Search

In recent decades, the creation and availability of Voluntary Geographic Information (VGI) have changed the paradigm associated with the production of Geospatial Information (GI), since, due to its free access, citizens can view, analyse, process, and validate this type of data. One of the most popular examples of VGI is the collaborative OpenStreetMap (OSM) project which covers a wide range of themes or characteristics associated with the real world. One of these themes is the feature "waterway" that represents watercourses. The quality of OSM data characteristics is a topic that has been published by many authors in recent years, particularly on the analysis of the completeness indicator. However, few references are found in the literature about studies that analyse the completeness of OSM watercourses or even watercourses obtained by other sources. All this motivated the authors to develop a study that aims to analyse the completeness of these specific lines that have so much relevance to hydrologists. The study presents an analysis of the variation over time in completeness/coverage of the OSM "waterway" feature in the period between 2014 and 2023 in a mountainous region included in the Mondego River basin, located in the Inland of Portugal. The methodology applied is supported by classical methods of measuring the completeness of lines that may be found in the literature. The total length of the watercourses was calculated and compared in percentage terms with the total length of the reference watercourses for dates under analysis. The watercourses of the military official hydrography of the 1/25,000 scale were used as a reference. The relation of the OSM completeness with some indicators related to terrain surface (altitude, slope, and location/proximity settlements) was also analysed. The choice of these indicators was motivated by the fact that the study area has strong mountain characteristics and is crossed by the main Portuguese river. The analysis was performed using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) data and satellite image of Geographic Information System software. The results show that the completeness of this OSM feature (waterway) has a slight increase, considering the amplitude of the studied period (nine years) and the fact that, nowadays, digital mobile devices enable easy access to satellite images, allowing the digitalization of geographic entities or objects of the real world remotely. Regarding the indicator altitude, slope, and location/proximity of the settlements, we believe that there is no influence of these indicators on the evolution of the completeness of the OSM waterways in the study area. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine the impact of vacuum impregnation on selected physical properties of courgettes, the drying process, and kinetics of the drying process. Vacuum impregnation was used as a pretreatment in the conducted research. The drying process was carried out using three techniques (convection drying, freeze drying, and vacuum drying). In the presented work, selected properties of courgettes, i.e., water activity, dry weight, density, VGI, shrinkage, and color were investigated, and the best model describing the kinetics of the drying process was selected. As a result of the study, it was found that the pretreated courgette was characterized by increased dry matter (0.44% to 4.08%) and density content (15.52% to 33.78%) and reduced or increasing water activity (−5.08 to 38.62%) depending on the drying method. The process also resulted in reduced drying shrinkage (−2.13% to −6.97%). Tomato juice was used as an impregnating solution, resulting in an increase in red intensity (8.44) and a decrease in the L* color index (80.16 to 58.00 for the fresh courgette). Dries with the most favorable properties were obtained using the freeze-drying method. The best model of the drying process kinetics was the logistic model. [ABSTRACT FROM AUTHOR]

ABSTRACTOpenStreetMap (OSM) has been playing increasingly important roles in location-based services, urban planning, tourism, and disaster management. In this paper, we examine a fundamental issue: can OSM reliably provide its services in the long run? To address this sustainable issue, we propose (1) inner cycle of career stages (i.e. activity levels) for monitoring the sustainable status of OSM and (2) critical mass theory for identifying driving factors that make OSM sustain. These new tools are used to track individual trajectories of editing behavior in 2005–2021. Our results suggest that OSM has been self-sustaining up to now and does not show a sign of decline yet. This is supported by the ever-decreasing turnover time and stabilized turnover rate. Together with the finding that most contributors transitioned to a higher career stage within a month, we identify an accelerated socialization process within OSM. We find further that it is the critical mass of people rather than content that makes OSM sustain. This contrasts with the common wisdom: recruiting more people with small contributions is far more important to the long-term growth of OSM than relying on a group of core members with large contributions. Implications and recommendations are critically discussed.

ABSTRACTEarth observation data provides valuable information and support along the disaster management cycle. However, information from satellite remote sensing is often not available in the first hours a crisis occurs, due to several reasons, e.g. pre-defined acquisition times, cloud coverage, downlink capacities. To fill this time gap and add value to the incoming results from remote sensing data, ancillary datasets such as Twitter data become useful to enrich data and get insights into events by leveraging their spatio-temporal and thematic references. However, the main disadvantage of using Twitter data is the noise that is introduced into analyses by these data. Among other reasons, this is mainly caused by the use of insignificant search criteria that are used to harvest the data, that often result in irrelevant, noisy data (e.g. using insignificant keywords or incorrect geotags to filter data). This paper presents a method to identify crisis-event specific signal words, that are then used together with Part Of Speech (POS) tagging to filter the Twitter streams, and gather crisis-event specific data. These data are then used to estimate the location hotspots of the crisis events. The developed methods are applied as a proof-of-concept to determine flood events in May of 2022 .

In recent decades, the creation and availability of Voluntary Geographic Information (VGI) have changed the paradigm associated with the production of Geospatial Information (GI), since, due to its free access, citizens can view, analyse, process, and validate this type of data. One of the most popular examples of VGI is the collaborative OpenStreetMap (OSM) project which covers a wide range of themes or characteristics associated with the real world. One of these themes is the feature “waterway” that represents watercourses. The quality of OSM data characteristics is a topic that has been published by many authors in recent years, particularly on the analysis of the completeness indicator. However, few references are found in the literature about studies that analyse the completeness of OSM watercourses or even watercourses obtained by other sources. All this motivated the authors to develop a study that aims to analyse the completeness of these specific lines that have so much relevance to hydrologists. The study presents an analysis of the variation over time in completeness/coverage of the OSM “waterway” feature in the period between 2014 and 2023 in a mountainous region included in the Mondego River basin, located in the Inland of Portugal. The methodology applied is supported by classical methods of measuring the completeness of lines that may be found in the literature. The total length of the watercourses was calculated and compared in percentage terms with the total length of the reference watercourses for dates under analysis. The watercourses of the military official hydrography of the 1/25,000 scale were used as a reference. The relation of the OSM completeness with some indicators related to terrain surface (altitude, slope, and location/proximity settlements) was also analysed. The choice of these indicators was motivated by the fact that the study area has strong mountain characteristics and is crossed by the main Portuguese river. The analysis was performed using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) data and satellite image of Geographic Information System software. The results show that the completeness of this OSM feature (waterway) has a slight increase, considering the amplitude of the studied period (nine years) and the fact that, nowadays, digital mobile devices enable easy access to satellite images, allowing the digitalization of geographic entities or objects of the real world remotely. Regarding the indicator altitude, slope, and location/proximity of the settlements, we believe that there is no influence of these indicators on the evolution of the completeness of the OSM waterways in the study area.

The purpose of this study was to determine the impact of vacuum impregnation on selected physical properties of courgettes, the drying process, and kinetics of the drying process. Vacuum impregnation was used as a pretreatment in the conducted research. The drying process was carried out using three techniques (convection drying, freeze drying, and vacuum drying). In the presented work, selected properties of courgettes, i.e., water activity, dry weight, density, VGI, shrinkage, and color were investigated, and the best model describing the kinetics of the drying process was selected. As a result of the study, it was found that the pretreated courgette was characterized by increased dry matter (0.44% to 4.08%) and density content (15.52% to 33.78%) and reduced or increasing water activity (−5.08 to 38.62%) depending on the drying method. The process also resulted in reduced drying shrinkage (−2.13% to −6.97%). Tomato juice was used as an impregnating solution, resulting in an increase in red intensity (8.44) and a decrease in the L* color index (80.16 to 58.00 for the fresh courgette). Dries with the most favorable properties were obtained using the freeze-drying method. The best model of the drying process kinetics was the logistic model.

This paper focuses on social (informal, interpersonal) relationships and identifies their positive effects on business activity in – a rarely empirically analyzed – the video game industry. Given the lack of previous research on the positive business effects of social relationships in the chosen empirical context, qualitative and explorative research was conducted. To address the research goals, seventeen semi-structured in-depth interviews and one focus group interview with six video game developers were carried out. The interviews were analyzed using inductive coding procedure. The key contributions are as follows: (1) specific industry characteristics determine the relevance of social relationships for business activity; (2) among the key actors maintaining such relationships are: consumers (i.e., gamers), business partners (investors, contractors, and co-operators), employees (past and current), and competitors; (3) industry-specific social relationships benefits are identified by comparing them with the general social relationships benefits indicated in the literature, detailing them in the chosen empirical context, and addressing the type of actors with which social links are exploited. Game developers can realize with whom to maintain social relations within video game industry to generate particular business benefits, for example, better promotion and higher game sales, building a proper firm's image. The identification of actors of social relationships diverse in terms of establishing and intentional usage of social relationships, the recognition of the specific business-related benefits of maintaining social relationships by video game developers, and providing empirical evidence of the industry-wide determinants of the benefits of maintaining social relationships. [ABSTRACT FROM AUTHOR]

Currently, a large part of the population has access to location applications, consequently, any individual can become a producer of Voluntary Geographic Information (VGI). In view of this, the objective of this work is to carry out a collaborative mapping of the living spaces of Geography students at Federal University of Santa Maria, in order to understand the social, economic and leisure relationships that they experience in Santa Maria (RS) and region. The project was developed in the Canvis application and the points were entered in QGIS 3.4. Students can be divided into two groups: one of those who live with their families, live in peripheral neighborhoods and use businesses such as large hypermarket chains; and another of those who use real estate services, since they lived in municipalities in the country side, moving to the Centro and Camobi neighborhoods, with access to medium to small businesses. The VGI data demonstrated the importance of the University as a local and regional development factor. [ABSTRACT FROM AUTHOR]

The drying process is used in the food industry to extend the shelf life of fruits and vegetables without the use of preservatives. As quality, visual, and aroma characteristics are important determinants of consumer interest, they play a key role in the development of new foods. In the present study, vacuum impregnation (VI) was used prior to vacuum drying (VD) and freeze drying (FD) of courgette and broccoli. Organic beet juice was used to produce the novel snacks. The study showed that the use of vacuum impregnation significantly affected the VOCs profile (volatile organic compounds profile), in which the following compounds were found: viz: 2-(E)-hexen-1-ol, 2-(Z)-hexen-1-ol and aceto-phenone. VI caused a decrease in volumetric gel index (VGI), drying shrinkage (S), water activity (AW), decreased color saturation (∆C), and increased dry matter content (DM). All these properties testify to the positive effect of the pretreatment used. The drying methods used had a significant effect on the properties of the dried vegetables. The dries obtained by the FD method showed higher density and water activity, as well as better preserved color (lower ∆E) and higher VOCs, so it is considered that freeze drying is a suitable method for obtaining novel courgette and broccoli snacks. [ABSTRACT FROM AUTHOR]

Social media is widely used to share real-time information and report accidents during natural disasters. Named entity recognition (NER) is a fundamental task of geospatial information applications that aims to extract location names from natural language text. As a result, the identification of location names from social media information has gradually become a demand. Named entity correction (NEC), as a complementary task of NER, plays a crucial role in ensuring the accuracy of location names and further improving the accuracy of NER. Despite numerous methods having been adopted for NER, including text statistics-based and deep learning-based methods, there has been limited research on NEC. To address this gap, we propose the CTRE model, which is a geospatial named entity recognition and correction model based on the BERT model framework. Our approach enhances the BERT model by introducing incremental pre-training in the pre-training phase, significantly improving the model's recognition accuracy. Subsequently, we adopt the pre-training fine-tuning mode of the BERT base model and extend the fine-tuning process, incorporating a neural network framework to construct the geospatial named entity recognition model and geospatial named entity correction model, respectively. The BERT model utilizes data augmentation of VGI (volunteered geographic information) data and social media data for incremental pre-training, leading to an enhancement in the model accuracy from 85% to 87%. The F1 score of the geospatial named entity recognition model reaches an impressive 0.9045, while the precision of the geospatial named entity correction model achieves 0.9765. The experimental results robustly demonstrate the effectiveness of our proposed CTRE model, providing a reference for subsequent research on location names. [ABSTRACT FROM AUTHOR]

O trabalho utiliza a tecnologia de geoinformação como ferramenta para a análise das dinâmicas urbanas e suas particularidades na cidade de Divinópolis, localizada no centro-oeste do estado de Minas Gerais, de forma a compreender as potencialidades existentes no município para o estímulo ao desenvolvimento dos setores de lazer e cultura. Para tanto, parte da caracterização espacial na área municipal, seguida do detalhamento no recorte da mancha urbana, mapeando condições ambientais, culturais, históricas, econômicas e sociais, a fim de identificar possíveis carências e pontos de interesse para o desenvolvimento de propostas de requalificação urbana e revitalização dos espaços de uso público para a população. Inova no método de trabalho, pois aplica o roteiro: 1) caracterização segundo variáveis principais por geoprocessamento; 2) coleta de opinião cidadã por mapeamento voluntariado por VGI – Volunteered Geographic Information; 3) integração de variáveis por Análise de Multicritérios para análise técnica das áreas com potencialidade (indicação) e vulnerabilidade social (necessidade); 4) comparação do resultado da escuta cidadã com abordagem técnica de indicação de lugares ótimos; 5) escolha da área de projeto e desenvolvimento de proposta de desenho urbano. É, desta forma, uma contribuição metodológica para o ensino do papel do arquiteto urbanista ou geógrafo como um decodificador da vontade cidadã e orquestrador de análise técnica, seguido de atuação autoral no desenho de propostas. Desenvolvido no âmbito de atividades didáticas na EA-UFMG, é uma contribuição à formação do arquiteto-urbanista ou geógrafo.

OpenStreetMap (OSM) as one of the most successful projects of Volunteered Geographical Information (VGI) has attracted millions of contributors to work together and produces massive open geographical data. However, the co-work does not always run smoothly since mapping can involve conflicted understandings of the reality. In this paper, we investigate behaviors of mapping related to territorial disputes to reveal the characteristics of contributions and examine the contradictions between ground truth as the vision of OSM and the theory of critical cartography. We perform our experiments from the perspectives of entities, changesets, and contributors using the full history data of OSM. The experiments show that territorial-dispute-related contributions have substantially different characteristics from various aspects but they cannot be treated as outliers either, considering that most contributors do not focus on disputed boundaries. Interpreting OSM data as a converging state to ground truth or equally opinions can both be inaccurate. We also find that mapping disputes may not be absolutely negative in a VGI project. We perform quantitative, large-scale (global) analysis of dispute-related mapping. The results show that territorial-dispute-related contributions and contributors are different from contributions and contributors in general. Territorial-dispute-related mapping is not an independent phenomenon for OSM. The contributors make much more disputes-unrelated contributions. Dispute-related entities have more (divergent) versions than normal boundaries, attract more participants, and are more semantically complete, especially for names. Dispute-related changesets generally attract more discussions. The spatial distribution of the dispute-related changesets is consistent with real-world territorial disputes and very different from that of all boundary-related changesets and all changesets. Contributors who participate in dispute-related contributions are generally more active. These users tend to have a special interest in boundaries but most of them do not focus on disputed boundaries. [ABSTRACT FROM AUTHOR]

The paper presents a vehicle-to-grid (V2G) technology. On an example of a vehicle fleet using the V2G technology, the ability of electric vehicles (EVs) is demonstrated to participate in flexibility services to ensure the grid stability. A simple simulation tool in Python with the PuLP library, is developed to calculate the optimal scheduling of the EVs charging and discharging. The optimization written with linear programming assures minimum number of EV charging and discharging hours while meeting the EVs and grid requirements. From the aggregator's point of view, we found that the most suitable use case is the one with the EVs charged at 20% at plug-in and the required state of charge at disconnection at 80%. [ABSTRACT FROM AUTHOR]

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. The latest advances in spatial information technology have led to the emergence of Volunteered Geographic Information (VGI) as enrichment to existing spatial data sources. Additionally, Decision Support Systems (DSS) are among the fields that have seen major advances. Volunteered Geographic Information (VGI) has great potential as a valuable data source to decision support systems. Several studies have been proposed to integrate VGI data into DSS. However, as VGI data may have different levels of quality, integrating VGI data with poor quality may affect the decision-making process. In fact, VGI data with poor quality. that are obsolete or incomplete, could, if integrated into a spatial DSS, lead to inappropriate analysis results. This paper presents an approach that aims to enhance spatial DSS analysis and exploitation by integrating high quality VGI data that are appropriate to the user requirements, and that have a good indicator completeness and time relevance. The approach introduces a conceptual framework that evaluates VGI data quality and integrates only high quality VGI data into spatial DSS. The proposed approach is experimented on a road maintenance project in Grand-Tunis. We develop the Map-Report prototype, and we evaluate the efficiency of our approach in enhancing data analysis and exploitation in spatial DSS by reducing the error rate and providing accurate and precise analysis results. [ABSTRACT FROM AUTHOR]

Floods are among the most frequent and costly rainfall-triggered disasters. In this context, geospatial content generated by non-professionals using geolocated systems offers the possibility of monitoring environmental events. This study shows a statistical correlation between in situsensors, radar, Twitter posts, and flooding events. Furthermore, we observed in this study that flooding-related keywords are statistically more significant on flooding days than on non-flooding days and reinforce that Twitter can be employed as a complementary data source for flood management systems. [ABSTRACT FROM AUTHOR]

Earth observation data provides valuable information and support along the disaster management cycle. However, information from satellite remote sensing is often not available in the first hours a crisis occurs, due to several reasons, e.g. pre-defined acquisition times, cloud coverage, downlink capacities. To fill this time gap and add value to the incoming results from remote sensing data, ancillary datasets such as Twitter data become useful to enrich data and get insights into events by leveraging their spatio-temporal and thematic references. However, the main disadvantage of using Twitter data is the noise that is introduced into analyses by these data. Among other reasons, this is mainly caused by the use of insignificant search criteria that are used to harvest the data, that often result in irrelevant, noisy data (e.g. using insignificant keywords or incorrect geotags to filter data). This paper presents a method to identify crisis-event specific signal words, that are then used together with Part Of Speech (POS) tagging to filter the Twitter streams, and gather crisis-event specific data. These data are then used to estimate the location hotspots of the crisis events. The developed methods are applied as a proof-of-concept to determine flood events in May of 2022. [ABSTRACT FROM AUTHOR]

This paper examines the toponymic heritage used in Kenya's Authoritative Geographic Information (AGI) toponyms database of 26,600 gazetteer records through documentation and characterization of meanings of place names in topographic mapping. A comparison was carried out between AGI and GeoNames and between AGI and OpenStreetMap (OSM) volunteered records. A total of 15,000 toponymic matchings were found. Out of these, 1567 toponyms were then extracted for further scrutiny using AGI data in the historical records and from respondents on toponyms' meanings. Experts in toponymy assisted in verifying these data. From the questionnaire responses, 235 names occurred in more than one place while AGI data had 284. The elements used to characterize the toponyms included historical perceptions of heritage evident in toponyms in their localities, ethnographic, toponymical and morphology studies on Kenya's dialects. There was no significant relationship established between the same place name usages among dialects as indicated by a positive weak correlation r (438), = 0.166, p < 0.001 based on the effect of using the related places and the distance between related places. The weak correlation implies that the one name one place principle does not apply due to diverse language boundaries, strong bonds associated with historical toponyms in the form of heritage and significant variations on how names resist changes to preserve their heritage. [ABSTRACT FROM AUTHOR]

Spatial data infrastructure (SDI) is a system that supports the management and use of geospatial data and related resources. It involves the creation and maintenance of a network of organizations, people, and technology that enables the sharing of geospatial data across sectors and stakeholders. In recent years, the growth of geospatial data and the increasing reliance on it by various sectors has led to the emergence of new trends in SDI, such as the use of cloud computing and big data analytics, the integration of geospatial data with other types of data, and the emphasis on open data and data interoperability. Volunteered geographic information (VGI) refers to geospatial data that is collected and contributed by individuals or groups, rather than traditional sources using the application of web 2.0 and location based applications, social media, mobile devices or say citizens as the censors. Crowdsourcing in geospatial data generation concept of VGI has changed the traditional concept of SDI having one way relationship as producers and users to the user driven SDI, where user create diverse, high quality data (spatial, temporal, attribute) and also use the data interoperable, transparently, world widely and free of cost. Various authors have discussed about the application of VGI in the world of the digital data and also point outs the possibility of integration of VGI in SDI as the starting of the new generation of SDI in the form of Global GIS platform, Data Spaces, System of Systems (SoS), Geoverse, Digital Earth, Digital Twin, Virtual Geographic Environment (VGEs). However, there exists multiple VGI challenges such as data quality, data structure, data differentiation, data copyright, and data confidentiality and privacy, but with the proper cooperation and partnerships, policy and legal arrangements, standard developments, financial arrangements, inter/intra communication and added advantages of web 3.0, concept of Global Digital Ecosystem containing Geoverse, SDI and SoS is possible. Hence, VGI is the present and also the future in this SDI continuum. [ABSTRACT FROM AUTHOR]

The purpose of this article was to provide the user with information about the number of buildings in the analyzed OpenStreetMap (OSM) dataset in the form of data completeness indicators, namely the standard OSM building areal completeness index (C Index), the numerical completeness index (COUNT Index) and OSM building location accuracy index (TP Index). The official Polish vector database BDOT10k (Database of Topographic Objects) was designated as the reference dataset. Analyses were carried out for Piaseczno County in Poland, differentiated by land cover structure and urbanization level. The results were presented in the form of a bivariate choropleth map with an individually selected class interval suitable for the statistical distribution of the analyzed data. The results confirm that the completeness of OSM buildings close to 100% was obtained mainly in built-up areas. Areas with a commission of OSM buildings were distinguished in terms of area and number of buildings. Lower values of completeness rates were observed in less urbanized areas. The developed methodology for assessing the quality of OSM building data and visualizing the quality results to assist the user in selecting a dataset is universal and can be applied to any OSM polygon features, as well as for peer review of other spatial datasets of comparable thematic scope and detail. [ABSTRACT FROM AUTHOR]

The Portuguese Land Cover Monitoring System SMOS (Sistema de Monitorização da Ocupação do Solo) is an initiative conceived and developed by Direção-Geral do Território (DGT) with the aim of continuously produce reference and thematic cartographic products on land use land cover for continental Portugal. SMOS is a collaborative and multifunctional system that involves the Public Administration, the national scientific and technological system, the private sector and the citizen, guided by user needs and open data policy. SMOS represents a paradigm shift in cartography production and uses the latest developments in space technologies and Artificial Intelligence to create maps with more detail, quality and promptness. Here, SMOS is presented, including the initial steps leading to the implementation of the system, its organization and governance, cartographic products and viewers, and foreseen developments. [ABSTRACT FROM AUTHOR]

One of the issues brought on by rapid urbanization is the loss of livability owing to poor waste management, a lack of resources, air pollution, traffic congestion, and outdated buildings and public infrastructure. The idea of a smart city has arisen as one of the potential solutions to this concern. In this study, we have demonstrated how citizen science helps immensely to the growth of a smart city by crowdsourcing data about urban decay. Tehran, the capital of Iran, which is home to the history of many people’s social lives, has begun losing some of its vitality. Municipalities have plans for dealing with urban decline and revival, but we indicate that volunteered geographic information (VGI) would be more effective in turning such ideas into practice. According to our research, there are three different types of organizations active in urban renewal: municipalities, civil and urban planning agencies, and organizations that support tourism and cultural heritage. As a result, we provided a system design that outlines the duties that each institution has in relation to urban renewal. In addition, we suggested a service broker with the capacity to publish VGI in order to offer interoperability related to geographic data and services based on spatial data infrastructure (SDI). Beyond that, various types of urban decay, necessary management procedures, the sequence of information in system components, and the volunteer’s behavior confronting the system were all specified in the form of an object-oriented design. A prototype system based on state-of-the-art in open-source technologies was developed to test our hypothesis. The VGI system enabled us to monitor urban decay events in real time using the management dashboard. We have shown that volunteers can distinguish between urban declines and typical instances like a binary classifier in the quality control method proposed in this study. Performance was 90% based on the findings of the quality control. A running VGI system would start beneficence after 3 months and make a profit of 150% after a year, based on our estimates. [ABSTRACT FROM AUTHOR]

The purpose of this article was to provide the user with information about the number of buildings in the analyzed OpenStreetMap (OSM) dataset in the form of data completeness indicators, namely the standard OSM building areal completeness index (C Index), the numerical completeness index (COUNT Index) and OSM building location accuracy index (TP Index). The official Polish vector database BDOT10k (Database of Topographic Objects) was designated as the reference dataset. Analyses were carried out for Piaseczno County in Poland, differentiated by land cover structure and urbanization level. The results were presented in the form of a bivariate choropleth map with an individually selected class interval suitable for the statistical distribution of the analyzed data. The results confirm that the completeness of OSM buildings close to 100% was obtained mainly in built-up areas. Areas with a commission of OSM buildings were distinguished in terms of area and number of buildings. Lower values of completeness rates were observed in less urbanized areas. The developed methodology for assessing the quality of OSM building data and visualizing the quality results to assist the user in selecting a dataset is universal and can be applied to any OSM polygon features, as well as for peer review of other spatial datasets of comparable thematic scope and detail.

Timely mapping of flooded areas is critical to several emergency management tasks including response and recovery activities. In fact, flood crisis maps embed key information for an effective response to the natural disaster by delineating its spatial extent and impact. Crisis mapping is usually carried out by leveraging data provided by satellite or airborne optical and radar sensors. However, the processing of these kinds of data demands experienced visual interpretation in order to achieve reliable results. Furthermore, the availability of in situ observations is crucial for the production and validation of crisis maps. In this context, a frontier challenge consists in the use of Volunteered Geographic Information (VGI) as a complementary in situ data source. This paper proposes a procedure for flood mapping that integrates VGI and optical satellite imagery while requiring limited user intervention. The procedure relies on the classification of multispectral images by exploiting VGI for the semi-automatic selection of training samples. The workflow has been tested with photographs and videos shared on social media (Twitter, Flickr, and YouTube) during two flood events and classification consistency with reference products shows promising results (with Overall Accuracy ranging from 87% to 93%). Considering the limitations of social media-sourced photos, the use of QField is proposed as a dedicated application to collect metadata needed for the image classification. The research results show that the integration of high-quality VGI data and semi-automatic data processing can be beneficial for crisis map production and validation, supporting crisis management with up-to-date maps. [ABSTRACT FROM AUTHOR]

Volunteered geographic information (VGI) is often cited as a potential solution to persistent global inequalities in map data, particularly in areas undergoing humanitarian crises. Poor volunteer engagement, slow data production, and low-quality outputs have limited progress, however, and can unintentionally exaggerate inequalities. Hybrid machine learning–VGI (ML–VGI) frameworks can help to overcome these challenges through a combination of workflow automation and purposive human input, but the use of these workflows is rare in practice. Here, we implement an ML–VGI framework (Centaur VGI) and undertake a detailed comparative usability assessment against an existing, widely used VGI mapping platform to demonstrate its potential to improve volunteer engagement, mapping speed, and data quality. Our results suggest that through automated building, searching, and labeling, the Centaur VGI platform provides greater usability, quicker data production, and improved data quality for most users. Consequently, we provide the first evidence that hybrid ML–VGI approaches can be used to facilitate increased public participation in humanitarian building mapping efforts and thus help reduce global inequalities in map data. [ABSTRACT FROM AUTHOR]

Due to their status as non-professionals, the reputations of Volunteered Geographic Information (VGI) contributors have a very important impact on data quality. In the process of contributor reputation evaluation in OpenStreetMap (OSM), it is very difficult to calculate the semantic similarity between object versions contributed by volunteers. Aimed at this issue, this paper proposes a model of contributor's reputation based on semantic similarity of ontology concepts. Firstly, contributors are classified into three categories based on an improved WPCA and classification method. Then, an initial reputation is set for every OSM user in each class according to these categories and related research. Secondly, the related concept ontology is constructed for OSM entities; then, the semantic similarity of the object version is calculated according to the similarity of concept attributes and the semantic distance of concept. The contributor's evaluation reputation is computed by synthesizing the semantic similarity, geometric similarity, and topological similarity of object versions. Thirdly, the contributor's evaluation reputation and the initial reputation is aggregated to obtain the contributor's reputation; finally, the OSM data of Rutland, England, is used as an example to verify the validity of our model. The experimental results show that the proposed model can obtain a more comprehensive contributor's evaluation by fusing with the semantic similarity of ontology concept. The evaluation bias caused only by the semantic change between versions can be eliminated. Moreover, the obtained user's reputation is positively correlated with the data quality. The contributor's reputation evaluation method proposed in this paper is an effective method for evaluating the contributor's reputation in OSM-like systems. [ABSTRACT FROM AUTHOR]

Due to the occurrence of extreme weather phenomena, flooding caused by precipitation has become a frequent, widespread, and highly destructive natural disaster. For densely populated urban areas, efficient identification of people's emergency rescue needs and mapping of high flood risk areas are particularly important for decision makers to formulate disaster relief strategies. In this study, we employed a machine learning approach along with volunteer geographic information (VGI)1 data and environmental-socio-economic indicators to map the flood risk area in Zhengzhou. Firstly, the sentiment analysis tool based on artificial intelligence platform initially identified the public's emergency rescue needs. Secondly, the crowd-sourcing approach and geographical approach were used to evaluate the VGI data quality and the variable importance measures (VIMs)2 method to optimize the evaluation indicators. Lastly, the flood risk area was classified in four categories: flood environmental susceptibility, flood-induced risk, flood exposure risk, and flood mitigation capacity, subsequently resulting in a comprehensive risk map of Zhengzhou. The results showed that the accuracy of flood risk assessment increased from 85.7 % to 92.4 % after VGI quality evaluation. The most important indicators affecting flood risk assessment were population density, river system and nighttime light intensity. The comprehensive flood risk map better reflected the spatial distribution of high-risk areas in Zhengzhou. It allows emergency responders to quickly identify areas that require urgent attention. [ABSTRACT FROM AUTHOR]

Social media is widely used to share real-time information and report accidents during natural disasters. Named entity recognition (NER) is a fundamental task of geospatial information applications that aims to extract location names from natural language text. As a result, the identification of location names from social media information has gradually become a demand. Named entity correction (NEC), as a complementary task of NER, plays a crucial role in ensuring the accuracy of location names and further improving the accuracy of NER. Despite numerous methods having been adopted for NER, including text statistics-based and deep learning-based methods, there has been limited research on NEC. To address this gap, we propose the CTRE model, which is a geospatial named entity recognition and correction model based on the BERT model framework. Our approach enhances the BERT model by introducing incremental pre-training in the pre-training phase, significantly improving the model’s recognition accuracy. Subsequently, we adopt the pre-training fine-tuning mode of the BERT base model and extend the fine-tuning process, incorporating a neural network framework to construct the geospatial named entity recognition model and geospatial named entity correction model, respectively. The BERT model utilizes data augmentation of VGI (volunteered geographic information) data and social media data for incremental pre-training, leading to an enhancement in the model accuracy from 85% to 87%. The F1 score of the geospatial named entity recognition model reaches an impressive 0.9045, while the precision of the geospatial named entity correction model achieves 0.9765. The experimental results robustly demonstrate the effectiveness of our proposed CTRE model, providing a reference for subsequent research on location names.

The latest advances in spatial information technology have led to the emergence of Volunteered Geographic Information (VGI) as enrichment to existing spatial data sources. Additionally, Decision Support Systems (DSS) are among the fields that have seen major advances. Volunteered Geographic Information (VGI) has great potential as a valuable data source to decision support systems. Several studies have been proposed to integrate VGI data into DSS. However, as VGI data may have different levels of quality, integrating VGI data with poor quality may affect the decision-making process. In fact, VGI data with poor quality. that are obsolete or incomplete, could, if integrated into a spatial DSS, lead to inappropriate analysis results. This paper presents an approach that aims to enhance spatial DSS analysis and exploitation by integrating high quality VGI data that are appropriate to the user requirements, and that have a good indicator completeness and time relevance. The approach introduces a conceptual framework that evaluates VGI data quality and integrates only high quality VGI data into spatial DSS. The proposed approach is experimented on a road maintenance project in Grand-Tunis. We develop the Map-Report prototype, and we evaluate the efficiency of our approach in enhancing data analysis and exploitation in spatial DSS by reducing the error rate and providing accurate and precise analysis results.

Data from GPS-based fitness tracker apps have become a prominent source for studying cycling behavior. This type of crowd-sourced data provides larger datasets than were previously attainable by travel surveys and cyclist counts, which allows for the comparison of trip characteristics between geographic regions and the study of temporal trends in bicycle ridership. Researchers acknowledge that different types of biases come with tracking data from fitness tracker apps, such as self-selection bias, which is partially based on different target audiences among fitness tracker apps. This begs the question as to whether the behavior of cyclists, and thus the characteristics of routes traveled, varies among the apps. To provide a first insight into this question, this research analyzes trips reported on three fitness tracker apps, Bikemap, Endomondo, and MapMyFitness, for South Florida (Miami-Dade, Broward, and Palm Beach counties) and North Holland. Comparison of trip characteristics is made among the three apps and across both study regions. Results show that cycling behavior observed in the three apps is similar relative to a set of control trips in each region (e.g. fewer primary roads than reference trips observed), but that there are some pronounced differences in trips recorded with the different apps between both regions. This suggests that geographic region plays a role in how trip characteristics recorded on different apps compare to each other, demonstrating the presence of an additional aspect of geographic bias in crowd-sourced cycling data. [ABSTRACT FROM AUTHOR]

Cryptococcus gattii is an underrecognized cause of meningitis, especially in nonendemic regions. This report details C gattii disease progression from admission to autopsy in an otherwise healthy 40-year-old male in Texas. It brings awareness to an often unsuspected organism that can cause severe infection requiring early recognition and treatment in immunocompetent individuals. [ABSTRACT FROM AUTHOR]

Pedestrian and vehicular navigation relies mainly on Global Navigation Satellite System (GNSS). Even if different navigation systems are integrated, GNSS positioning remains the core of any navigation process as it is the only system capable of providing independent solutions. However, in harsh environments, especially urban ones, GNSS signals are confronted by many obstructions causing the satellite signals to reach the receivers through reflected paths. These No-Line of Sight (NLOS) signals can affect the positioning accuracy significantly. This contribution proposes a new algorithm to detect and exclude these NLOS signals using 3D building models constructed from Volunteered Geographic Information (VGI). OpenStreetMap (OSM) and Google Earth (GE) data are combined to build the 3D models incorporated with GNSS signals in the algorithm. Real field data are used for testing and validation of the presented algorithm and strategy. The accuracy improvement, after exclusion of the NLOS signals, is evaluated employing phase-smoothed code observations. The results show that applying the proposed algorithm can improve the horizontal positioning accuracy remarkably. This improvement reaches 10.72 m, and the Root Mean Square Error (RMSE) drops by 1.64 m (46 % improvement) throughout the epochs with detected NLOS satellites. In addition, the improvement is analyzed in the Along-Track (AT) and Cross-Track (CT) directions. It reaches 6.89 m in the AT direction with a drop of 1.076 m in the RMSE value, while it reaches 8.64 m with a drop of 1.239 m in the RMSE value in the CT direction. [ABSTRACT FROM AUTHOR]

Floods are among the most frequent and costly rainfall-triggered disasters. In this context, geospatial content generated by non-professionals using geolocated systems offers the possibility of monitoring environmental events. This study shows a statistical correlation between in situsensors, radar, Twitter posts, and flooding events. Furthermore, we observed in this study that flooding-related keywords are statistically more significant on flooding days than on non-flooding days and reinforce that Twitter can be employed as a complementary data source for flood management systems.

One potential source of geospatial open data for monitoring sustainable development goals (SDG) indicators is OpenStreetMap (OSM). The purpose of this paper is to provide a comprehensive evaluation of the spatial data quality elements of OSM against the national official data—the database of topographic objects at a scale of 1:10,000. Such spatial data quality elements as location accuracy, data completeness and attribute compatibility were analysed. In the conducted OpenStreetMap tests, basic land-cover classes such as roads, railroads, river network, buildings, surface waters and forests were analysed. The test area of the study consisted of five counties in Poland, which differ in terms of location, relief, surface area and degree of urbanization. The best results of the quality of OSM spatial data were obtained for highly urbanized areas with developed infrastructure and a high degree of affluence. The highest degree of completeness of OSM linear and area objects in the studied counties was acquired in Piaseczyński County (82%). The lowest degree of completeness of the line and area objects of OSM in the studied counties was obtained in the Ostrowski County (51%). The calculated correlation coefficient between the quality of OSM data and the income per capita in the county was 0.96. The study complements the previous research results in the field of quantitative analysis of the quality of OSM data, and the obtained results confirm their dependence on the geometric type of the analysed objects and characteristics of test areas, i.e., in this case counties in Poland. The obtained results of OSM data quality analysis indicate that OSM data may provide strong support for other spatial data, including official and state data. OSM stores significant amounts of geospatial data with relatively high data quality that can be a valuable source for monitoring some SDG indicators. [ABSTRACT FROM AUTHOR]

In recent years, the use of crowdsourcing has positively transformed the way geographic information is collected, stored and analyzed. Many countries have promoted and funded research into the potential of using crowdsourcing in various fields of governance. This paper focuses on developing a methodology for fast, low-cost and reliable coastal management for touristic purposes in Greece. In particular, a group of a professional surveyor have developed the methodology and trained two volunteers to collect a variety of data points of interest about a public coastal zone, such as the area size of free and unused public space, rocky areas, parking spaces (organized or not), land use types, build up and green areas, municipal lighting, pedestrian crossing points, beach umbrellas, path routes, street furniture, etc. A pilot case study was compiled for a part of the Athenian Riviera to check the methodology. Derived conclusions point out that the developed methodology may be successfully used for managing the 16,000 km length of the coastal zone of Greece for touristic purposes. Considerations for further improvements to the methodology are given.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.