ABSTRACT The integration of citizen science, volunteered geographic information (VGI), and Web/mobile geographic information systems (GIS) has demonstrated significant potential in enhancing disaster response efforts. However, delivering timely, comprehensive and trustworthy information remains a major challenge, particularly when relying on passive data collection from social media. While researchers have developed specialized platforms for natural hazards and advanced models for data analysis, few studies present a holistic lifecycle from stakeholder-oriented design through development, especially with attention to the design phase. To address this gap, this paper introduces an agile and iterative user-centered framework for designing and developing a participatory mobile GIS application for collecting reliable, first-hand observations. A pilot study conducted during real-world hurricane events demonstrated the application’s ability to operate both in real time and offline, enabling the collection of precise geotagged data, categorized labels, and diverse media formats. The results highlight the potential of this active, stakeholder-centered approach to support intelligent disaster response strategies and complement passive and authoritative data sources. This paper advances the integration of citizen science and mobile GIS by providing a framework that follows user-centered design principles to inform future disaster response applications.

Urban revitalization processes are increasingly requiring inclusive and data-driven approaches that address spatial inequalities and support the achievement of the Sustainable Development Goals (SDGs). The article presents a methodology for utilizing social geoparticipation tools in the revitalization process of the Warsaw University of Technology campus. The study demonstrates how campus-scale geoparticipation can incorporate SDGs and spatial justice principles in micro-urban contexts, with a methodology that is transferable to city-scale projects and provides practical guidance for inclusive and sustainable urban governance. This enables the transformation of volunteered geographic information (VGI) data and spatial databases into practical spatial knowledge that supports sustainable urban development. Empirical analysis of 710 responses and nearly 1000 mapped locations revealed that 83% of respondents identified insufficient greenery as the primary spatial problem. At the same time, accessibility (β = 0.618) and green infrastructure quality (β = 0.553) were the strongest predictors of the need for change. The collected feedback from the academic community was processed using exploratory data analysis and spatial statistics into a spatial knowledge base. ESRI’s ArcGIS Experience Builder (Developer Edition version 1.16) was employed in the app’s development. A custom function was developed to meet the requirements of the geo-questionnaire fully. The application was ultimately deployed within the CENAGIS domain of the IT infrastructure at Warsaw University of Technology. Authors employed the structural equation modeling (SEM) method and provided statistical analysis of community expectations. The findings provide actionable evidence for urban planners, campus managers, and decision-makers seeking to implement data-driven, participatory revitalization strategies, demonstrating how social geoparticipation can directly inform sustainable design and policy-making at both campus and city levels.

Abstract. Volunteered Geographic Information (VGI) is spatial data collected through digital participatory mapping, where non-expert volunteers create and share spatial knowledge. Despite claims of empowerment and improved decision-making, the long-term outcomes of participatory mapping remain under-evaluated. In humanitarian emergency response, VGI production enables the creation of updated real-time post-crisis maps, thus improving response effectiveness. This paper examines the lasting socio-economic outcomes of VGI production in humanitarian assistance, beyond the immediate crisis period, with a focus on the 2015 Nepal earthquake response. VGI production led to the creation of the first freely available and comprehensive digital basemap for Nepal, which catalyzed the development of new spatial services and platforms. Beyond disaster relief, the mapping data provided a foundation for new business and educational opportunities, including Baato Maps, a culturally relevant, low-cost navigation tool. The adoption of these tools has fostered a competitive ecosystem for spatial services like ride-sharing, e-commerce, and delivery, fostering economic resilience. This paper demonstrates how participatory mapping, when purposefully integrated, can drive disruptive innovation and create socio-economic benefits which support the transition from emergency response to long-term development. It highlights the importance of incorporating community generated VGI into future humanitarian planning and evaluation to support sustainable, community-driven innovation and enhance long-term resilience.

Geospatial Artificial Intelligence (GeoAI) offers a scalable solution for automating the generation and updating of volunteered geographic information (VGI) maps—addressing the limitations of manual contributions to crowd-source mapping platforms such as OpenStreetMap (OSM). This study evaluates the accuracy of GeoAI-generated buildings specifically, using two Generative Adversarial Network (GAN) models. These are OSM-GAN—trained on OSM vector data and Google Earth imagery—and OSi-GAN—trained on authoritative “ground truth” Ordnance Survey Ireland (OSi) vector data and aerial orthophotos. Altogether, we assess map feature completeness, shape accuracy, and positional accuracy and conduct qualitative visual evaluations using live OSM database features and OSi map data as a benchmark. The results show that OSi-GAN achieves higher completeness (88.2%), while OSM-GAN provides more consistent shape fidelity (mean HD: 3.29 m; σ = 2.46 m) and positional accuracy (mean centroid distance: 1.02 m) compared to both OSi-GAN and the current OSM map. The OSM dataset exhibits moderate average deviation (mean HD 5.33 m) but high variability, revealing inconsistencies in crowd-source mapping. These empirical results demonstrate the potential of GeoAI to augment manual VGI mapping workflows to support timely downstream applications in urban planning, disaster response, and many other location-based services (LBSs). The findings also emphasize the need for robust Quality Assurance (QA) frameworks to address “AI slop” and ensure the reliability and consistency of GeoAI-generated data.

The Sustainable Development Goals (SDGs) have become the global framework for evaluating the effectiveness of humanitarian projects. Humanitarian mapping is considered a popular voluntary geographic information technique that provides data for disaster response. Although humanitarian mapping has contributed significantly to the SDGs, there is a lack of in-depth studies on the state of this relationship. This paper aims to assess the potential relationship between the SDGs and humanitarian mapping by (1) analyzing SDG indicators to determine their potential contribution to humanitarian mapping, and (2) identifying the actual contribution of humanitarian mapping projects to the SDGs. To achieve this, the study uses a structured methodology that combines SDG indicator analysis with project-level data filtering and text mining. Three major humanitarian mapping platforms—HOT-TM, MapSwipe, and Ushahidi—are examined in order to capture their potential and actual contributions to the SDG framework. Ultimately, the study highlights the strong alignment between humanitarian mapping activities and the need to monitor the SDGs, particularly in water, urban infrastructure, and land use, emphasizing the potential of volunteer-driven geospatial data to address critical data gaps.

The increasing use of Global Positioning System (GPS) tools reshapes nature-based recreational practices. While previous research has examined the role of GPS technologies in outdoor recreation, limited attention has been given to the specific factors driving GPS use in nature-based settings such as natural parks. This case study examines the sociodemographic, behavioural, motivational and experiential factors influencing GPS use among visitors to the Capçaleres del Ter i del Freser Natural Park (Catalonia, Spain). A structured visitor survey (n = 999) was conducted over a one-year period and a hierarchical binary logistic regression model was applied to evaluate the explanatory contribution of four sequential variable blocks. The results showed that the behavioural factors (i.e., physical activity intensity) emerged as the strongest predictor of GPS use. Additionally, the final model demonstrated that visitors who were younger, engaged in higher-intensity physical activities, motivated by health-related goals, undertook longer routes, and reported more positive experiences were significantly more likely to use GPS tools during their visit. These findings highlight the need to adapt communication strategies to diverse visitor profiles and leverage volunteered geographic information (VGI) for improved visitor monitoring, flow management, and adaptive conservation planning.

Volunteered geographic information (VGI) often contains rich geolocations that are crucial for disaster response and post-disaster assessment. However, existing studies on VGI geolocalization have not fully used the potential of multi-source and multimodal data. In this paper, we constructed a multimodal disaster dataset (MultiIan) and developed two novel methods (i.e. StaGeo and TriGeo) to enhance the cross-view geolocalization accuracy of disaster-related VGI. MultiIan comprised VGI texts and images, street view imagery (SVI) and remote sensing imagery (RSI). Large language models (LLMs) were used to extract the implicit geoinformation from VGI texts for geotagging. StaGeo was developed using staged training with ConvNeXt and vision transformer (ViT), while TriGeo used VGI ↔ SVI ↔ RSI triple-objective joint training of the ViT based on DINOv2. Using SVI to link VGI and RSI, our methods significantly improved the geolocalization accuracy of VGI across various train–test splits in MultiIan. With a typical 8:2 data split, StaGeo achieved Recall@1, Recall@5, Recall@10 and Recall@1% of 54.93%, 71.27%, 77.93% and 80.33%, respectively. TriGeo further improved these metrics, achieving 62.87%, 85.55%, 90.54% and 90.89%, respectively. These findings demonstrate significant advancements in our cross-view geolocalization methods, enabling timely geolocation to support rapid decision-making in emergency response and promoting the broader application of GeoAI in geospatial analysis.

Cycling practice is quickly increasing around the world, giving rise to the development of devoted infrastructure to protect its users and offer them a more enjoyable ride. Mobility infrastructure is represented in geographical databases, but these databases are often centered on car and pedestrian mobility. This causes some data quality problems like the lack of completeness or freshness. Volunteered geographical information (VGI) is affected by this kind of problem with a variable extent relying on the contributors' wish and skills. Research on VGI evolution for a network mainly focuses on the main usage of a road section, ignoring secondary information related to other road users of a specific section. This paper contains two contributions. To model the evolution, we define a multiplex graph where each layer represents a snapshot. It is implemented with an infrastructure class based on how cyclists perceive an infrastructure. We also present two complementary VGI road network evolution methods with a usage-centric approach on cycling. These approaches are adaptable for any usage of the network and are based on the multiplex graph. The first approach is based on the road sections, analyzing the evolution of each section individually. The second approach is based on randomly generated starting/ending points. These methods are illustrated in the Centre-Val de Loire region with OpenStreetMap.

Estimating park visitation in Canadian national parks using volunteered geographic information (VGI)

A digital twin to promote and preserve the endangered volunteer geographic information

Voluntary Geographic Information initiatives are transforming the disaster response landscape. Our research provides insights into how the concept of collective intelligence is accomplished in humanitarian mapping initiatives. The main source originates from the data obtained in 746 mapping projects organised by the Humanitarian OpenStreetMap Team between December 2021 and November 2023, where 38,893 contributors completed 312,289 mapping tasks. These data include detailed attributes of the contributors and the states the tasks go through. The methodology adopts a quantitative approach, including descriptive and inferential statistics, and standard process mining techniques. Our results indicate that, in general terms, in humanitarian mapping, a group of contributors from outside the area of interest perform straightforward mapping tasks with limited collaboration among them. The “wisdom” of advanced contributors is the cornerstone that sustains the system. The discussion section elaborates on (1) how these findings suggest that humanitarian mapping projects effectively meet their short-term mapping objectives but fall short if more sustainable mapping objectives are sought and (2) possible strategies for better harnessing the collective intelligence of these efforts.

ABSTRACT Social interactions among data contributors are essential to the success of many VGI (volunteered geographic information) projects, and the patterns of such interactions are often shaped by geographies in which the contributors are situated. However, there is a lack of investigations on geographic context’s influences on VGI contributor behaviour and interaction. This study explores patterns and drivers of inter-contributor species identification activities in the iNaturalist biodiversity citizen science community using a custom geovisual analytics tool integrating visualization and analysis of social networks in geographic context. Geovisual explorations of the iNaturalist social network revealed that the frequency and intensity of species identification interactions in iNaturalist are influenced by three geographic contextual factors, namely, the geographic distance, land cover composition similarity and species taxon composition similarity between observer contributors and identifier contributors. The findings align with social theories concerning the key forces driving the formation of social interactions in a social network, wherein geographic distance reflects physical proximity and land cover composition similarity, and species taxon composition similarity reflects homophily effects. The geovisual analytics tool effectively facilitates exploring patterns and drivers of social interactions and offers a new lens through which to examine the social and geographic dynamics of social interactions in VGI communities.

The availability of user-generated spatial data (user spatial content, USC) has transformed social science research, enabling the real-time, large-scale exploration of socio-spatial dynamics. This article traces the evolution from volunteered geographic information (VGI) to USC, highlighting their multidimensional nature and epistemological significance. Brief examples underscore USC’s potential for capturing the interplay between territorial factors, digital activity, and social phenomena, ranging from mapping urban vitality to tracking large-scale crises. However, the recent tightening of data access in the post-API era demands a rethinking of research approaches. Alternatives such as data donation, dedicated applications, and geoparsing can maintain the viability of USC-driven analyses. Overall, this article underlines the need for diversified, ethical, and methodologically sound strategies to harness USC’s value in understanding the digitally intertwined realities of contemporary society.

This study, based on Volunteered Geographic Information (VGI) and multi-source data, aims to construct an interpretable macro-scale analytical framework to explore the factors influencing urban physical activities. Using 290 prefecture-level cities in China as samples, it investigates the impact of socioeconomic, geographical, and built environment factors on both overall physical activity levels and specific types of mobile physical activities. Machine learning methods were employed to analyze the data systematically. Socioeconomic, geographical, and built environment indicators were used as explanatory variables to examine their influence on activity willingness and activity intensity across different types of physical activities (e.g., running, walking, cycling). Interaction effects and non-linear patterns were also assessed. The study identified three key findings: (1) A significant difference exists between the influencing factors of activity willingness and activity intensity. Socioeconomic factors primarily drive activity willingness, whereas geographical and built environment factors have a stronger influence on activity intensity. (2) The effects of influencing factors vary significantly by activity type. Low-threshold activities (e.g., walking) tend to amplify both promotional and inhibitory effects of the factors. (3) Some influencing factors display typical non-linear effects, consistent with findings from micro-scale studies. The findings provide comprehensive theoretical support for understanding and optimizing physical activity among urban residents. Based on these results, the study proposes guideline-based macro-level intervention strategies aimed at improving urban physical activity through effective public resource allocation. These strategies can assist policymakers in developing more scientific and targeted approaches to promote physical activity.

Visualization and interpretation of user-generated spatial content such as Volunteered Geographic Information (VGI) is challenging because it combines enormous data volume and heterogeneity with a spatial bias. When dealing with point data on a map, these characteristics can lead to point clutter, reducing the readability of the map product and misleading users to false interpretations of patterns in the data, e.g., regarding specific clusters or extreme values. With this work, we provide a framework that is able to generalize point data, preserving spatial clusters and extreme values simultaneously. The framework consists of an agent-based generalization model using predefined constraints and measures. We present the architecture of the model and compare the results with methods focusing on extreme value preservation as well as clutter reduction. As a result, we can state that our agent-based model is able to preserve elementary characteristics of point datasets, such as the point density of clusters, while also retaining the existing extreme values in the data.

Since the inception of volunteered geographic information in 2007, this area of study has seen a proliferation of terms and concepts representing diverse forms of user-generated geographic data and systems. Despite the rich development of VGI (volunteered geographic information) in geography, recent trends indicate a disjointed research field. This progress report critically examines the trajectory of VGI, mapping its journey from an emergent set of practices to a fragmented research domain. Moving forward, it is up to the research community to either reignite the interaction and integration required to build a subdiscipline of GIScience or allow this research domain to extinguish.

Abstract In the realm of volunteered geographic information (VGI), the existence of comparable tags, attributes, and values across diverse categories of geographic objects gives rise to major categorization challenges such as conceptual overlap and indiscernibility. Enhancing the semantic data retrieval of VGI relies on the semantic quality of descriptive content annotated for tagging geographic objects. The main focus of this study is analyzing the descriptive content of OpenStreetMap to assess the significance of semantic levels. The proposed methodology relies on fuzzy rough set calculations to determine the degrees of dependency and significance of semantic levels. Three indicators, namely, the significance of semantic levels, decreasing the heterogeneity of attributes, and replicability were defined and assessed for a subset of building‐related tags. Analyzing building‐related tags in OpenStreetMap unveiled the higher significance for simple object, similarity, purpose, and function levels. The value of decreasing the heterogeneity of attributes was calculated at 63%, and the average replicability indicator of important attributes was doubled. Based on the results, the significance of semantic levels was deemed fit to enhance semantic homogeneity and replicability.

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or volunteered geographic information (VGI). Key challenges with such “opportunistic” data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

Abstract In this article, we compile and characterize a total of 43 collaborative web map projects by a set of parameters that enable the understanding and comparability of current and future projects. We then develop a comprehensive methodological framework to explore volunteered geographic information (VGI) and spatial data infrastructure (SDI) convergence based on this review. The main results show the dominance of citizen science projects, followed by initiatives promoting sustainability values, local development, and governance. Although values remain low, the potential to achieve convergence in VGI–SDI features is very high in citizen science projects, where the presence of experts and the funding of these projects by governments and decision‐making entities enable quality standards in the collection and distribution of the contributed information. The work concludes by addressing two major challenges facing current VGI projects: firstly, accessing affordable technological solutions that allow the creation of collaborative web maps with SDI‐like functions. Secondly, guaranteeing the project's sustainability and the preservation of the information gathered.

Combining environmental-socio-economic data with volunteer geographic information for mapping flood risk zones in Zhengzhou, Henan Province, China