Volunteered Geographic Information Research Articles

Beyond extraction accuracy: addressing the quality of geographical named entity through advanced recognition and correction models using a modified BERT framework

ABSTRACT In the realm of geospatial services and applications, the accuracy of address information is of utmost importance. Traditional methods of data collection, being both labor-intensive and costly, have prompted researchers to turn to Volunteered Geographic Information (VGI) for the extraction of Geographical Named Entity (GNE).Notwithstanding, prior studies have predominantly concentrated on enhancing extraction accuracy, while often overlooking the critical aspect of GNE quality. This study addresses this gap by employing a multifaceted approach. Initially, a Geographical Named Entity Semantic Model (GNESM) was constructed by improving the BERT framework and conducting ablation experiments on multiple influencing factors to verify its feasibility. Based on GNESM, a Geographical Named Entity Recognition Model (GNERM) was constructed by incremental pre-training with social media text data and fine-tuning to achieve a recognition accuracy of 90.9%. Subsequently, a Geographical Named Entity Error Correction Model (GNEECM) was constructed by training GNESM with standard GNE data and incorporating error detection and correction modules, achieving a remarkable accuracy of 96.6% in error detection and correction tasks. The experimental results convincingly demonstrate that the proposed identification and correction methods outperform all compared methods. Through the identification and correction process, this study successfully obtained high-quality GNE data, providing a reference for expanding standard address libraries and subsequent research on geographic named entity.

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis

Natural hazards can present a significant risk to road infrastructure. This infrastructure is a fundamental component of the transportation infrastructure, with significant importance. During emergencies, society heavily relies on the functionality of the road infrastructure to facilitate evacuation and access to emergency facilities. This study introduces a versatile, multi-scale framework designed to analyze accessibility within road networks during natural hazard scenarios. The first module of the framework focuses on assessing the influence of natural hazards on road infrastructure to identify damaged or blocked road segments and intersections. It relies on near real-time information, often provided by citizen science through Volunteered Geographic Information (VGI) data and Natural Language Processing (NLP) of VGI texts. The second module conducts network analysis based on freely available Open Street Map (OSM) data, differentiating between intact and degraded road networks. Four accessibility measures are employed: betweenness centrality, closeness centrality, a free-flow assumption index, and a novel alternative routing assumption measure considering congestion scenarios. The study showcases its framework through an exemplary application in California, the United States, considering different hazard scenarios, where degraded roads and connected roads impacted by the hazard can be identified. The road extraction methodology allows the extraction of 75% to 100% of the impacted roads mentioned in VGI text messages for the respective case studies. In addition to the directly extracted impacted roads, constructing the degraded network also involves finding road segments that overlap with hazard impact zones, as these are at risk of being impacted. Conducting the network analysis with the four different measures on the intact and degraded network, changes in network accessibility due to the impacts of hazards can be identified. The results show that using each measure is justified, as each measure could demonstrate the accessibility change. However, their combination and comparison provide valuable insights. In conclusion, this study successfully addresses the challenges of developing a generic, complete framework from impact extraction to network analysis independently of the scale and characteristics of road network types.

Rapid natural hazard extent estimation from twitter data: investigation for hurricane impact areas

Natural hazards have occurred more frequently in the past years and pose a severe risk to human life. Their extents and, thereby, the most heavily affected areas must be estimated at the earliest to limit damages or initiate rescue services. For such estimations, a widely available data source, which is comparatively responsive to short-time changes, is needed and provided by volunteered geographic information (VGI) data. Tropical cyclones are natural hazard events that can cause enormous spatially extended damage. In this study, we introduce Machine Learning approaches such as Extremely Randomized Tree (ET) and Geographically Weighted Regression for estimating hurricane-impacted regions from VGI data. In addition to the general approximate track extent estimation, we also evaluate the possibilities of temporal estimation of track development from VGI data. Different scenarios are evaluated, and we find that the results mainly depend on the choice of the geographical splits for training and test data for the underlying regression task. Suitable splits lead to R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 99% in the best cases with the ET model. The estimation results are satisfying when considering the temporal aspect and represent a use-case scenario. Such a combination of Machine Learning approaches and VGI is a simple and fast approach for early natural hazard estimation.

An MIU-based deep embedded clustering model for urban functional zoning from remote sensing images and VGI data

Urban functional zoning offers valuable insights into urban morphology and sustainable development. However, the conventional fixed spatial units, such as blocks and grids, cannot easily capture the morphological characteristics inherent in functional union and separation during urban evolution. In this paper, by taking advantage of remote sensing images and geospatial big data, we propose a minimum identification unit (MIU)-based urban functional zoning model. This approach integrates the deep embedded clustering of buildings to generate the spatial unit segmentation, and then identifies the urban function by generating semantic vectors with the Word2Vec model. The effectiveness of the proposed method was tested in the city of Wuhan in China. The results highlight that MIUs provide a more flexible and suitable unit for segmenting urban functional zones compared to traditional street blocks. The proposed method is a feasible way to deal with the semantic redundancy of volunteered geographic information (VGI) data when identifying urban function, and the quality issue only has a significant impact on the minor functional types. Moreover, the building clustering results can effectively reveal the fine-scale urban structure, especially for the administration, manufacturing, and residential types. This demonstrates the potential of our approach in enhancing the understanding of urban morphology and supporting sustainable urban development.

A web-based geovisualization framework for exploratory analysis of individual VGI contributor’s participation characteristics

ABSTRACT Understanding the participation characteristics of VGI (volunteered geographic information) contributors is important as their active participation and consistent data contribution are key to success of any VGI project. Existing studies on this matter focused primarily on deriving and interpreting participation patterns of contributor groups. There is a lack of investigation into the participation pattern of individual contributors, which can be complementary to a comprehensive understanding of VGI contributors’ participation characteristics. Building and using a custom web-based geovisualization framework, this study explores the individual-level participation characteristics of VGI contributors from the perspectives of spatial, temporal, thematic, and social interaction patterns. I conducted geovisual exploratory data analysis on VGI datasets from the iNaturalist biodiversity citizen science project to gain intuitions on the clustering and variabilities of participation patterns in iNaturalist, detect participation pattern shifts over time and form explanation hypotheses, and assess and develop metrics to measure participation. The geovisualization framework is expected to be generally applicable to other VGI datasets for exploring individual-level contributor participation patterns. This work is among the first efforts to explore individual-level VGI participation characteristics through geovisualization and geovisual analytics.

Gönüllü coğrafi bilgi kullanılarak Dilek Yarımadası’ndaki doğa temelli rekreasyonel aktivitelerin haritalanması

Korunan alanlar, açık hava rekreasyonu ve doğaya dayalı turizm için başlıca destinasyonlar haline gelmiştir. Korunan alanların sürdürülebilir ve etkin yönetimi için rekreasyonel aktivitelerin konumsal ve zamansal verilerine ihtiyaç duyulmaktadır. Günümüzde kitle kaynaklı platformlardan elde edilen gönüllü coğrafi bilgi, doğa temelli rekreasyonel aktivitelerin zamansal ve konumsal analizi için kullanılmaktadır. Bu çalışmada, Küresel Navigasyon Uydu Sistemleri (GNSS) tabanlı gönüllü coğrafi bilgiler aracılığıyla Dilek Yarımadası’ndaki doğa temelli rekreasyonel aktivitelerin konumsal modellerinin haritalanması amaçlanmıştır. Wikiloc’tan elde edilen 653 adet doğa yürüyüşü, 59 adet dağ bisikleti ve 22 adet yol bisikleti aktivitesinin GNSS izleri kullanılarak Dilek Yarımadası’nın rekreasyonel kullanımı analiz edilmiştir. Çalışmanın sonuçları, GNSS tabanlı gönüllü coğrafi bilgilerin, doğa temelli rekreasyonel aktivitelerin haritalanmasında etkili bir araç olduğunu ve geleneksel veri kaynaklarına alternatif veya tamamlayıcı bir kaynak olarak kullanılabileceğini göstermiştir.

Geographic proximity and homophily effects drive social interactions within VGI communities: an example of iNaturalist

ABSTRACT Social interactions among online community members contributing to volunteered geographic information (VGI) are a key element and often crucial to VGI project success. Existing studies on VGI lack investigations into the patterns of social interactions within VGI communities and the drivers that may have shaped these patterns. This study bridges this gap by analyzing social interaction patterns in the iNaturalist citizen science project and exploring potential driving factors through social network analysis. The relationships between potential drivers of social interactions in iNaturalist (i.e. geographic distance, species taxon composition similarity, and land cover composition similarity) and the establishment, intensity, and clustering of interactions were examined. The results revealed that geographic proximity, common interests in species taxon categories, and shared preferences in observation environments are major drivers of inter-contributor species identification interactions in iNaturalist. These findings are supported by theories that explain the social forces behind social link formation. Geographic distance represents physical proximity, whereas species taxon composition similarity and land cover similarity reflect homophily effects. iNaturalist and many other VGI communities are spatially embedded social networks. The discovered interaction drivers in iNaturalist generally align with those in spatial social networks and are expected to be generalizable to VGI communities beyond iNaturalist.

Spatial-temporal behaviour of hikers in the southeastern margin of Qinghai-Tibet Plateau: insights from volunteered geographic information

Understanding the macroscopic spatial-temporal behaviour of hikers plays an important role in targeted planning, marketing and management for regional hiking tourism development. The southeastern margin of Qinghai-Tibet Plateau, with abundant outdoor tourism resources and several World Heritage Sites, was chosen as the study area, and the development characteristics of hiking tourism and the spatial-temporal behaviour of its hikers were explored by seasonal intensity index, kernel density analysis, line density analysis and K-means clustering with volunteered geographic information (VGI). The results showed that hikers differed significantly from traditional tourists in terms of travel time, travel mode and destination choice, which provide vital implications for sustainable policy-making and tourism management in general and in the southeastern margin of Qinghai-Tibet Plateau in particular.

THE POTENTIAL OF CROWDSOURCED ASSET MAPPING TECHNOLOGIES FOR SUPPORTING DEMENTIA CAREGIVERS

Abstract It is well-established that caregivers of people living with dementia (PLWD) often report having difficulty finding information about education and support that is available to them in their community. Caregiver support groups have demonstrated to be an effective approach to caregiver education through mutual support, though many caregivers are unable to participate in caregiver support groups due to time and geographic constraints. Crowdsourced technologies that rely on volunteered geographic information (VGI) are a viable way of community members to provide mutual support on a variety of issues, including disaster response, traffic conditions, health, and travel. These technologies often rely on a mobile app where users upload VGI that are of interest to others who use the app. There are benefits and drawbacks of crowdsourced technologies. For example, they may be an easy way for community members to share needed information, though there may be challenges in the quality of data that users provide. This presentation will report findings from a project that examined dementia caregivers’ interest in using crowdsourced mapping technologies to share information about services and resources in their community that they have found helpful. Data were collected from a diverse sample of dementia caregivers living in Alabama through an online survey that was administered between June and November of 2022. Overall, it was found that caregivers were interested in using a crowdsourcing technology for support and education. Additional findings provide guidance for developing a crowdsourcing protocol that is based on caregivers’ reported information needs.

Evaluating OSM Building Footprint Data Quality in Québec Province, Canada from 2018 to 2023: A Comparative Study

OpenStreetMap (OSM) is among the most prominent Volunteered Geographic Information (VGI) initiatives, aiming to create a freely accessible world map. Despite its success, the data quality of OSM remains variable. This study begins by identifying the quality metrics proposed by earlier research to assess the quality of OSM building footprints. It then evaluates the quality of OSM building data from 2018 and 2023 for five cities within Québec, Canada. The analysis reveals a significant quality improvement over time. In 2018, the completeness of OSM building footprints in the examined cities averaged around 5%, while by 2023, it had increased to approximately 35%. However, this improvement was not evenly distributed. For example, Shawinigan saw its completeness surge from 2% to 99%. The study also finds that OSM contributors were more likely to digitize larger buildings before smaller ones. Positional accuracy saw enhancement, with the average error shrinking from 3.7 m in 2018 to 2.3 m in 2023. The average distance measure suggests a modest increase in shape accuracy over the same period. Overall, while the quality of OSM building footprints has indeed improved, this study shows that the extent of the improvement varied significantly across different cities. Shawinigan experienced a substantial increase in data quality compared to its counterparts.

Achieving fine-grained urban flood perception and spatio-temporal evolution analysis based on social media

Timely understanding of affected areas during disasters is essential for the implementation of emergency response activities. As one of the low-cost and information-rich volunteer geographic information, social media data can reflect geographic events through human behavior, which is a powerful supplementary source for fine-grained flood monitoring in urban areas. However, the value of social media data has not been fully exploited as potential location and water depth information may be embedded in both text and images. In this study, we propose a novel framework for fine-grained information extraction and dynamic spatial-temporal awareness in disaster-stricken areas based on Sina Weibo. First, we construct a novel fine-grained location corpus specifically for urban flooding contexts. The corpus summarizes characteristics of address descriptions in flood-related Weibo texts, including standard address entities and spatial relationship entities, based on the named entity recognition (NER) model. Then, water depth information in texts and images is obtained based on different deep learning modules and fused at the decision level. Specifically, in text analysis module, we summarize and extract diverse descriptions of water depth, and in image analysis module, we develop a water level hierarchical mapping method. Finally, we analyze the spatio-temporal distribution characteristics and variation patterns of the extracted information to enhance situational awareness. Taking the urban flood occurred in Anhui, China as a case study, we find that the variation of flooding hotspot areas in Sina Weibo and rainfall centers show a significant spatial and temporal consistency, and the fusion of text and image-based information can facilitate dynamic perception of flood processes. The framework presented in this study provides a feasible way to implement refined situational awareness and spatio-temporal evolution analysis of urban floods at the city level in time.

Crop type recognition of VGI road-side images via hierarchy structure based on semantic segmentation model Deeplabv3+

The application of artificial intelligence in the field of agricultural crop type recognition is of great significance. There are currently two methods for collecting crop information: remote sensing and volunteered geographic information (VGI). As one pixel in a remote sensed satellite image may correspond to multiple crop types due to its low resolution, more refined VGI images can be a supplementary data source for agricultural crop monitoring. A hierarchal semantic segmentation structure based on Deeplabv3+ is proposed in this paper. Inspired by causality theories, a binary classification is adopted in the first stage of the model to segment crops from background. The second stage aims to recognize the crop types including rape, corn, fallow and bare land, wheat and rice. As wheat and rice have many similar characteristics, they are considered as one category in this stage and will be further processed in the downstream. In the third stage, a priori knowledge such as the location and time of image acquisition is utilized for fine recognition between wheat and rice. We have carried out experiments on the proposed Deeplabv3+ model and other different semantic segmentation models are validated on a VGI dataset with 20% rough labels and 80% accurate labels, including thousands of road-side images collected from different image sensors, under different weather and from different geographic locations. Experiments results show that Deeplabv3+ achieves the best performance. The recognition of five categories of crops has reached a precision of 87%, a recall of 91%, and IoU of 81%, showing good potential for use in the crop type recognition for VGI images.

State-of-the-Art and Framework for Identifying Urban Patterns by Remote Sensing Data

The increase in the spatial resolution of satellite imagery and the greater distribution of data have enabled the use of remote sensing for urban studies. However, there is still a lack of databases and cartographic publications for cities in the Brazilian Amazon. Thus, the following questions guided this work: what is the state-of-the-art in remote sensing to identify urban patterns in Brazil? Do these urban patterns equally cover all Brazilian regions? How to identify urban patterns in different contexts, such as the Amazon urban region? To this end, we conducted a review of publications that have identified urban patterns in Brazilian cities and put forward an identification model. For the most part, we observed that the works found analyzed small areas in São Paulo cities, using visual interpretation in private access and high spatial resolution imagery. To cover this gap, we propose a framework to identify Urban and Socio-Environmental Patterns based on remote sensing data, Voluntary Geographic Information data and census data. Although the framework can be applied in all regions of Brazil, the focus of the classification model is on Amazonian cities.

Semantic enrichment of building functions through geospatial data integration and ontological inference

The comprehensive definition of buildings in urban spatial databases (SDBs) and geographic information systems (GISs) is crucial for city management, considering their essential role in cities. Modern urban geospatial applications such as smart cities need a rich-information infrastructure, relying upon urban SDBs and GISs, powered by multiple data sources. In this respect, many geospatial techniques are available for acquiring geometric data of buildings, but their semantic definitions require extra effort. Today, volunteered geographic information (VGI) platforms are assumed to be alternative geospatial data sources and their integration with official datasets provides a new opportunity for the enrichment of urban geospatial datasets. In this context, geospatial semantic web technologies can contribute to the semantic enrichment process. This study presents a methodology for enriching various building datasets using GIS and geospatial semantic web technologies, enabling an enhanced definition of building functions. The proposed method makes use of an OpenStreetMap (OSM) dataset to obtain missing semantic information about building features. The enrichment process was implemented by transferring OSM point of interest (POI) values to associated OSM building features through the application of semantic web rule language (SWRL) rules. Furthermore, the same process was also applied to two additional official datasets at different levels of detail (TOPO1K and TOPO25K), which were subsequently incorporated into the geospatial ontology. The resulting geospatial ontology offers new opportunities for functional definition and logical rule-making for buildings, with the potential to uncover new classes and insights. In addition, it allows for the definition of terms and explanations in multiple languages, covering most of the OSM values used worldwide for selected tags to define building functions.

Development of an Algorithm to Evaluate the Quality of Geolocated Addresses in Urban Areas

The spatial and semantic data of geographic addresses are extremely important for citizens, governments, and companies. The addresses can georeference environmental, economic, security, health, and demographic parameters in urban areas. Additionally, address components can be used by users to locate any point of interest (POI) with location-based systems (LBSs). For this reason, errors in address data can affect the geographic location of events, map representations, and spatial analyses. Thus, this paper presents the development of an algorithm for evaluating the quality of semantic and geographic information in any geospatial address dataset. The reference datasets are accessible using open data platforms or spatial data infrastructure (SDI) and volunteered geographic information (VGI), and both have been compared with commercial datasets using geocoding web services. Address quality analysis was developed using several open-source data science code libraries combined with spatial databases and geographic information systems. In addition, the quality of geographic addresses was evaluated by carrying out normalized tests in accordance with International Geospatial Standards (ISO 19157). Finally, this methodology assesses the quality of authorized and VGI address datasets that can be used for geocoding any relevant information in specific urban areas.

ChineseCTRE: A Model for Geographical Named Entity Recognition and Correction Based on Deep Neural Networks and the BERT Model

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.

A School of Thought on VGI Challenges: A Literature Review

Anyone who uses the Internet, wherever they live, is a potential participator. Whether informed or not, individuals participate in creating, collecting, and sharing geographic information. With the introduction of Web 2.0, content creation became a topic of universal interest. Web 2.0, the current state of the Internet, suggests greater user interactivity and user-generated content than its earlier incarnation. In recent years, user-generated spatial content, also known as volunteered geographic information (VGI), has gained popularity. Much of the existing literature explores VGI from a data typology perspective, and few studies consider it as a system. In this study, a vertical approach is employed to showcase the novel challenges of VGI that developers have encountered during the implementation or utilization of a project. First, six facets of VGI, encompassing political, economic, sociocultural, technological, legal, and ethical factors are thoroughly examined. Next, VGI challenges regarding its factors are categorized. Results reveal that most VGI challenges are technological in nature. Thus, drawing more attention to the technical facets of VGI is required. Additionally, establishing a standard for VGI development based on its various factors is necessary.

Quality Evaluation of Volunteered Geographic Information (VGI) by assessing user’s reliability, using Artificial Neural Networks (ANN)

Quality Evaluation of Volunteered Geographic Information (VGI) by assessing user’s reliability, using Artificial Neural Networks (ANN)

Community Activation for Disaster Risk Reduction Through OpenStreetMap: A Scoping Review

The advancement of geospatial technology has significantly altered how data is utilised for facilitating the growth of open-access data for disaster risk reduction (DRR). OpenStreetMap (OSM) is a part of Volunteered Geographic Information (VGI), built on the concept of volunteerism and heavily reliant on user contributions to spatial data. In recent years, OSM has emerged as a fundamental tool in all aspects of DRR, enabling well-informed decision-making and empowering active community contributions. This study aims to review the utilisation of OSM in DRR and proposes recommendations to enhance community resilience. The five-step scoping review method was conducted on 25 selected papers to investigate the current knowledge and uncover the potential applications of OSM within the community for DRR. The review commenced by defining the research questions, identifying pertinent literature sources, selecting relevant literature for inclusion, extracting key themes from the literature, and finally, consolidating, summarising, and reporting the results. The findings revealed that OSM’s worldwide accessibility for various objectives has been instrumental in its widespread adoption and rising prominence. Nevertheless, this review emphasises that despite its rapid expansion into other fields, OSM continues to hold significant importance and widespread usage in the context of DRR. This study presented findings on community involvement in OSM and explored the opportunities for community-led DRR implementation. The paper provides insights into the application of OSM in advanced and developing countries while delving into potential future pathways. OSM places significant emphasis on community-driven, on-the-ground data collection, providing governments with a chance to enhance policy management in DRR. As the incorporation of OSM in decision-making expands, it will garner attention from diverse sectors, demonstrating the platform’s effectiveness on a broader and more impactful level.