

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18 pages, 3108 KiB

Open AccessArticle

DKP: A Geographic Data and Knowledge Platform for Supporting Climate Service Design

by Martine Collard, Erick Stattner, Wilfried Segretier, Reynald Eugenie and Nathan Jadoul

ISPRS Int. J. Geo-Inf. 2020, 9(5), 337; https://doi.org/10.3390/ijgi9050337 - 22 May 2020

Viewed by 3055

Abstract

This article falls within the related areas of climate services and geographic information. We present the architecture and features of the Data and Knowledge Platform (DKP), innovative geographic software that was designed as support for climate-service elaboration in the context of change on [...] Read more.

This article falls within the related areas of climate services and geographic information. We present the architecture and features of the Data and Knowledge Platform (DKP), innovative geographic software that was designed as support for climate-service elaboration in the context of change on given geographic areas. It is intended for a community of stakeholders who need visual and geographic tools to design services improving the resilience of society regarding specific local issues. The platform provides different functions for seeking all available geographic information. Anticipating large volumes of data that are to be stored, we opted for a NoSQL database rather than a textual repository. In this paper, we present the different features of the platform and its ability to support visual climate service co-design, and we illustrate our statement with an example. Full article

(This article belongs to the Special Issue Geographic Information Extraction and Retrieval)

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15 pages, 7054 KiB

Open AccessArticle

Dynamic Floating Stations Model for Emergency Medical Services with a Consideration of Traffic Data

by Chih-Hong Sun, Chen-Yang Cheng, Cheng-Hui Wang and Po-Huan Hsiao

ISPRS Int. J. Geo-Inf. 2020, 9(5), 336; https://doi.org/10.3390/ijgi9050336 - 20 May 2020

Cited by 4 | Viewed by 3054

Abstract

To equally distribute the workload and minimize the travel distance for fire departments, we developed a new dynamic floating stations model (DFSM) to target traffic-related emergency medical services (EMS) during peak hours. This study revealed that traffic-related EMS incidents have different characteristics to [...] Read more.

To equally distribute the workload and minimize the travel distance for fire departments, we developed a new dynamic floating stations model (DFSM) to target traffic-related emergency medical services (EMS) during peak hours. This study revealed that traffic-related EMS incidents have different characteristics to other EMS incidents. The number of floating stations was determined by the number of available ambulances at a given time. The optimum floating station location was identified by using the given capacity to establish the smallest service radius. In DFSM simulations using floating stations with a capacity of 100 and 150 EMS incidents, the result shows significant improvements in comparison to the current situation. Full article

(This article belongs to the Special Issue Enhanced Modeling and Surveying Tools for Smart Cities)

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17 pages, 4136 KiB

Open AccessArticle

Modelling Offset Regions around Static and Mobile Locations on a Discrete Global Grid System: An IoT Case Study

by David Bowater and Monica Wachowicz

ISPRS Int. J. Geo-Inf. 2020, 9(5), 335; https://doi.org/10.3390/ijgi9050335 - 20 May 2020

Cited by 6 | Viewed by 3147

Abstract

With the huge volume of location-based point data being generated by Internet of Things (IoT) devices and subsequent rising interest from the Digital Earth community, a need has emerged for spatial operations that are compatible with Digital Earth frameworks, the foundation of which [...] Read more.

With the huge volume of location-based point data being generated by Internet of Things (IoT) devices and subsequent rising interest from the Digital Earth community, a need has emerged for spatial operations that are compatible with Digital Earth frameworks, the foundation of which are Discrete Global Grid Systems (DGGSs). Offsetting is a fundamental spatial operation that allows us to determine the region within a given distance of an IoT device location, which is important for visualizing or querying nearby location-based data. Thus, in this paper, we present methods of modelling an offset region around the point location of an IoT device (both static and mobile) that is quantized into a cell of a DGGS. Notably, these methods illustrate how the underlying indexing structure of a DGGS can be utilized to determine the cells in an offset region at different spatial resolutions. For a static IoT device location, we describe a single resolution approach as well as a multiresolution approach that allows us to efficiently determine the cells in an offset region at finer (or coarser) resolutions. For mobile IoT device locations, we describe methods to efficiently determine the cells in successive offset regions at fine and coarse resolutions. Lastly, we present a variety of results that demonstrate the effectiveness of the proposed methods. Full article

(This article belongs to the Special Issue Global Grid Systems)

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40 pages, 21218 KiB

Open AccessEditor’s ChoiceArticle

Automated Conflation of Digital Elevation Model with Reference Hydrographic Lines

by Timofey E. Samsonov

ISPRS Int. J. Geo-Inf. 2020, 9(5), 334; https://doi.org/10.3390/ijgi9050334 - 20 May 2020

Cited by 8 | Viewed by 6966

Abstract

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model [...] Read more.

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation. Full article

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14 pages, 6305 KiB

Open AccessArticle

Visualizing When, Where, and How Fires Happen in U.S. Parks and Protected Areas

by Nicole C. Inglis and Jelena Vukomanovic

ISPRS Int. J. Geo-Inf. 2020, 9(5), 333; https://doi.org/10.3390/ijgi9050333 - 20 May 2020

Cited by 5 | Viewed by 3374

Abstract

Fire management in protected areas faces mounting obstacles as climate change alters disturbance regimes, resources are diverted to fighting wildfires, and more people live along the boundaries of parks. Evidence-based prescribed fire management and improved communication with stakeholders is vital to reducing fire [...] Read more.

Fire management in protected areas faces mounting obstacles as climate change alters disturbance regimes, resources are diverted to fighting wildfires, and more people live along the boundaries of parks. Evidence-based prescribed fire management and improved communication with stakeholders is vital to reducing fire risk while maintaining public trust. Numerous national fire databases document when and where natural, prescribed, and human-caused fires have occurred on public lands in the United States. However, these databases are incongruous and non-standardized, making it difficult to visualize spatiotemporal patterns of fire and engage stakeholders in decision-making. We created interactive decision analytics (“VISTAFiRe”) that transform fire history data into clear visualizations of the spatial and temporal dimensions of fire and its management. We demonstrate the utility of our approach using Big Cypress National Preserve and Everglades National Park as examples of protected areas experiencing fire regime change between 1980 and 2017. Our open source visualizations may be applied to any data from the National Park Service Wildland Fire Events Geodatabase, with flexibility to communicate shifts in fire regimes over time, such as the type of ignition, duration and magnitude, and changes in seasonal occurrence. Application of the tool to Everglades and Big Cypress revealed that natural wildfires are occurring earlier in the wildfire season, while human-caused and prescribed wildfires are becoming less and more common, respectively. These new avenues of stakeholder communication are allowing the National Park Service to devise research plans to prepare for environmental change, guide resource allocation, and support decision-making in a clear and timely manner. Full article

(This article belongs to the Special Issue Geospatial Advances in Landscape Ecology)

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23 pages, 4715 KiB

Open AccessArticle

A GIS-based DRASTIC Model and an Adjusted DRASTIC Model (DRASTICA) for Groundwater Susceptibility Assessment along the China–Pakistan Economic Corridor (CPEC) Route

by Ahsen Maqsoom, Bilal Aslam, Umer Khalil, Omid Ghorbanzadeh, Hassan Ashraf, Rana Faisal Tufail, Danish Farooq and Thomas Blaschke

ISPRS Int. J. Geo-Inf. 2020, 9(5), 332; https://doi.org/10.3390/ijgi9050332 - 19 May 2020

Cited by 43 | Viewed by 6390

Abstract

Land use types and anthropogenic activities represent considerable threats to groundwater pollution. To effectively monitor the groundwater quality, it is vital to measure pollution levels before they become severe. In our research area, located in Gilgit Baltistan in northern Pakistan, groundwater supplies are [...] Read more.

Land use types and anthropogenic activities represent considerable threats to groundwater pollution. To effectively monitor the groundwater quality, it is vital to measure pollution levels before they become severe. In our research area, located in Gilgit Baltistan in northern Pakistan, groundwater supplies are diminishing due to urban sprawl. In this study, we used a GIS-based DRASTIC model (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, Hydraulic conductivity) to analyze the area’s hydrological attributes to assess the groundwater susceptibility to pollution. Considering the importance of anthropogenic activities, this research primarily utilizes an adjusted DRASTIC model called DRASTICA, which incorporates anthropogenic impact as a parameter in the model. The resulting map, which depicts vulnerability to groundwater contamination, reveals that 19% of the study area is classed as having high vulnerability, 42% has moderate vulnerability, 37% has low vulnerability, and 2% has very low vulnerability to groundwater contamination. The adopted validation process (nitrate parameter of water quality) revealed that the suggested DRASTICA model achieved better results than the established DRASTIC model in a built-up environment. We used the nitrate concentration in groundwater to verify the formulated results, and the single parameter sensitivity analysis and map removal sensitivity analysis to analyze the model sensitivity. The sensitivity analysis indicated that the groundwater vulnerability to pollution is largely influenced by anthropogenic impact and depth to the water table, thereby suggesting that anthropogenic impact must be explicitly tackled in such studies. The groundwater zones exposed to anthropogenic pollution can be better classified with the help of the proposed DRASTICA model, particularly in and around built-up environments. The responsible authorities can use this groundwater contamination data as an early warning sign, so they can take practical actions to avoid extra pressure on this vital resource. Full article

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20 pages, 341 KiB

Open AccessEditor’s ChoiceReview

State-of-the-Art Geospatial Information Processing in NoSQL Databases

by Dongming Guo and Erling Onstein

ISPRS Int. J. Geo-Inf. 2020, 9(5), 331; https://doi.org/10.3390/ijgi9050331 - 19 May 2020

Cited by 36 | Viewed by 8160

Abstract

Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival [...] Read more.

Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages. Full article

(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)

31 pages, 5876 KiB

Open AccessReview

A Review of Techniques for 3D Reconstruction of Indoor Environments

by Zhizhong Kang, Juntao Yang, Zhou Yang and Sai Cheng

ISPRS Int. J. Geo-Inf. 2020, 9(5), 330; https://doi.org/10.3390/ijgi9050330 - 19 May 2020

Cited by 129 | Viewed by 15875

Abstract

Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper [...] Read more.

Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand. Full article

(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)

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16 pages, 2353 KiB

Open AccessArticle

Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification

by Darius Phiri, Matamyo Simwanda, Vincent Nyirenda, Yuji Murayama and Manjula Ranagalage

ISPRS Int. J. Geo-Inf. 2020, 9(5), 329; https://doi.org/10.3390/ijgi9050329 - 19 May 2020

Cited by 20 | Viewed by 4168

Abstract

Decision tree (DT) algorithms are important non-parametric tools used for land cover classification. While different DTs have been applied to Landsat land cover classification, their individual classification accuracies and performance have not been compared, especially on their effectiveness to produce accurate thresholds for [...] Read more.

Decision tree (DT) algorithms are important non-parametric tools used for land cover classification. While different DTs have been applied to Landsat land cover classification, their individual classification accuracies and performance have not been compared, especially on their effectiveness to produce accurate thresholds for developing rulesets for object-based land cover classification. Here, the focus was on comparing the performance of five DT algorithms: Tree, C5.0, Rpart, Ipred, and Party. These DT algorithms were used to classify ten land cover classes using Landsat 8 images on the Copperbelt Province of Zambia. Classification was done using object-based image analysis (OBIA) through the development of rulesets with thresholds defined by the DTs. The performance of the DT algorithms was assessed based on: (1) DT accuracy through cross-validation; (2) land cover classification accuracy of thematic maps; and (3) other structure properties such as the sizes of the tree diagrams and variable selection abilities. The results indicate that only the rulesets developed from DT algorithms with simple structures and a minimum number of variables produced high land cover classification accuracies (overall accuracy > 88%). Thus, algorithms such as Tree and Rpart produced higher classification results as compared to C5.0 and Party DT algorithms, which involve many variables in classification. This high accuracy has been attributed to the ability to minimize overfitting and the capacity to handle noise in the data during training by the Tree and Rpart DTs. The study produced new insights on the formal selection of DT algorithms for OBIA ruleset development. Therefore, the Tree and Rpart algorithms could be used for developing rulesets because they produce high land cover classification accuracies and have simple structures. As an avenue of future studies, the performance of DT algorithms can be compared with contemporary machine-learning classifiers (e.g., Random Forest and Support Vector Machine). Full article

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22 pages, 4294 KiB

Open AccessArticle

Using GIS for Disease Mapping and Clustering in Jeddah, Saudi Arabia

by Abdulkader Murad and Bandar Fuad Khashoggi

ISPRS Int. J. Geo-Inf. 2020, 9(5), 328; https://doi.org/10.3390/ijgi9050328 - 18 May 2020

Cited by 38 | Viewed by 13580

Abstract

Geographic information systems (GIS) can be used to map the geographical distribution of the prevalence of disease, trends in disease transmission, and to spatially model environmental aspects of disease occurrence. The aim of this study is to discuss a GIS application created to [...] Read more.

Geographic information systems (GIS) can be used to map the geographical distribution of the prevalence of disease, trends in disease transmission, and to spatially model environmental aspects of disease occurrence. The aim of this study is to discuss a GIS application created to produce mapping and cluster modeling of three diseases in Jeddah, Saudi Arabia: diabetes, asthma, and hypertension. Data about these diseases were obtained from health centers’ registered patient records. These data were spatially evaluated using several spatial–statistical analytical models, including kernel and hotspot models. These models were created to explore and display the disparate patterns of the selected diseases and to illustrate areas of high concentration, and may be invaluable in understanding local patterns of diseases and their geographical associations. Full article

(This article belongs to the Special Issue GIS in Healthcare)

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11 pages, 3434 KiB

Open AccessArticle

Optimal Lowest Astronomical Tide Estimation Using Maximum Likelihood Estimator with Multiple Ocean Models Hybridization

by Mohammed El-Diasty

ISPRS Int. J. Geo-Inf. 2020, 9(5), 327; https://doi.org/10.3390/ijgi9050327 - 17 May 2020

Cited by 3 | Viewed by 3550

Abstract

Developing an accurate Lowest Astronomical Tide (LAT) in a continuous form is essential for many maritime applications as it can be employed to develop an accurate continuous vertical control datum for hydrographic surveys applications and to produce accurate dynamic electronic navigation charts for [...] Read more.

Developing an accurate Lowest Astronomical Tide (LAT) in a continuous form is essential for many maritime applications as it can be employed to develop an accurate continuous vertical control datum for hydrographic surveys applications and to produce accurate dynamic electronic navigation charts for safe maritime navigation by mariners. The LAT can be developed in a continuous (surface) using an estimated LAT surface model from the hydrodynamic ocean model along with coastal discrete LAT point values derived from tide gauges data sets to provide the corrected LAT surface model. In this paper, an accurate LAT surface model was developed for the Red Sea case study using a Maximum Likelihood Estimator (MLE) with multiple hydrodynamic ocean models hybridization, namely, WebTide, FES2014, DTU10, and EOT11a models. It was found that the developed optimal hybrid LAT model using MLE with multiple hydrodynamic ocean models hybridization ranges from 0.1 m to 1.63 m, associated with about 2.4 cm of uncertainty at a 95% confidence level in the Red Sea case study area. To validate the accuracy of the developed model, the comparison was made between the optimal hybrid LAT model developed from multiple hydrodynamic ocean models hybridization using the MLE method with the individual LAT models estimated from individual WebTide, FES2014, DTU10, or EOT11a ocean models based on the associated uncertainties estimated at a 95% confidence level. It was found that the optimal hybrid LAT model accuracy is superior to the individual LAT models estimated from individual ocean models with an improvement of about 50% in average, based on the estimated uncertainties. The importance of developing optimal LAT surface model using the MLE method with multiple hydrodynamic ocean models hybridization in this paper with few centimeters level of uncertainty can lead to accurate continuous vertical datum estimation that is essential for many maritime applications. Full article

(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)

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23 pages, 4073 KiB

Open AccessEditor’s ChoiceArticle

Ranking of Assets with Respect to Their Exposure to the Landslide Hazard: A GIS Proposal

by Paolino Di Felice

ISPRS Int. J. Geo-Inf. 2020, 9(5), 326; https://doi.org/10.3390/ijgi9050326 - 17 May 2020

Cited by 1 | Viewed by 2841

Abstract

The need to protect critical infrastructures (for short called assets within this paper) arises because of the hazards they are exposed to. In this article, the hazard is represented by the landslides. The first part of the paper proposes a scientifically robust method [...] Read more.

The need to protect critical infrastructures (for short called assets within this paper) arises because of the hazards they are exposed to. In this article, the hazard is represented by the landslides. The first part of the paper proposes a scientifically robust method for the identification of the top-N assets that can be modeled as “points” (mainly buildings). The developed method takes into account the slope of the terrain, the runout distance of the landslide and its trajectory. The latter is roughly estimated through the notion of linear regression line. The method is applied to a real case to carry out a preliminary validation of it. In the second part of the paper, it is formalized the problem of computing the ranking of assets that can be modeled as “lines” (e.g., highways, power lines, pipelines, railway lines, and so on, that cross a given territory). The problem is solved in three steps: (a) Segmentation (it “cuts” each route in segments), (b) Sampling (it extracts points from each segment), and (c) Calculation (it associates an exposure value to each extracted point and, then, computes the exposure of the various segments composing the routes). The computation of the exposure for the points is carried out by applying the method of the first part of the paper. Both rankings can be used by the local administrators as a conceptual tool for narrowing down a global problem to smaller, higher exposure, geographic areas where the management of the hazard is crucial. Full article

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21 pages, 12625 KiB

Open AccessArticle

Visual Exposure of Rock Outcrops in the Context of a Forest Disease Outbreak Simulation Based on a Canopy Height Model and Spectral Information Acquired by an Unmanned Aerial Vehicle

by Marie Balková, Aleš Bajer, Zdeněk Patočka and Tomáš Mikita

ISPRS Int. J. Geo-Inf. 2020, 9(5), 325; https://doi.org/10.3390/ijgi9050325 - 15 May 2020

Cited by 13 | Viewed by 3722

Abstract

This research was focused on the study of visual exposure evolution in the locality of the Drátenická skála nature monument (in the Czech Republic) and the surrounding forest complex in terms of history and through modelling for further possible stand development. The local [...] Read more.

This research was focused on the study of visual exposure evolution in the locality of the Drátenická skála nature monument (in the Czech Republic) and the surrounding forest complex in terms of history and through modelling for further possible stand development. The local forests underwent conversion from a natural fir-beech composition to an intensive spruce monoculture with few insect pests or windbreak events to an actual bark beetle infestation. Historic maps, landscape paintings, photographs, and orthophotos served as the basic materials for the illustration of the past situation. Further development was modelled using canopy height models and spectral properties captured by unmanned aerial vehicles (UAVs). As an example, the possible situation of total mortality among coniferous spruce trees after a bark beetle outbreak was modelled. Other options and a practical use of such preprocessed data are, for example, a model for opening and transforming the stands around the rock as one of the ongoing outcrop management trends in the protected landscape area (PLA) of Žďárské vrchy. Full article

(This article belongs to the Special Issue UAV in Smart City and Smart Region)

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18 pages, 3922 KiB

Open AccessArticle

Integrating Land-Use and Renewable Energy Planning Decisions: A Technical Mapping Guide for Local Government

by Jiaao Guo, Victoria Fast, Philip Teri and Kirby Calvert

ISPRS Int. J. Geo-Inf. 2020, 9(5), 324; https://doi.org/10.3390/ijgi9050324 - 14 May 2020

Cited by 6 | Viewed by 5927

Abstract

Land-based, utility-scale renewable energy (RE) systems using wind or solar resources to generate electricity is becoming a decisive solution to meet long-term carbon emission reduction goals. Local governments are responding in kind, by adopting their own goals and/or establishing policies to facilitate successful [...] Read more.

Land-based, utility-scale renewable energy (RE) systems using wind or solar resources to generate electricity is becoming a decisive solution to meet long-term carbon emission reduction goals. Local governments are responding in kind, by adopting their own goals and/or establishing policies to facilitate successful implementations of RE in their jurisdiction. One factor to successful RE development is to locate the most suitable lands, while continuing to sustain land-based economies and ecosystem services. Local governments often have limited resources; and this is especially true for small, land-constrained local governments. In this paper, we illustrate how a standardized RE technical mapping framework can be used by local governments to advance the implementation of RE in land-constrained areas, through a case study in the Town of Canmore, Alberta. Canmore has a limited municipal area surrounded by the Canadian Rockies, along with complex land-use bylaw and environmentally sensitive habitats. This mapping framework accounts for these conditions as it considers theoretical resources, technically recoverable lands, legally accessible lands, and the spatial capital cost of connecting new RE facilities. Different land-use planning scenarios are considered including changing setback buffers and expanding restrictions on development to all environmentally sensitive districts. The total RE potentials are then estimated based on the least-conflict lands. Technically speaking, even under restrictive land suitability scenarios, Canmore holds enough land to achieve ambitious RE targets, but opportunities and challenges to implementation remain. To eventually succeed in its long-term emission reduction goal, the most decisive step for Canmore is to balance the growth of energy demands, land-use changes, and practicable RE development. Mapping systems that can study the influence of land-use planning decisions on RE potential are critical to achieving this balance. Full article

(This article belongs to the Collection Spatial and Temporal Modelling of Renewable Energy Systems)

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15 pages, 3866 KiB

Open AccessEditor’s ChoiceArticle

An All-in-One Application for Temporal Coordinate Transformation in Geodesy and Geoinformatics

by Antonio Banko, Tedi Banković, Marko Pavasović and Almin Đapo

ISPRS Int. J. Geo-Inf. 2020, 9(5), 323; https://doi.org/10.3390/ijgi9050323 - 13 May 2020

Cited by 4 | Viewed by 3918

Abstract

Over the years, Global Navigation Satellite Systems (GNSS) have been established in the geosciences as a tool that determines the positions of discrete points (stations) on the Earth’s surface, on global to local spatial scales in a very simple and economical manner. Coordinates [...] Read more.

Over the years, Global Navigation Satellite Systems (GNSS) have been established in the geosciences as a tool that determines the positions of discrete points (stations) on the Earth’s surface, on global to local spatial scales in a very simple and economical manner. Coordinates obtained by space geodetic measurements ought to be processed, adjusted, and propagated in a given reference frame. As points on the Earth’s surface do not have a fixed position, but rather, are moving with associated velocities, it is inevitable to include those velocities in the coordinate transformation procedure. Station velocities can be obtained from kinematic models of tectonic plate motions. The development and realization of an all-in-one standalone desktop application is presented in this paper. The application unifies coordinate transformation between different realizations (reference frames) of the International Terrestrial Reference System (ITRS) and European Terrestrial Reference System 1989 (ETRS89) following European Reference Frame Technical Note (EUREF TN) recommendations with temporal shifts of discrete points on the Earth’s surface caused by plate tectonics by integrating no-net rotation (NNR) kinematic models of the Eurasian tectonic plate. Full article

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24 pages, 6921 KiB

Open AccessArticle

3D Building Façade Reconstruction Using Deep Learning

by Konstantinos Bacharidis, Froso Sarri and Lemonia Ragia

ISPRS Int. J. Geo-Inf. 2020, 9(5), 322; https://doi.org/10.3390/ijgi9050322 - 13 May 2020

Cited by 32 | Viewed by 6357

Abstract

In recent years, advances in computer hardware, graphics rendering algorithms and computer vision have enabled the utilization of 3D building reconstructions in the fields of archeological structure restoration and urban planning. This paper deals with the reconstruction of realistic 3D models of buildings [...] Read more.

In recent years, advances in computer hardware, graphics rendering algorithms and computer vision have enabled the utilization of 3D building reconstructions in the fields of archeological structure restoration and urban planning. This paper deals with the reconstruction of realistic 3D models of buildings façades, in the urban environment for cultural heritage. The proposed approach is an extension of our previous work in this research topic, which introduced a methodology for accurate 3D realistic façade reconstruction by defining and exploiting a relation between stereoscopic image and tacheometry data. In this work, we re-purpose well known deep neural network architectures in the fields of image segmentation and single image depth prediction, for the tasks of façade structural element detection, depth point-cloud generation and protrusion estimation, with the goal of alleviating drawbacks in our previous design, resulting in a more light-weight, robust, flexible and cost-effective design. Full article

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23 pages, 9973 KiB

Open AccessArticle

Using GIS to Explore the Potential of Business Rating Data to Analyse Stock and Value Change for Land Administration: A Case Study of York

by Paul Greenhalgh, Helen King, Kevin Muldoon-Smith, Adejimi Adebayo and Josephine Ellis

ISPRS Int. J. Geo-Inf. 2020, 9(5), 321; https://doi.org/10.3390/ijgi9050321 - 12 May 2020

Cited by 7 | Viewed by 4768

Abstract

This study explores the potential of GIS to map and analyse the distribution, stock and value of commercial and industrial property using rating data compiled for the purposes of charging business rates taxation on all non-residential property in the UK. Rating data from [...] Read more.

This study explores the potential of GIS to map and analyse the distribution, stock and value of commercial and industrial property using rating data compiled for the purposes of charging business rates taxation on all non-residential property in the UK. Rating data from 2010, 2017 and 2019, comprising over 6000 property units in the City of York, were filtered and classified by retail, office and industrial use, before geocoding by post code. Nominal rateable values and floor areas for all premises were aggregated in 100 m diameter hexagonal grid and average rateable value calculated to reveal changes in the distribution and value of all employment floorspace in the City over the last decade. Temporospatial analysis revealed polarisation of York’s retail property market between the historic city centre and out-of-town locations. Segmenting traditional retail from food and drink premises revealed growth in the latter has mitigated the hollowing out of the city core. This study is significant in developing a replicable and efficient method of using GIS, using a nationally available rating dataset, to represent changes in the quantum, spatial distribution and relative value of employment floorspace over time to inform local and national land administration, spatial planning and economic development policy making. Full article

(This article belongs to the Special Issue Applications of GIScience for Land Administration)

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23 pages, 5893 KiB

Open AccessArticle

Subjective or Objective? How Objective Measures Relate to Subjective Life Satisfaction in Europe

by Karel Macků, Jan Caha, Vít Pászto and Pavel Tuček

ISPRS Int. J. Geo-Inf. 2020, 9(5), 320; https://doi.org/10.3390/ijgi9050320 - 12 May 2020

Cited by 20 | Viewed by 5762

Abstract

Quality of life and life satisfaction are topics that currently receive a great deal of attention across the globe. Many approaches exist, which use both qualitative and quantitative methods, to capture these phenomena. Historically, quality of life was measured exclusively by economic indicators. [...] Read more.

Quality of life and life satisfaction are topics that currently receive a great deal of attention across the globe. Many approaches exist, which use both qualitative and quantitative methods, to capture these phenomena. Historically, quality of life was measured exclusively by economic indicators. However, it is indisputable that other factors influence people’s life satisfaction, which is captured by subjective survey-based data. By contrast, objective data can easily be obtained and cover a wider range, in terms of population and area. In this research, the multiple fuzzy linear regression model is applied in order to explain the relationship between subjective life satisfaction and selected objective indicators used to evaluate quality of life. The great advantage of the fuzzy model lies in its ability to capture uncertainty, which is undoubtedly associated with the vague concept of subjective life satisfaction. The main outcome of the paper is the detection of indicators that have a statistically significant relationship with life satisfaction. Subsequently, a pan-European sub-national prediction of life satisfaction after the consideration of the most relevant input indicators was proposed, including the uncertainty associated with the prediction of such a phenomenon. The study revealed significant regional differences and similarities between the originally reported satisfaction of life and the predicted one. With the help of spatial and non-spatial statistics supported by visual analysis, it is possible to assess life satisfaction more precisely, while taking into account the ambiguity of the perception of life satisfaction. Additionally, predicted values supplemented with the uncertainty measure (fuzzy approach) and the synthesis of results in the form of European typology help to compare and contrast the results in a more useful manner than in existing studies. Full article

(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)

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23 pages, 4018 KiB

Open AccessArticle

Participation, for Whom? The Potential of Gamified Participatory Artefacts in Uncovering Power Relations within Urban Renewal Projects

by Teodora Iulia Constantinescu, Oswald Devisch and Liesbeth Huybrechts

ISPRS Int. J. Geo-Inf. 2020, 9(5), 319; https://doi.org/10.3390/ijgi9050319 - 12 May 2020

Cited by 5 | Viewed by 3384

Abstract

When defining participation in urban renewal projects in a political sense, this concept implies the challenging of power relations in each of its dimensions while addressing the need for knowledge, action and consciousness. Knowledge is defined as a resource which affects observable [...] Read more.

When defining participation in urban renewal projects in a political sense, this concept implies the challenging of power relations in each of its dimensions while addressing the need for knowledge, action and consciousness. Knowledge is defined as a resource which affects observable decision making. Action looks at who is involved in the production of such knowledge in order to challenge and shape the political agenda. Consciousness is how the production of knowledge changes the awareness or worldview of those involved, thus shaping the psychological and conceptual boundaries of what is possible. This paper addresses these politics of participation via the use of gamification, and more particularly gamified participatory artefacts. We discuss how a ‘good’ participatory planning process implies rebalancing existing power relations via the redistribution of knowledge, consciousness and actions, and aims to operationalize this ambition through a game. We particularly focus on the urban renewal process of one particular case, namely the Vennestraat—one of the main commercial streets of the city of Genk (BE) and present a three year participatory mapping process that made use of three gamified participatory artefacts (i.e., socio-economic network mapping, gathering mental images and scenario games). After uncovering the complex field of power relations in the entrepreneurial street, we analyze the different types of relations/groups that emerge from this participatory mapping process. The paper concludes with an analytical framework that employs gamified participatory artefacts in order to map and understand power relations and the mechanisms that frame them. Full article

(This article belongs to the Special Issue Gaming and Geospatial Information)

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18 pages, 5626 KiB

Open AccessArticle

Using Local Toponyms to Reconstruct the Historical River Networks in Hubei Province, China

by Aini Zhong, Yukun Wu, Ke Nie and Mengjun Kang

ISPRS Int. J. Geo-Inf. 2020, 9(5), 318; https://doi.org/10.3390/ijgi9050318 - 12 May 2020

Cited by 10 | Viewed by 4341

Abstract

As an important data source for historical geography research, toponyms reflect the human activities and natural landscapes within a certain area and time period. In this paper, a novel quantitative method of reconstructing historical river networks using toponyms with the characteristics of water [...] Read more.

As an important data source for historical geography research, toponyms reflect the human activities and natural landscapes within a certain area and time period. In this paper, a novel quantitative method of reconstructing historical river networks using toponyms with the characteristics of water and direction is proposed. It is suitable for the study area which possesses rich water resources. To reconstruct the historical shape of the river network, (1) water-related toponyms and direction-related toponyms are extracted as two datasets based on the key words in each village toponym; (2) the feasibility of the river network reconstruction based on these toponyms is validated via a quantitative analysis, according to the spatial distributions of toponyms and rivers; (3) the reconstructed historical shape of the river network can be obtained via qualitative knowledge and geometrical analysis; and (4) the reconstructed rivers are visualized to display their general historical trends and shapes. The results of this paper demonstrate the global correlation and local differences between the toponyms and the river network. The historical river dynamics are revealed and can be proven by ancient maps and local chronicles. The proposed method provides a novel way to reconstruct historical river network shapes using toponym datasets. Full article

(This article belongs to the Special Issue Geovisualization and Social Media)

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27 pages, 4040 KiB

Open AccessArticle

How Much Do We Learn from Addresses? On the Syntax, Semantics and Pragmatics of Addressing Systems

by Ali Javidaneh, Farid Karimipour and Negar Alinaghi

ISPRS Int. J. Geo-Inf. 2020, 9(5), 317; https://doi.org/10.3390/ijgi9050317 - 11 May 2020

Cited by 3 | Viewed by 3130

Abstract

An address is a specification that refers to a unique location on Earth. While there has been a considerable amount of research on the syntactic structure of addressing systems in order to evaluate and improve their quality, aspects of semantics and pragmatics have [...] Read more.

An address is a specification that refers to a unique location on Earth. While there has been a considerable amount of research on the syntactic structure of addressing systems in order to evaluate and improve their quality, aspects of semantics and pragmatics have been less explored. An address is primarily associated by humans to the elements of their spatial mental representations, but may also influence their spatial knowledge and activities through the level of detail it provides. Therefore, it is not only important how addressing components are structured, but it is also of interest to study their meaning as well as the pragmatics in relation to an interpreting agent. This article studies three forms of addresses (i.e., structured as in Austria, semi-formal as in Japan, and descriptive as in Iran) under the principles of semiotics (i.e., through levels of syntax, semantics, and pragmatics). Syntax is discussed through formal definitions of the addressing systems, while semantics and pragmatics are assessed through an agent-based model to explore how they influence spatial knowledge acquisition and growth. Full article

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17 pages, 5187 KiB

Open AccessArticle

Selection Method of Dendritic River Networks Based on Hybrid Coding for Topographic Map Generalization

by Chengming Li, Wei Wu, Pengda Wu, Yong Yin and Zhaoxin Dai

ISPRS Int. J. Geo-Inf. 2020, 9(5), 316; https://doi.org/10.3390/ijgi9050316 - 10 May 2020

Cited by 3 | Viewed by 2958

Abstract

As the coding of a dendritic river system can be used to represent the stream order and spatial-structure of a river network, it is always used in river selection, which is a key step in topographic map generalization. There are two categories of [...] Read more.

As the coding of a dendritic river system can be used to represent the stream order and spatial-structure of a river network, it is always used in river selection, which is a key step in topographic map generalization. There are two categories of conventional hydrological coding systems, one is the top-down approach, and the other is the bottom-up approach. However, the former does not accurately reflect the hierarchies of a dendritic river network, which is produced by catchment relationships, and it is not appropriate for the stream selection of river networks with uniform distributions of tributaries. The latter cannot directly indicate the subtree depth of a stream, and it is not favorable to stream selection of river systems that have topologically deep structures. Therefore, a selection method for dendritic river networks based on hybrid coding is proposed in this paper. First, the dendritic river network is coded through classical top-down Horton coding. Second, directed topology trees are constructed to organize the river network data, and stroke connections are calculated to code the river network in the bottom-up approach. Third, the river network is marked through hybrid usage of the top-down approach and bottom-up approach, and based on the spatial characteristics of the river network, the river network is classified into three kinds of subtrees: deep branch, shallow branch and modest branch. Then, appropriate coding is assigned automatically to different subtrees to achieve river selection. Finally, actual topographic map data of a river system in a region of Hubei Province are used to comparatively validate the hybrid coding system against two existing isolated coding systems. The experimental results demonstrate that the hybrid coding method is very effective for river network selection, not only in highlighting hierarchies formed by catchment relationships but also in the uniform distribution of tributaries. Full article

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20 pages, 6776 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Disdyakis Triacontahedron DGGS

by John Hall, Lakin Wecker, Benjamin Ulmer and Faramarz Samavati

ISPRS Int. J. Geo-Inf. 2020, 9(5), 315; https://doi.org/10.3390/ijgi9050315 - 8 May 2020

Cited by 17 | Viewed by 5686

Abstract

The amount of information collected about the Earth has become extremely large. With this information comes the demand for integration, processing, visualization and distribution of this data so that it can be leveraged to solve real-world problems. To address this issue, a carefully [...] Read more.

The amount of information collected about the Earth has become extremely large. With this information comes the demand for integration, processing, visualization and distribution of this data so that it can be leveraged to solve real-world problems. To address this issue, a carefully designed information structure is needed that stores all of the information about the Earth in a convenient format such that it can be easily used to solve a wide variety of problems. The idea which we explore is to create a Discrete Global Grid System (DGGS) using a Disdyakis Triacontahedron (DT) as the initial polyhedron. We have adapted a simple, closed-form, equal-area projection to reduce distortion and speed up queries. We have derived an efficient, closed-form inverse for this projection that can be used in important DGGS queries. The resulting construction is indexed using an atlas of connectivity maps. Using some simple modular arithmetic, we can then address point to cell, neighbourhood and hierarchical queries on the grid, allowing for these queries to be performed in constant time. We have evaluated the angular distortion created by our DGGS by comparing it to a traditional icosahedron DGGS using a similar projection. We demonstrate that our grid reduces angular distortion while allowing for real-time rendering of data across the globe. Full article

(This article belongs to the Special Issue Global Grid Systems)

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2 pages, 208 KiB

Open AccessEditorial

Spatially Supported Disaster Management: Introduction to the Special Issue “GI for Disaster Management”

by Orhan Altan, Tullio Joseph Tanzi, Madhu Chandra, Filiz Sunar and Lena Halounová

ISPRS Int. J. Geo-Inf. 2020, 9(5), 314; https://doi.org/10.3390/ijgi9050314 - 8 May 2020

Viewed by 1871

Abstract

This special issue explores most of the scientific issues related to spatially supported disaster management and its integration with geographical information system technologies in different disaster examples and scales [...] Full article

(This article belongs to the Special Issue GI for Disaster Management)

16 pages, 7957 KiB

Open AccessArticle

Identifying the Socio-Spatial Logics of Foreclosed Housing Accumulated by Large Private Landlords in Post-Crisis Catalan Cities

by Aaron Gutiérrez and Antoni Domènech

ISPRS Int. J. Geo-Inf. 2020, 9(5), 313; https://doi.org/10.3390/ijgi9050313 - 8 May 2020

Cited by 5 | Viewed by 2954

Abstract

The article analyses the socio-spatial logic behind the accumulation of foreclosed housing in the hands of large private landlords in the neighbourhoods of all the Catalan cities with over 100,000 inhabitants. Spatial regression and clustering techniques are applied to identify the determinants of [...] Read more.

The article analyses the socio-spatial logic behind the accumulation of foreclosed housing in the hands of large private landlords in the neighbourhoods of all the Catalan cities with over 100,000 inhabitants. Spatial regression and clustering techniques are applied to identify the determinants of the concentration patterns of 10,725 housing units in these cities. The socioeconomic variables, such as income level, percentage of foreign population, level of studies or percentage of unemployed residents, are identified as key explanatory factors of clustering of foreclosures in working-class neighbourhoods. A high presence of previously mortgaged homes is a variable especially relevant in the case of working-class neighbourhoods, but it has no incidence in the case of the medium-high class neighbourhoods. Our findings provide a detailed urban geography of the housing accumulated by banks which, at the same time, correspond to areas in which the vulture funds are focusing their business in the present and in the forthcoming years. New evidences of the spatial logic of the housing crisis and detailed information for the understanding of the new scenarios that have emerged during the post-crisis phase are revealed. Full article

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31 pages, 12742 KiB

Open AccessArticle

Combining AHP and ROC with GIS for Airport Site Selection: A Case Study in Libya

by Turan Erman Erkan and Wael Mohamed Elsharida

ISPRS Int. J. Geo-Inf. 2020, 9(5), 312; https://doi.org/10.3390/ijgi9050312 - 8 May 2020

Cited by 19 | Viewed by 5854

Abstract

Choosing airport locations requires thorough and comprehensive decisions to be made. To do so in a professional and logical manner is crucial for the social, economic, and logistic settings intended for any region. The present research takes place in Libya, where airports are [...] Read more.

Choosing airport locations requires thorough and comprehensive decisions to be made. To do so in a professional and logical manner is crucial for the social, economic, and logistic settings intended for any region. The present research takes place in Libya, where airports are just as vital for the economy in terms of tourism and investment by allowing for improved transportation throughout the developing market and supplier locations as well as trading between the industrial and financial sectors. For this reason, using the geographic information system (GIS) to determine the appropriate airport site, twenty-three criteria were considered. In addition, two different methods—analytic hierarchy process (AHP) and rank order centroid (ROC)—were utilized to derive the related weights. The comparison of the output maps from these two distinctive approaches shows that both approaches provide identical results. Finally, a sensitivity analysis was carried out to evaluate the reliability of the method used and select the best site among the proposed ones based on the result of the highest suitability index for each candidate site. This research provides a siting approach and substantial support for decision-makers in the issue of airport locations selection in Libya and other developing countries. Full article

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26 pages, 29488 KiB

Open AccessArticle

Geospatial Serverless Computing: Architectures, Tools and Future Directions

by Sujit Bebortta, Saneev Kumar Das, Meenakshi Kandpal, Rabindra Kumar Barik and Harishchandra Dubey

ISPRS Int. J. Geo-Inf. 2020, 9(5), 311; https://doi.org/10.3390/ijgi9050311 - 7 May 2020

Cited by 23 | Viewed by 6627

Abstract

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease [...] Read more.

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data. Full article

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20 pages, 6891 KiB

Open AccessEditor’s ChoiceArticle

Visualization of 3D Survey Data for Strata Titles

by Trias Aditya, Dany Laksono, Febrian F. Susanta, I. Istarno, D. Diyono and Didik Ariyanto

ISPRS Int. J. Geo-Inf. 2020, 9(5), 310; https://doi.org/10.3390/ijgi9050310 - 7 May 2020

Cited by 11 | Viewed by 5420

Abstract

Major cities and urban areas are beginning to develop and use 3D properties and public facilities. Consequently, 3D cadastral surveys are increasingly being employed for strata unit ownership registration as a part of land administration services. At present, most national land information systems [...] Read more.

Major cities and urban areas are beginning to develop and use 3D properties and public facilities. Consequently, 3D cadastral surveys are increasingly being employed for strata unit ownership registration as a part of land administration services. At present, most national land information systems do not support 2D and 3D cadastral visualizations. A field survey or validation survey is required to determine the geometry of 3D spatial units for property registration. However, the results of 3D surveys and mapping are not stored in the land information system. This work aims to integrate 2D and 3D geospatial data of property units collected from cadastral surveys with their corresponding legal data. It reviews the workflow for the use of 3D survey data for first-titling of 3D properties in Indonesia. A scenario of use and a prototype were developed based on existing practices and the possibility of extending Indonesia’s Land Administration Domain Model (LADM) to represent 3D units. Data submitted to the prototype as 3D geometries was survey data from 3D cadastral surveys or validation surveys utilizing terrestrial survey methods. The prototype used PostGIS and Cesium Ion to store 3D geometries of data from six 3D surveys. Registrars in local land offices could use the prototype to undertake strata unit registration that establishes a relationship among geospatial features and their survey documents and legal documents. Cesium JS was used as a 3D browser, customized as a web application, to manage and visualize 3D survey data to support strata title registration. The results demonstrate that the first titling of 3D cadaster objects could be conducted and properly visualized in Indonesia by extending the existing LADM with more support for 3D spatial representations and survey documents. Full article

(This article belongs to the Special Issue Applications of GIScience for Land Administration)

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14 pages, 3166 KiB

Open AccessEditor’s ChoiceArticle

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands

by Tuomas Yrttimaa, Ninni Saarinen, Ville Kankare, Niko Viljanen, Jari Hynynen, Saija Huuskonen, Markus Holopainen, Juha Hyyppä, Eija Honkavaara and Mikko Vastaranta

ISPRS Int. J. Geo-Inf. 2020, 9(5), 309; https://doi.org/10.3390/ijgi9050309 - 7 May 2020

Cited by 16 | Viewed by 4034

Abstract

Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, [...] Read more.

Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (H_g) and mean stem volume (V_mean). Most notably, the root-mean-square-error (RMSE) in H_g improved from 0.8 to 0.58 m and the bias improved from −0.75 to −0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, V_mean, H_g, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries. Full article

(This article belongs to the Special Issue UAV in Smart City and Smart Region)

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ISPRS Int. J. Geo-Inf., Volume 9, Issue 5 (May 2020) – 53 articles

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