Search Results (152)

The widespread availability of tools to collect and share spatial data enables us to produce a large amount of geographic information on a daily basis. This enormous production of spatial data requires scalable data management systems. Geospatial architectures have changed from clusters to cloud architectures and more parallel and distributed processing platforms to be able to tackle these challenges. Peer-to-peer (P2P) systems as a backbone of distributed systems have been established in several application areas such as web3, blockchains, and crypto-currencies. Unlike centralized systems, data storage in P2P networks is distributed across network nodes, providing scalability and no single point of failure. However, managing and processing queries on these networks has always been challenging. In this work, we propose a spatio-temporal indexing data structure, DSTree. DSTree does not require additional Distributed Hash Trees (DHTs) to perform multi-dimensional range queries. Inserting a piece of new geographic information updates only a portion of the tree structure and does not impact the entire graph of the data. For example, for time-series data, such as storing sensor data, the DSTree performs around 40% faster in spatio-temporal queries for small and medium datasets. Despite the advantages of our proposed framework, challenges such as 20% slower insertion speed or semantic query capabilities remain. We conclude that more significant research effort from GIScience and related fields in developing decentralized applications is needed. The need for the standardization of different geographic information when sharing data on the IPFS network is one of the requirements. Full article

People’s actions and behaviours contribute to the diversity and personality of a space, transforming it into a vibrant and thriving living environment. The main goal of this research is to present a GIS-based framework for assessing places. The framework is constructed based on the idea of conceptual spaces, integrating spatial and semantic concepts inside a geometric structure. The explanation of place-related concepts is achieved via the use of linear programming and convex polytopes. By projecting these concepts into the spatial domain, a strong connection between geographical and semantic space is established. This connection allows a wide range of analytical calculations using geographic information systems to be carried out. The study focuses on the sense of city centre in Tehran, Iran, by employing questionnaires administrated on-site to evaluate the correlation between identified city centres and the participants’ responses. The findings demonstrate a good correlation, as shown by a Pearson correlation value of 0.74 and a rank correlation coefficient of 0.8. Interestingly, the city centres that were selected did not always align with the geographic centre. However, participants still perceived them as city centres. This framework serves as a valuable tool for planners and policymakers, providing a comprehensive understanding of the built environment. By considering both semantic and geographical aspects, the framework emphasises the importance of emotions, memories, and meanings in creating an inclusive environment. Full article

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables. Full article

This study offers a quantitative solution that automates the creation of a historical timeline starting with old drawings from the beginning of the 18th century and ending with present-day photographs of the Old City of Jerusalem. This is performed using GIScience approaches, computer vision, and deep learning. The motivation to select the Old City of Jerusalem is the substantial availability of old archival drawings and photographs, owing to the area’s significance throughout the years. This task is challenging, as drawings, old photographs, and new photographs exhibit distinct characteristics. Our method encompasses several key components for the analyses: a 2D location recommendation engine, which detects an approximate location in the image of 3D landmarks; 2D landmarks to 3D conversion; and 2D polygonal areas to 3D GIS polylines conversion. This is applied to the segmentation of built areas. To achieve more accurate results, Meta’s Segment Anything model was utilized, which eliminates the need for extensive data preparation, training, and validation, thus optimizing the process. Using such techniques enabled us to examine the landscape development throughout the last three centuries and gain deeper insights concerning the evolution of prominent landmarks and features such as built area over time. Full article

Reproductive investment, including the number of offspring produced, is one of the fundamental characteristics of a species. It is particularly important for island vertebrates, which face a disproportionate number of threats to their survival, because it predicts, among other things, a species’ resilience to environmental disruption. Taxa producing more offspring recover more quickly from environmental perturbations and survive environmental change better. However, ecologists do not understand which primary drivers shape a species’ reproductive investment well. Here, we compare the reproductive efforts of 14 island populations of the Aegean Wall Lizard (Podarcis erhardii), which lives across widely diverging environmental conditions. We test three hypotheses, namely that reproductive investment (measured as clutch size, clutch volume) is (1) positively associated with predation risk [‘Predation Risk Hypothesis’]; (2) positively associated with the presence of reliable vegetation cover that provides shelter [‘Gravid Female Protection Hypothesis’]; and (3) limited by (and hence positively correlated with) food availability [‘Food Limitation Hypothesis’]. Although field data are somewhat consistent with all three hypotheses, statistical analyses provide strong support for the Predation Risk Hypothesis. The results not only shed light on which fundamental forces shape reproductive investment in island vertebrates, but can also help shape conservation priorities. Full article

Sea level rise (SLR) poses a significant threat to shorelines and the environment in terms of flooding densely populated areas and associated coastal ecosystems. Scenario analysis is often used to investigate potential SLR consequences, which can help stakeholders make informed decisions on climate change mitigation policies or guidelines. However, SLR scenario analysis requires considerable geospatial data analytics and repetitive execution of SLR models for alternative scenarios. Having to run SLR models many times for scenario analysis studies leads to heavy computational needs as well as a large investment of time and effort. This study explores the benefits of incorporating cyberinfrastructure technologies, represented by scientific workflows and high-performance computing, into spatially explicit SLR modeling. We propose a scientific workflow-driven approach to modeling the potential loss of marshland in response to different SLR scenarios. Our study area is the central South Carolina coastal region, USA. The scientific workflow approach allows for automating the geospatial data processing for SLR modeling, while repetitive modeling and data analytics are accelerated by leveraging high-performance and parallel computing. With support from automation and acceleration, this scientific workflow-driven approach allows us to conduct computationally intensive scenario analysis experiments to evaluate the impact of SLR on alternative land cover types including marshes and tidal flats as well as their spatial characteristics. Full article

The task of image retrieval is common in the world of data science and deep learning, but it has received less attention in the field of remote sensing. The authors seek to fill this gap in research through the presentation of a web-based landscape search engine for the US state of Wisconsin. The application allows users to select a location on the map and to find similar locations based on terrain and vegetation characteristics. It utilizes three neural network models—VGG16, ResNet-50, and NasNet—on digital elevation model data, and uses the NDVI mean and standard deviation for comparing vegetation data. The results indicate that VGG16 and ResNet50 generally return more favorable results, and the tool appears to be an important first step toward building a more robust, multi-input, high resolution landscape search engine in the future. The tool, called LSE Wisconsin, is hosted publicly on ShinyApps.io. Full article

The current research study aims to introduce the experience of implementing a serious game using the concept of game-based GIS approach for crisis management during earthquake disasters. In this study, we aimed to develop a game-based GIS approach and examine its efficiency for simulating earthquake rescue management in Tabriz city. In designing this game, typical scenario-based, game-based GIS methods and techniques were employed, and the proposed approach was applied to crisis management. To achieve this goal, we addressed the technical details regarding the development and implementation of the scenario-based and game-based GIS approach. Based on the results, game-based simulations can be considered an efficient approach for disaster simulation and can improve the skills of rescue teams. The outcome of this application is an intellectual game that almost all users at any age can play, and the game can challenge their ability to solve critical issues. The results are critical for explaining the effectiveness of rescue teams and crisis management facilities. As we intended to develop an approach for the simulation of earthquake disasters and emergency responses, we therefore conclude that the results of this study can also be employed to improve the skills of rescue teams and citizens for dealing with crises resulting from earthquake disasters. As a result of this research, the developed tool is published, together with this paper, as an open source and can be employed for any scenario-based analysis in other case studies. By presenting a-state-of-the-art approach, the results of this research study can provide significant contribution to further the development of GIScience and its applications for disaster and risk mitigation and management. Full article

Urban heat islands, characterized by higher temperatures in cities compared to surrounding areas, have been studied using various techniques. However, during heat waves, existing models often underestimate the intensity of these heat islands compared to empirical measurements. To address this, an hourly time-series-based model for predicting heat island intensity during heat wave conditions is proposed. The model was developed and validated using empirical data from the National Monitoring Network in Temuco, Chile. Results indicate a strong correlation (r > 0.98) between the model’s predictions and actual monitoring data. Additionally, the study emphasizes the importance of considering the unique microclimatic characteristics and built environment of each city when modelling urban heat islands. Factors such as urban morphology, land cover, and anthropogenic heat emissions interact in complex ways, necessitating tailored modelling approaches for the accurate representation of heat island phenomena. Full article

Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, the completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as “no building”, “complete” or “incomplete”. To assess the quality of the produced data, the completeness feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic approach to quantify completeness and with the prediction of an existing model. Our results show that the crowd-sourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent estimates for the case study regions while the other two approaches showed a higher variability. Our study also revealed that volunteers tend to classify nearly completely mapped tiles as “complete”, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery. Full article

As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO₂ equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort. Full article

Urban Green Spaces (UGS) contribute to the sustainable development of the urban ecosystem, positively impacting quality of life and providing ecosystem services and social benefits to inhabitants. For urban planning, mapping and quantification of UGS become crucial. So far, the contribution of private green spaces to ecosystem services in urban areas has yet to be studied. At the same time, in many Italian cities, they represent a considerable part of the urban green cover. This study utilises a methodological approach and provides insights into the contribution of urban public and private green spaces by the consideration of a case study area in Northeast Italy. To achieve this goal, the main steps were: (i) NDVI extraction from very high-resolution (20 cm) orthophotos, (ii) classification of property status and (iii) analysis of the degree of the greenness of land cover units. From our results, the total amount of the green spaces is 5.70 km², of which 72.1% (4.11 km²) is private, and 28.9% (1.59 km²) is public. As for the land cover, three NDVI classes were identified, highlighting different degrees of homogeneity in NDVI reflectance response within each urban land cover unit. These results will support the planning of new green areas in the post-epidemic National Recovery and Resilience Plan. Full article