Organized mapping activities within OpenStreetMap frequently lead to the production of massive amounts of data over a short period. In this article we utilize a novel procedure to identify such large‐scale data production events in the history of OpenStreetMap and analyze their patterns. We find that events account for a significant share of OpenStreetMap data and that organizational practices have shifted over time towards local knowledge‐based events and well‐organized data imports. However, regions in the “Global South” remain dependent on remote mapping events, pointing to uneven geographies of representation. We also find that events are frequently followed by periods of increased activity, with the exact nature of effects depending on contextual elements such as previous events. These findings portray organized activities as a significant and unique component which requires consideration when using OpenStreetMap data and analyzing their quality.

The collaborative nature of activities in Web 2.0 projects leads to the formation of online communities. To reinforce this community, these projects often rely on happenings centred around data creation and curation activities. We suggest an integrated framework to directly assess online community member performance in a quantitative manner and applied it to the case study of OpenStreetMap. A set of mappers who participated in both field and remote mapping-related happenings was identified. To measure the effects of happenings, we computed attributes characterising the mappers’ contribution behaviour before and after the happenings and tested for significant impacts in relation to a control group. Results showed that newcomers to OpenStreetMap adopted a contribution behaviour similar to the contribution behaviour typical for the respective happening they attended: When contributing after the happening, newcomers who attended a remote mapping event tended to concentrate on creating new data with lower quality but high quantity in places foreign to their home region; newcomers who attended a field mapping event updated and enhanced existing local data with high accuracy. The behaviour of advanced mappers stayed largely unaffected by happenings. Unfortunately, our results did not reveal a positive effect on the community integration of newcomers through happenings.

OpenStreetMap (OSM) is a well-known example of volunteered geographic information. It has evolved to one of the most used geographic databases. As data quality of OSM is heterogeneous both in space and across different thematic domains, data quality assessment is of high importance for potential users of OSM data. As use cases differ with respect to their requirements, it is not data quality per se that is of interest for the user but fitness for purpose. We investigate the fitness for purpose of OSM to derive land-use and land-cover labels for remote sensing-based classification models. Therefore, we evaluated OSM land-use and land-cover information by two approaches: (1) assessment of OSM fitness for purpose for samples in relation to intrinsic data quality indicators at the scale of individual OSM objects and (2) assessment of OSM-derived multi-labels at the scale of remote sensing patches ( $$1.22 \times 1.22$$1.22×1.22 km) in combination with deep learning approaches. The first approach was applied to 1000 randomly selected relevant OSM objects. The quality score for each OSM object in the samples was combined with a large set of intrinsic quality indicators (such as the experience of the mapper, the number of mappers in a region, and the number of edits made to the object) and auxiliary information about the location of the OSM object (such as the continent or the ecozone). Intrinsic indicators were derived by a newly developed tool based on the OSHDB (OpenStreetMap History DataBase). Afterward, supervised and unsupervised shallow learning approaches were used to identify relationships between the indicators and the quality score. Overall, investigated OSM land-use objects were of high quality: both geometry and attribute information were mostly accurate. However, areas without any land-use information in OSM existed even in well-mapped areas such as Germany. The regression analysis at the level of the individual OSM objects revealed associations between intrinsic indicators, but also a strong variability. Even if more experienced mappers tend to produce higher quality and objects which underwent multiple edits tend to be of higher quality, an inexperienced mapper might map a perfect land-use polygon. This result indicates that it is hard to predict data quality of individual land-use objects purely on intrinsic data quality indicators. The second approach employed a label-noise robust deep learning method on remote sensing data with OSM labels. As the quality of the OSM labels was manually assessed beforehand, it was possible to control the amount of noise in the dataset during the experiment. The addition of artificial noise allowed for an even more fine-grained analysis on the effect of noise on prediction quality. The noise-tolerant deep learning method was capable to identify correct multi-labels even for situations with significant levels of noise added. The method was also used to identify areas where input labels were likely wrong. Thereby, it is possible to provide feedback to the OSM community as areas of concern can be flagged.

Achieving climate neutrality requires a socio-ecological transformation of the mobility sector. Consequently, urban traffic infrastructure must be redesigned to promote and support active mobility. By analysing multiple criteria extracted from open-access geodata via OSM (OpenStreetMap), we can assess the current state of the urban traffic infrastructure and evaluate the walkability and bikeability of streets and neighborhoods. HeiGIT’s walkability and bikeability indices – HiWalk and HiBike – provide traffic planners, decision-makers, NGOs, and the general public with quantitative insights into how well a city's traffic infrastructure supports active mobility.In recent years, numerous walkability and bikeability indices have emerged, focusing primarily on accessibility metrics. However, these anlyses often assume that all streets are equally suitable for active mobility, overlooking the specific needs of groups such as the elderly, young children, people with disabilities, and risk-averse cyclists.It is therefore essential to address two critical gaps: (a) providing detailed information on the suitability of urban environments for active mobility, and (b) ensuring that traffic infrastructure transformation is inclusive. To meet these needs, we collaborate with practitioners and NGOs to co-create street-level indices of walkability (HiWalk) and bikeability (HiBike). Our goal is to offer practical applications that go beyond general index values for cities or neighborhoods. By assessing bikeability and walkability at the street-level, we can better inform routing engines and support accessibility analyses of inclusive “15-minute cities“.HiWalk and HiBike consist of indicators that assess the user-friendliness, attractiveness, and safety of paths and streets. They evaluate factors such as the presence of sidewalks and cycling lanes, surface smoothness, and surface material types. HiBike also incorporates information on street-side parking from OSM to identify streets where cyclists may be at risk of “dooring”. Both indices are entirely based on open data and can be adapted to various urban settings worldwide.Since HiWalk and HiBike are still under development, our presentation will focus on the main challenges we have encountered, including (1) their application for cities with different cultural, socioeconomic, and environmental contexts, and (2) the variability in the quality and completeness of OSM data. These challenges underscore the benefits of our co-creation approach in enhancing the indices' usability and impact on policy.

Residential space heating remains a major source of greenhouse gas emissions in the building sector. In Germany, space heating accounts for the largest share of residential energy consumption, and accurate quantification of associated emissions is essential to meet national climate mitigation targets.Most research on residential heating emissions focuses on the regional or national levels, while estimates at finer spatial scales remain limited. Data availability further constrains the transferability and usability of current models. Consequently, approaches that deliver spatially and temporally detailed emission estimates and interactive tools to support analysis and decision-making by stakeholders are urgently needed.We introduce the Climate Action Navigator (CAN), a dashboard for the analysis and visualization of climate mitigation and adaptation spatial data, based entirely on open science principles. One of the tools available in the CAN estimates carbon dioxide emissions from residential heating at fine spatial at temporal scales. The tool applies a bottom-up accounting methodology at 100 m spatial resolution based on publicly available census and building characteristics data in Germany, including building age and dominant energy carriers. The resulting emission estimates are consistent with official city- and national-level inventories, confirming methodological reliability. Germany-wide analyses reveal strong spatial heterogeneity in energy consumption and emissions that correlate with urban morphological characteristics.Temporal dynamics are captured through an hourly simulation using the Demand Ninja model based on local weather data. The resulting temporal emission patterns can support inverse emission modelling applications as well as aid energy management by, for example, revealing peak heating demand times and locations.Results are delivered via the CAN interface as intuitive, interactive maps and charts that allow users to compare across neighborhoods, explore temporal emission dynamics, and assess potential mitigation actions. By integrating open-source data with high-resolution modeling and visualization, the Climate Action Navigator bridges the gap between scientific emission quantification and practical decision making. The approach supports transparent attribution and tracking of residential space-heating emissions, thereby advancing evidence-based climate mitigation planning.