ISPRS Int. J. Geo-Inf., Volume 10, Issue 3 (March 2021) – 89 articles

With the rapid development of earth observation, satellite navigation, mobile communication, and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. As a new form of data management, the global discrete grid can be used for the efficient storage and application of large-scale global spatial data, which is a digital multiresolution georeference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing, and application of current spatial data. There are different types of grid systems according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, as well as establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves the availability of the hexagonal-grid-based remote sensing data of remote sensing images. This evaluation method is generally applicable to all raster remote sensing images based on hexagonal grids, and it can be used to evaluate the availability of hexagonal grid images. Full article

The Three-River Source Region (TRSR) is vital to the ecological security of China. However, the impact of global warming on the dynamics of vegetation along the elevation gradient in the TRSR remains unclear. Accordingly, we used multi-source remote sensing vegetation indices (VIs) (GIMMS (Global Inventory Modeling and Mapping Studies) LAI (Leaf Area Index), GIMMS NDVI (Normalized Difference Vegetation Index), GLOBMAP (Global Mapping) LAI, MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index), MODIS NDVI, and MODIS NIRv (near-infrared reflectance of vegetation)) and digital elevation model data to study the changes of VGEG (Vegetation Greenness along the Elevation Gradient) in the TRSR from 2001 to 2016. Results showed that the areas with a positive correlation of vegetation greenness and elevation accounted for 36.34 ± 5.82% of the study areas. The interannual variations of VGEG showed that the significantly changed regions were mainly observed in the elevation gradient of 4–5 km. The VGEG was strongest in the elevation gradient of 4–5 km and weakest in the elevation gradient of >5 km. Correlation analysis showed that the mean annual temperature was positively correlated with VIs, and the effect of the mean annual precipitation on VIs was more obvious at low altitude than in high altitude. This study contributes to our understanding of the VGEG variation in the TRSR under global climate variation and also helps in the prediction of future carbon cycle patterns. Full article

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy. Full article

The Ecuadorian Amazon is considered a biodiverse region, and at the same time contains the largest number of oil blocks and oilfields in the country. Oil exploitation requires the implementation of oil facilities and related infrastructure, such as roads, water, and energy supply, for operation. These large engineering works can alter the dynamics of the Amazonian natural ecosystems. This paper analyzes the land use and land cover (LULC) change and relates spatial patterns within an oil block located in the province of Orellana, Ecuador. The study was processed in two phases, the first corresponding to the collection and classification of LULC classes within the oil block. The second phase concerned the calculation of landscape metrics, with the purpose of quantitatively characterizing each class. This analysis was carried out for the pre-concession, post-concession scenarios of the oil block and the current scenario of the region. The results revealed that the low predominance of forest cover within the study region is not directly associated with the beginning of the Block 47 concession. On the other hand, a significant reduction of the Coca River was evidenced for the 2018 scenario. Full article

Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This paper presents such a model in which three conceptual levels of abstraction are proposed to frame an agent-based representation of movement decision-making processes: ‘attribute,’ ‘actor,’ and ‘autonomous agent’. These in combination with three temporal, spatial, and spatiotemporal general forms of observations distinguish nine (3 × 3) representation typologies of movement data within the agent framework. Thirdly, there are three levels of cognitive reasoning: ‘association,’ ‘intervention,’ and ‘counterfactual’. This makes for 27 possible types of operation embedded in a conceptual cube with the level of abstraction, type of observation, and degree of cognitive reasoning forming the three axes. The conceptual model is an arena where movement queries and the statement of relevant objectives takes place. An example implementation of a tightly constrained spatiotemporal scenario to ground the agent-structure was summarised. The platform has been well-defined so as to accommodate different tools and techniques to drive causal inference in computational movement analysis as an immediate future step. Full article

The identification of urban functional areas is essential for urban planning and sustainable development. Spatial grids are the basic units for the implementation of urban plans and management by cities or development zones. The emergence of internet “big data” provides new ideas for the identification of urban functional areas. Based on point of interest (POI) data from Baidu Maps, the Xicheng District of Beijing was divided into grids with side lengths of 200, 500, and 1000 m in this study. The kernel density method was used to analyze the spatial structure of POI data. Two indicators, that is, the frequency density and category ratio, were then used to identify single- and mixed-functional areas. The results show that (1) commercial and financial areas are concentrated in the city center and multiple business centers have not developed; (2) scenic areas account for the largest proportion of single-functional areas in the Xicheng District of Beijing, followed by education and training, residence, and party and government organizations areas; and (3) the 200 × 200 m and 500 × 500 m grids are the most suitable for the identification of single- and mixed-functional areas, respectively. Full article

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling. Full article

3D scene classification has become an important research field in photogrammetry, remote sensing, computer vision and robotics with the widespread usage of 3D point clouds. Point cloud classification, called semantic labeling, semantic segmentation, or semantic classification of point clouds is a challenging topic. Machine learning, on the other hand, is a powerful mathematical tool used to classify 3D point clouds whose content can be significantly complex. In this study, the classification performance of different machine learning algorithms in multiple scales was evaluated. The feature spaces of the points in the point cloud were created using the geometric features generated based on the eigenvalues of the covariance matrix. Eight supervised classification algorithms were tested in four different areas from three datasets (the Dublin City dataset, Vaihingen dataset and Oakland3D dataset). The algorithms were evaluated in terms of overall accuracy, precision, recall, F1 score and process time. The best overall results were obtained for four test areas with different algorithms. Dublin City Area 1 was obtained with Random Forest as 93.12%, Dublin City Area 2 was obtained with a Multilayer Perceptron algorithm as 92.78%, Vaihingen was obtained as 79.71% with Support Vector Machines and Oakland3D with Linear Discriminant Analysis as 97.30%. Full article

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale. Full article

Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function patterns of buildings, while building recognition rates remain unsatisfying. In this paper, we propose a new method to infer the mixed use of buildings based on a tensor decomposition algorithm, which integrates information from both high-resolution remote sensing images and social sensing data. We selected the Tianhe District of Guangzhou, China to validate our method. The results show that the recognition rate of buildings can reach 98.67%, with an average recognition accuracy of 84%. Our study proves that the tensor decomposition algorithm can extract different function patterns of buildings unsupervised, while remote sensing data can provide key information for inferring building functions. The tensor decomposition-based method can serve as an effective and efficient way to infer the mixed use of buildings, which can achieve better results with simpler steps. Full article

Dam deformation monitoring can directly identify the safe operation state of a dam in advance, which plays an important role in dam safety management. Three-dimensional (3D) terrestrial laser scanning technology is widely used in the field of deformation monitoring due to its fast, complete, and high-density 3D data acquisition capabilities. However, 3D point clouds are characterized by rough surfaces, discrete distributions, which affect the accuracy of deformation analysis of two states data. In addition, it is impossible to directly extract the correspondence points from an irregularly distributed point cloud to unify the coordinates of the two states’ data, and the correspondence lines and planes are often difficult to obtain in the natural environment. To solve the above problems, this paper studies a displacement change detection method for arch dams based on two-step point cloud registration and contour model comparison method. In the environment around a dam, the stable rock is used as the correspondence element to improve the registration accuracy, and a two-step registration method from rough to fine using the iterative closest point algorithm is present to describe the coordinate unification of the two states’ data without control network and target. Then, to analyze the displacement variation of an arch dam surface in two states and improve the accuracy of comparing the two surfaces without being affected by the roughness of the point cloud, the contour model fitting the point clouds is used to compare the change in distance between models. Finally, the method of this paper is applied to the Xiahuikeng Arch Dam, and the displacement changes of the entire dam in different periods are visualized by comparing with the existing methods. The results show that the displacement change in the middle area of the dam is generally greater than that of the two banks, increasing with the increase in elevation, which is consistent with the displacement change behavior of the arch dam during operation and can reach millimeter-level accuracy. Full article

The General Allotment Act of 1887, also known as the Dawes Act, established the legal basis for the United States government to break up remaining tribally-owned reservation lands in the U.S. by allotting individual parcels to tribal members and selling the remaining “surplus.” This research explores the processes involved in mapping these historical allotments and describes a method to automatically generate spatial data of allotments. A custom geographic information systems (GIS) tool was created that takes tabular based allotment land descriptions and digital Public Land Survey (PLSS) databases to automatically generate spatial and attribute data of those land parcels. The Standing Rock Sioux Tribe of North and South Dakota was used as the initial study area to test the mapping technique, which resulted in successfully auto-mapping over 99.1% of allotted lands on the reservation, including the smallest aliquot parcels. This GIS technique can be used to map any tribal lands or reservation with allotment data available, and currently it can be used to map over 120 individual reservations using publicly available data from the Bureau of Land Management (BLM). Full article

In this paper, we propose a decentralized semantic reasoning approach for modeling vague spatial objects from sensor network data describing vague shape phenomena, such as forest fire, air pollution, traffic noise, etc. This is a challenging problem as it necessitates appropriate aggregation of sensor data and their update with respect to the evolution of the state of the phenomena to be represented. Sensor data are generally poorly provided in terms of semantic information. Hence, the proposed approach starts with building a knowledge base integrating sensor and domain ontologies and then uses fuzzy rules to extract three-valued spatial qualitative information expressing the relative position of each sensor with respect to the monitored phenomenon’s extent. The observed phenomena are modeled using a fuzzy-crisp type spatial object made of a kernel and a conjecture part, which is a more realistic spatial representation for such vague shape environmental phenomena. The second step of our approach uses decentralized computing techniques to infer boundary detection and vertices for the kernel and conjecture parts of spatial objects using fuzzy IF-THEN rules. Finally, we present a case study for urban noise pollution monitoring by a sensor network, which is implemented in Netlogo to illustrate the validity of the proposed approach. Full article

The work of Philibert Girault de Prangey, who was a draughtsman, pioneering photographer and an Islamic architecture scholar, has been the subject of recent exhibitions in his hometown (Langres, 2019), at the Metropolitan Museum (New York, 2019) and at the Musée d’Orsay (Paris, 2020). After visiting Andalusia between 1832 and 1833, Prangey completed the publication “Monuments arabes et moresques de Cordoue, Seville et Grenada” in 1839, based on his own drawings and measurements. For the first time, this research analyses his interior perspectives of the Mosque-Cathedral of Cordoba (Spain). The novel methodology is based on its comparison with a digital model derived from the point cloud captured by a 3D laser scanner. After locating the different viewpoints, the geometric precision and the elaboration process are analysed, taking into account historic images by various authors, other details published by Prangey and the architectural transformations of the building. In this way, the veracity and documentary interest of some beautiful perspectives of a monument inscribed on the World Heritage List by UNESCO is valued. Full article

Conserving landscape connections among favorable habitats is a widely used strategy to maintain populations in an increasingly fragmented world. A species can then exist as a metapopulation consisting of several subpopulations connected by dispersal. Our study focuses on the importance of human–wildlife coexistence areas in maintaining connectivity among primary habitats of small ungulates within and outside protected areas in a large landscape in central India. We used geospatial information and species presence data to model the suitable habitats, core habitats, and connectivity corridors for four antelope species in an ~89,000 km² landscape. We found that about 63% of the core habitats, integrated across the four species, lie outside the protected areas. We then measured connectivity in two scenarios: the present setting, and a hypothetical future setting—where habitats outside protected areas are lost. We also modelled the areas with a high risk of human-influenced antelope mortality using eco-geographical variables and wildlife mortality records. Overall, we found that the habitats in multiple-use forests play a central role in maintaining the connectivity network for antelopes. Sizable expanses of privately held farmlands and plantations also contribute to the essential movement corridors. Some perilous patches with greater mortality risk for species require mitigation measures such as underpasses, overpasses, and fences. Greater conservation efforts are needed in the spaces of human–wildlife coexistence to conserve the habitat network of small ungulates. Full article

This paper synthesizes vulnerability, risk, resilience, and sustainability (VRRS) in a way that can be used for decision evaluations about sustainable systems, whether such systems are called coupled natural–human systems, social–ecological systems, coupled human–environment systems, and/or hazards influencing global environmental change, all considered geospatial open systems. Evaluations of V-R-R-S as separate concepts for complex decision problems are important, but more insightful when synthesized for improving integrated decision priorities based on trade-offs of V-R-R-S objectives. A synthesis concept, called VRRSability, provides an overarching perspective that elucidates Tier 2 of a previously developed four-tier framework for organizing measurement-informed ontology and epistemology for sustainability information representation (MOESIR). The new synthesis deepens the MOESIR framework to address VRRSability information representation and clarifies the Tier 2 layer of abstraction. This VRRSability synthesis, composed of 13 components (several with sub-components), offers a controlled vocabulary as the basis of a conceptual framework for organizing workflow assessment and intervention strategies as part of geoinformation decision support software. Researchers, practitioners, and machine learning algorithms can use the vocabulary results for characterizing functional performance relationships between elements of geospatial open systems and the computing technology systems used for evaluating them within a context of complex sustainable systems. Full article

In developing countries, crime is a serious problem that affects the operation and viability of firms. Offenses such as vandalism, robbery, and theft raise the operating costs of firms and imposes on them indirect costs. The literature on spatial analysis of crime is vast; however, relatively little research has addressed business crime, especially in developing countries’ cities. Spatial and temporal analysis of crime concentration represents a basic input for the design and implementation of appropriate prevention and control strategies. This article explores the spatial concentration and stability of thefts committed against commercial establishments in the city of Mexicali, Mexico, from 2009 to 2011 using the Gini coefficient, Lorenz curve, and decile maps. Results revealed that thefts were highly concentrated in a small percentage of urban basic geostatistical areas. Moreover, a portion of these areas were classified as having the highest deciles of thefts (hot spots) and remained in this group throughout the period. In both cases, the relationship between crime and place was close to the 80/20 rule, or the Pareto principle. Full article

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation. Full article

Ecosystem-based adaptation to climate change impacts, such as shoreline retreat, has been promoted at the international, national, and even local levels. However, among scientists, opinions about how to implement it in spatial-planning practices are varied. Science-based environmental factors, human wellbeing, and sustainable development can be strengthened by developing spatial-planning-based ecosystem adaptations (SPBEAs). Therefore, this article aims to assess how the SPBEA model can be developed within an area prone to shoreline retreat. A coastal area of the Sayung subdistrict in Central Java, Indonesia, was selected as a study area because it has experienced a massive shoreline retreat. A multicriteria analysis (MCA) method was employed for developing the model by using the geographic information system (GIS) technique of analysis, divided into three steps: the fishpond zone determination, which involved the analytical hierarchy process (AHP) method in the process of model development; the fishpond site determination; SPBEA fishpond site development. The results show that the SPBEA model is the best practice solution for combatting shoreline retreat because of tidal waves and/or sea-level rise. The spatial site management should empower the coastal protection zone and the sustainable fishpond zone by implementing a silvofishery approach. Full article

Against the backdrop of urban stock renewal, as the core area of a city rich in culture, aesthetics, and tourism resources, the assessment of landscape visual sensitivity of historic districts can provide an accurate, objective, and intuitive decision-making basis for the multi-purpose planning of districts. The main purpose of this study was to develop an assessment method based on the geographic information system (GIS) in order to make a visual sensitivity index map on a district scale. To this end, this study uses the multi-criteria evaluation (MCE) method, selects the visibility (VS_v), the number of potential users (VS_u), and remarkableness (VS_e) as the main criteria, and constructs a comprehensive assessment model of the visual sensitivity of the historic landscape. The most well-protected Wudadao Historic District in Tianjin (Wudadao) was selected as the study area, and its visual sensitivity was assessed. The assessment results are divided into four levels: areas of high sensitivity, moderate sensitivity, low sensitivity, and very low sensitivity. Results indicate that after the optimization and improvement of the evaluation index for visual sensitivity of a large-scale forest landscape, it is feasible to evaluate the small-scale visual sensitivity of historic districts; the higher the sensitivity level, the more important it is to be protected, and the more cautious it should be in the renewal of districts; the higher the number of potential users, the higher the visual sensitivity level, and so on. Further attention needs to be paid to planning and design to improve visual quality. Full article

Typhoon is one of the most destructive natural disasters in the world. Real-time information on the process of typhoon events serves as important reference for disaster emergency. In the era of big data, microblog text has been gradual applied to the prevention, preparation, response, and recovery of disaster management. However, previous studies mostly focused on the acquisition of different disaster information in microblog text, while ignoring the structural integration of this fragmented information, and thus cannot reflect the dynamic process of typhoon events. In this paper, a typhoon event information model (TEIM) considering the multi-granularity and dynamic characteristics of information is constructed from the spatio-temporal perspective. On the basis of extracting the information elements of typhoon events from microblog text, a process-oriented information aggregation method (TEPIA) is proposed to provide an ordered information resource for detecting the evolution process of typhoon events. Based on the case study of typhoon “Lekima” event using Sina Weibo, the results show that the method proposed in this paper can comprehensively detect the information of different objects on any spatio-temporal node during the process of typhoon events, which is beneficial to mining disaster emergencies in small scale from microblog text. Full article

The bus stop layout and route deployment may influence the efficiency of bus services. Evaluating the supply of bus service requires the consideration of demand from various urban activities, such as residential and job-related activities. Although various evaluation methods have been explored from different perspectives, it remains a challenging issue. This study proposes a spatial statistical approach by comparing the density of the potential demand and supply of bus services at bus stops. The potential demand takes jobs-housing locations into account, and the supply of bus services considers bus stops and their associated total number of daily bus arrivals. The kernel density estimation (KDE) and spatial autocorrelation analyses are employed to investigate the coupling relationship between the demand and supply densities at global and local scales. A coupling degree index (CDI) is constructed to standardize the measurement of demand-supply balance. A case study in Wuhan, China demonstrated that: (1) the spatial distribution of bus stops is reasonable at global level, (2) Seriously unbalanced locations for bus services have been detected at several stops. Related adjustments that can improve these defects are highly recommended. Full article

The natural ecological lands, such as forest land, grassland, wetland, etc., constitute the most important factor for maintaining and preserving the earth’s ecosystem, which must be well concerned in the regional function-oriented planning for the sustainability of human economic development. We analyzed and evaluated the change of natural ecological land in the function-oriented planning regions where we applied the major function-oriented zones introduced as a new concept in China. Using the land-use data from 2009 to 2018 that were produced by the National Land Use Survey, we re-classified natural ecological land types into the forest, grassland, wetland, and bare land, and then addressed the changes of natural ecological land types from 2009 to 2018 in the major function-oriented zones. As a result, the area of natural ecological lands generally tended to decrease from 2009 to 2018, while the decreasing trend of natural ecological land areas was controlled after 2015 with the implementation of governmental policies for environment protection and eco-logical projects. Especially, the decrease of forest land area significantly tended to be zero in 2018 in optimal development zones. The decreased areas of natural ecological lands were mostly converted from artificial land from 2008 to 2019. On the other side, the forest lands mostly changed from cropland and grassland in key development zones, agricultural production zones, and key ecological function zones, due to the fact that grassland conversed in afforestation during this period. The evaluation of natural ecological land changes, which could be implemented by using the annual updates of national land-use data in China, is significant to support the government’s spatial regulation design, to reshape the planned regions, and make policies for environmental restoration and protection management. Full article

Accessibility analyses are an essential step in the evaluation and planning of urban infrastructures such as transport or pipeline networks. However, these studies generally produce sharply defined lines (called isovarones) or areas (called isovarone areas) that represent the same or similar accessibility. Uncertainties in the input data are usually not taken into account. The aim of this contribution is, therefore, to set up a structured framework that describes the integration of uncertainty information for accessibility analyses. This framework takes uncertainties in the input data, in the processing step, in the target variables, and in the final visualization into account. Particular attention is paid, on the one hand, to the impact of the uncertainties in the target values, as these are key factors for reasoning and decision making. On the other hand, the visualization component is emphasized by applying a dichotomous classification of uncertainty visualization methods. This framework leads to a large set of possible combinations of uncertainty categories. Five selected examples that have been generated with a new software tool and that cover important combinations are presented and discussed. Full article

Small object detection in very-high-resolution (VHR) optical remote sensing images is a fundamental but challenaging problem due to the latent complexities. To tackle this problem, the MdrlEcf model is proposed by modifying deep reinforcement learning (DRL) and extracting the efficient convolution feature. Firstly, an efficient attention network is constructed by introducing the local attention into the convolutional neural network. Combining the shallow low-level features with rich detail descriptions and high-level features with more semantic meanings effectively, efficient convolution features can be obtained. By this, the attention network can effectively enhance the ability to extract small target features and suppressing useless features. Secondly, the efficient feature map is sent to the region proposal network constructed by modified DRL. Using the modified reward function, this model can accumulate more rewards to conduct the search process, and potentially generate effective subsequent proposals and classification scores. It also can increase the effectiveness of object locations and classifications for small targets. Quantitative and qualitative experiments are conducted to verify the detection performance of different models. The results show that the proposed MdrlEcf can effectively and accurately locate and identify related small objects. Full article

Innovation City projects, aimed at balanced national development in South Korea, have relocated public institutions from the Seoul metropolitan area to provinces, decentralizing population and economic functions, over the past decade. This study measured changes in regional centrality (the central and local location or hierarchy of objects in a network) at the 14 cities where Innovation City projects were constructed. Commuter Origin-Destination data were analyzed using Rstudio. In the case of connectivity centrality, 13 out of 14 regions saw a rise in centrality values; among them, Busan, Daegu, and Ulsan belong to large cities. This suggests that the impact of Innovation City projects on established metropolitan areas may not be very significant. Five of the 14 projects increased the value of eigenvector centrality, while 10 increased the centrality ranking. This means that the absolute traffic volume of Innovation Cities across the country had increased, while the centrality of areas around these cities declined, suggesting that Innovation Cities should pursue co-prosperity with surrounding areas. In this way, Innovation Cities can have a positive impact on surrounding areas, and positive externalities of relocation projects are maximized. However, such development effects are confined to Innovation City areas, negatively influencing balanced regional development. Full article

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction. Full article

There is an increasing need for spatial planning to manage coastal tourism, and applying social media data has emerged as an effective strategy to support coastal tourism spatial planning. Researchers and decision-makers require spatially explicit information that effectively reveals the current visitation state of the region. The purpose of this study is to identify coastal tourism hotspots considering appropriate spatial units in the regional scale using social media data to examine the advantages and limitations of applying spatial hotspots to spatial planning. Data from Flickr and Twitter with 30″ spatial resolution were obtained from four South Korean regions. Coastal tourism hotspots were then derived using Getis-Ord Gi. Comparing the derived hotspot maps with the visitation rate distribution maps, the derived hotspot maps sufficiently identified the spatial influences of visitors and tourist attractions applicable for spatial planning. As the spatial autocorrelation of social media data differs based on the spatial unit, coastal tourism hotspots according to spatial unit are inevitably different. Spatial hotspots derived from the appropriate spatial unit using social media data are useful for coastal tourism spatial planning. Full article

The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems. Full article

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources. Full article