Chaos Computer Club - archive feed

Man kommt oft nicht umher und muss sein Projekt oder seine Idee anderen Leuten vorstellen. Ich werde heute im Petit Foo erklären, worauf man beim Präsentieren achten sollte und wie man typische Fehler vermeidet. about this event: https://www.chaospott.de

about this event: https://c3voc.de

"Computer, schalte das Licht aus!" ist dank smarter Lautsprecher keine besondere Funktion mehr, sondern Alltag in vielen Haushalten. Eine umfangreiche Smarthome-Ausstattung haben jedoch nur wenige. In diesem Petit Foo wird erläutert welche Techniken es für das Smart Home gibt und worauf man achten sollte. Besonders wird darauf eingegegangen wie die Clubautomatisierung in der Foobar umgesetzt haben und am Beispiel der Heizungssteuerung erklären wie man weitere Automatismen und Geräte dort einbindet. about this event: https://www.chaospott.de

SotM Working group say thank you to all volunteers and attendees and good bye until next year. SotM Working group say thank you to all volunteers and attendees and good bye until next year. about this event: https://2021.stateofthemap.org/sessions/PRZUTW/

A/B Street is an open source traffic simulator built on OSM and public census data, easy for the general public to use. This talk will cover some case studies of A/B Street being used to advocate for cycling infrastructure in Seattle, and describe how to use it anywhere. A/B Street (abstreet.org) is an open source traffic simulator built on OSM and public census data. It simulates car, bicycle, foot, and public transit traffic, and runs on Mac, Windows, Linux, and directly in the web browser. A/B Street allows the user to reallocate existing road space between cars, protected cycle lanes, transit-only lanes, and street parking. Users can also modify traffic signal timing and create access-restricted neighborhoods. Individual and aggregate results from the simulation can be compared before and after the changes, creating a simple way to evaluate potential changes. A/B Street has been designed for the general public to easily explore proposals for reducing dependency on cars. This talk will cover some specific cases in Seattle where the software has been used to propose real changes, like opening a shortcut through a gated community for cycle and foot traffic to avoid dangerous roads. We'll discuss how to start using A/B Street in your area, the challenges in finding other open data-sets required, and some options for how to publish results. Finally, the talk will briefly demonstrate how A/B Street's rendering can be used for validating some tags, and how to get involved with the project. about this event: https://2021.stateofthemap.org/sessions/JNVWZS/

Global dataset and a map style for hiking and biking developed from OpenStreetMap - that's MapTiler Outdoor. During development, we have to solve multiple challenges originating from a lack of international standardization in trail marking. MapTiler Outdoor is a global dataset and a map style for hiking and biking available as a zoomable and web-compatible vector tiles, which are ready for use in OpenLayers, MapLibre, Leaflet, QGIS as well as mobile applications. After first excitement, challenges starts to appear one after another: most of them originating from a need to create a rigid set of rules for the entire world, which is full of diversity due to the variety of nature. We also faced the challenge of lacking international standardization for hiking trails, which is unique for each country (and even on this level with many exceptions and specialities - like oneways, via ferrata routes or even climbing trails). And last, but not least, we have to dig all this information from the OpenStreetMap and turn it into a map understandable for everyone. We managed to create a layer with trails and corresponding points of interest, which can be filtered on hiking or biking in the customize tool. This layer can be overlaid on top of any map. However, we created a specialized map style which combines OpenStreetMap with contour lines and hillshade and highlights things you need for moving around in the nature. The schema of MapTiler Outdoor is based on the open-source OpenMapTiles (OTM) schema and data are processed using OMT stack. MapTiler Outdoor is available via MapTiler Cloud as a service or in a form of a data package for download. about this event: https://2021.stateofthemap.org/sessions/EBQEZJ/

Describes current situation of people using and appreciating OpenStreetMap data without being aware at all about its source. Describes how it violates OpenStreetMap license and proposes to start enforcing it. OpenStreetMap is not really known among people. But it does not mean that they are not using it. I was talking with a pair of tourists using maps.me to navigate. They commented that OpenStreetMap sounds like an interesting project. But it seemed to them to be an unnecessary duplicate of maps.me with its great maps. They were completely unaware that all useful data displayed by maps.me is from the OpenStreetMap. OSM data can be used by anyone, with few requirements. One of them is requirement to clearly and prominently state source of data. Despite this, many companies somehow fail to include a proper attribution. Including ones that are incredibly wealthy and ones that put massive effort into designing their applications. How can we change that situation? Many people who would contribute are unaware that OpenStreetMap exists. We are losing potential contributors. Especially among people not interested already in open data and maps. It is one of reasons why demographics of OpenStreetMap are so diverged from overall population. One needs to be quite unusual to be even aware that it exists and that this data is widely used. Due to obvious conflict of interest between OpenStreetMap community and corporations using this data fixing this problem will not be easy. But we should try. about this event: https://2021.stateofthemap.org/sessions/PHJ83R/

During the past decades, the European Commission has invested billions in research through various programmes, such as H2020. In this study, we review exhaustively all the H2020 open deliverables to analyse how these public european projects are relying on OpenStreetMap. Since 1984, the European Commission has been supporting research through various successive programmes. Recently, from 2014 to 2020, the EU invested approximately 80 billion euros into its eighth programme, named Horizon 2020 [1]. Among various focuses such as the excellence of science or industrial secondments, H2020 emphasised on supporting an open access policy for all the research results [2]. Moreover, H2020 projects were strongly encouraged to use open source software and tools. Practically, all the research domains were eligible to be supported by the H2020 programme, and therefore, the scopes of the projects vary from e.g. computer science, to philology passing by agriculture… Technically, as these projects are almost always involving several partners located in several European member states joining forces from multiple institutions, there is often a need to deal with data coming from different places. And, more generally, geo-data are often involved to tag information which may be research data, meeting localisation, partner addresses, etc. In such a context where open source tools are recommended by the European Commission, we analyse the presence of OpenStreetMap in H2020 projects. In addition, we also review the presence of other geographic services such as Google, Bing and Baidu maps, in order to better understand how researchers tend to choose one over the other. Thanks to the open access policy, participants of the H2020 projects had to make their results available. To do so, their various types of materials were submitted to the European portal which then offers them publicly. As a consequence, for each project, one can access the articles (through DOIs), the blog posts, the slide decks, the deliverables… In particular, in our study, we decided to focus on the deliverables as they are accessible on the EC portal directly and are the common reports written by the partners to describe their approaches. Indeed, these deliverables (usually written on a regular basis during the project) report on the findings and methodology set up to achieve the project’s goals and authors explain their architectural choices in depth such as describing the tools used. As a consequence, cartographic services, if involved at some stage in the project, are likely to be mentioned in these documents either as acronyms (e.g. OSM) or as website references (e.g. https://www.openstreetmap.org/). In order to obtain the deliverables together with projects’ information, we combined two European sources of information to gather all the facets we wanted to cover: CORDIS [3] and Data.Europa [4]. In particular, we extracted from CORDIS various high-level information about the projects themselves: from their names and acronyms to their durations passing by the specific European call-for-fundings they answered and obtained their money from. This latter category can be useful in order to have a finer-grained understanding of the domains which are prone to involved cartographic services. Next in order, Data.Europa was used to download the deliverables themselves, which required several days of computing resources. Overall, during the course of the H2020 programme, 33636 projects were funded by the European Commission. Depending on the type of action which was set by the projects, not all of them had some open deliverables written (and thereby available on the Europa platform). Actually, a large part of these projects did not have deliverables per se but rather articles or web posts. We indeed counted 25157 projects without deliverables which restricted our study to the remaining 8479 projects. Out of them, we listed a total of 92612 distinct deliverables to be analysed, representing more than 260GB. Technically, once all these deliverables were downloaded, we searched them for various terms to know if some cartographic services are involved in the text. We therefore set up several regex rules (e.g. 'open.?street.?map’ or ‘[^a-z0-9]osm[^a-z0-9]’) which were run over the 92000+ deliverables. This allowed us to systematically count all the occurences of the considered cartographic solutions. In the end, we found that 1840 deliverables (from 651 projects) mention OpenStreetMap. More precisely, through all the H2020 deliverables, there are approximately: 18600 mentions to OSM, 2800 to GoogleMaps, 226 BingMaps and 4 to BaiduMaps. Empirically, we notice that 1) one order of magnitude separates the occurrences of each cartographic service and 2) OpenStreetMap is from far the most represented solution and thereby the one on which public European researchers rely the most. Contextually, it is also interesting to note that not all the deliverables (1796 of them) mentioni

Since now 10 years, the Overpass API allows to query the OpenStreetMap data. This is reason enough to show how OpenStreetMap and the Overpass API have changed over that timespan. I will present and overview over user statistics, technical challenges and all kinds of requests to me as the developer. Since now 10 years, the Overpass API allows to query the OpenStreetMap data. This is reason enough to compare the OpenStreetMap of then to the OpenStreetMap of now and to show how OpenStreetMap and the Overpass API have changed or not changed over that timespan. For this purpose I will present an overview: With which requests are the public instances of the Overpass API actually used? The Overpass API has been intended for compound queries for the public transit data model of OpenStreetMap, but actually got popular for the simpler tag queries on bounding boxes. The provisions for an area data type have never materialized. How has it protected itself and how does it now protect itself against excessive load? I have always been proud that the service runs on ordinary server hardware and nothing huge, but still can give every user a meaningful amount of computation time. The necessary measures to curb the greedy and the stupid have evolved over time. Which requests for features and questions have been addressed to me as the developer? Basically everything we use now has been requested by users of the service. Probably the best idea has been to change from the longish XML based query language to the now much shorter QL based language. about this event: https://2021.stateofthemap.org/sessions/FV8QGF/

I explore the spectrum of web map rendering techniques between raster and vector, and outline new approaches towards making universal map applications with OSM. OSM's mission is to create a free, global dataset of geodata for diverse applications. A mainstream OSM use case is **web cartography**: the graphical display of geographic features on the standardized browser platform. Cartographers want to make beautiful, labeled, multi-resolution maps; maps need to be loaded progressively for a smooth user experience. The web map status quo centers around two distinct approaches. One approach, used by the OSM Carto project, is server-rendered raster map tiles displayed as images in the browser. Another approach is to use a WebGL framework that consumes tiles of vector features. In the first part of this talk, I will investigate the tradeoffs inherent in these approaches with regards to **multilingual text and internationalization**, as these pose limitations for localizing OSM applications to different languages and cultures. The second part of this talk will introduce new techniques and libraries for rendering OSM data that are a compromise between the raster and vector map ecosystems. I'll demonstrate some of these ideas in protomaps.js, a new Canvas2D and Leaflet-based open source map rendering library. about this event: https://2021.stateofthemap.org/sessions/JGF9WA/

The rise of organized editing practices in the OpenStreetMap community has outpaced research methods for identifying mappers participating in these efforts and evaluating their work. This research uses machine-learning to improve upon prior approaches to estimating corporate editing on OSM, contributing both a novel methodology as well as summary statistics that shed light on corporate editing behavior in OSM. In the past five years, the OSM community has seen a dramatic rise in organized editing, including corporate, humanitarian, and educational, on the platform. These new actors have continued the ongoing debate surrounding OSM’s relationship with organized editing, with new rules and best-practices being implemented to align the interests of the organizations with those of the community. We became interested to study how the editing habits of these new actors differed from the community as a whole, but were quickly confronted by the challenge of producing accurate measures of their activities. In this paper we aim to fill this gap by creating computational methods of understanding different editing behaviours on OSM to classify editors as being corporate or volunteer. Classifying individual editors has been done in the past, on a more local level, for example in the recent analysis on editing in Mozambique. [1] Studying corporate editing behaviour, first requires a list of corporate editors. In the past, researchers have searched individual "organized editing team” webpages. Instead, our paper presents a novel method for classifying users on the platform, by scraping user profiles. There are two possible approaches to extract corporate mappers based on user profiles. The first approach uses a clustering of the keywords within the profiles. Though effective at uncovering relations between users (like students, programmers, Garmin editors, Colorado mappers), this method failed to properly capture all known corporate groups. Instead we did a keyword search for corporations listed on the Organized Editing List and classified similar users together. We then divided this list into corporate or non-corporate. This simplification was done to align with past research into corporate editing [2]. Using this extracted list, we discern features that could act as “signals” for organized editors. Explicitly, which features from the changesets can point to an editor being corporate or volunteer. Do corporate editors edit specific types of items? Do their time series signatures differ? For the creation of these features, we relied on Jennings Anderson’s past work on corporate editing for inspiration [2]. The first set of features came from OSM changeset metadata which is rich with user descriptive data like the editor used, comments, and source. We find that most organizations use editors like JSOM and iD. Next, we attempted to model which objects corporations edit by finding descriptive words like “service”, “road”, and “building” in the comments of the changeset. We observed that most corporations focus on services and roads, as opposed to buildings which tend to be dominated by volunteer mappers. The third feature was motivated by the observation that as the interests of a corporation change, the editing of its mapping team can also change. This has led to the well documented phenomena of corporate mappers having a geographically dispersed editing pattern. This is markedly different from many volunteer mappers who often begin by mapping their local neighbourhoods. Using established metrics, we calculated the geographic dispersion for each user based on the latitude and longitude of their edits. The metric we found most effective was the timeseries signature. Corporations have a traditional 9-5 mapping schedule, whereas non-corporate mappers tend to map far more haphazardly, including significant mapping on the weekend. When attempting to convert the timeseries signature into a usable metric, we came across a problem: timezones. All changesets in OSM are normalized to UTC time, this means that a user editing at 8am in Toronto, Canada and another user editing at 8pm in Beijing, China would in fact appear to be editing at the same time in OSM. Longitude and latitude data are not an effective method of extracting the mappers timezone, since editing on OSM is increasingly done remotely, through “armchair mapping”. To utilize this strong signal, we developed a new method for normalizing a users time signature, and it was based on the observation that individual corporations have several key editing patterns, depending on where their employees are located. For example, Facebook has two such patterns, each displaced by around 8 hours. This motivated us to create a “corporate editing signature” and translate the corporate signatures to find the minimal distance between the two. After using this method of adjustment, we were able to significantly improve the alignment of the time-series. In other words, we were able to re

This track gathers various lightning talks of 5 minutes each. ## Being ohsome with R *Oliver Fritz* | *[ofr1tz](https://www.openstreetmap.org/user/ofr1tz)* How to query the ohsome API from R to aggregate and extract elements from the OSM history. ## Introducing tilemaker 2.0 *Richard Fairhurst* | *[Richard](https://www.openstreetmap.org/user/Richard)* Vector tiles are great! Having to maintain a rendering database isn't. That's where tilemaker comes in. SOTM 2021 will see the release of the [all-new v2.0](https://github.com/systemed/tilemaker) which makes vector tiles speedily, on your own hardware, without a database. Richard Fairhurst explains how it works and how you can use it. ## GeOsm: The first mapping data-based social network to connect and empower communities. *SOB Willy Franck* Our goal is to create a mapping data-based social network for territory stakeholders. We want to work with different actors of the digital ecosystem to facilitate the promotion of our tool to those who need it. And it will be up to each community to appropriate the approach in an innovative way to help states, associations, people, companies and the environment. ## State of The Map Africa 2021 *Sharon Omoja* | *[shazomojah](https://www.openstreetmap.org/user/shazomojah)* The State of the Map Africa conference celebrates the culture of open mapping, open data and GIS and its impact across Africa. In this talk we will give updates and plans for this year's State of the Map Africa conference. ## Presenting New Numbers: Quantifying the Increase in Paid Editing Since 2018 *Jennings Anderson* There are now more than 2,500 active _paid editors_ in the OSM Community. The last time we comprehensively quantified this editing activity involved just 1,000 editors across ten companies. This talk describes the increase in paid editing since 2018 with country-level breakdowns of editing activity and information regarding new editing teams and the growth of the existing teams. ## Tanga Buildings Import and Community Mapping *Antidius Kawamala* | *[KAWAMALA](https://www.openstreetmap.org/user/KAWAMALA)* This is an Initiative for scaling up the OpenStreetMap Project by addition of buildings footprint data for entire region of Tanga,Datasets was developed by Ardhi University Students (ARU) in Collaboration with Tanzania Communication Regulatory Authority (TCRA) in 2021. Furthermore, There are different community mapping activities as micro-works going on in the region, as the mission of enriching and making freely global spatial data as well as capacity building to the local community on how to use free and open source tools in contributing data to OSM. ## Activities performed by IRDP YouthMappers on 2021 *Shabani Magawila* | *[SHABANI MAGAWILA ](https://www.openstreetmap.org/user/SHABANI MAGAWILA)* [IRDP YouthMappers](https://twitter.com/irdp_mappers) is one of the Active Chapter available in Dodoma, under the supervision of the Institute of rural Development Planning - Dodoma. The chapter has been able to participate in various projects and was able to organize several trainings. With the skills we had we were able to help our fellow students to complete their Field Work Practice. about this event: https://2021.stateofthemap.org/sessions/3CZDTS/

We develop and test user embeddings approaches to vandalism detection in OSM. We successfully demonstrate improvements to previous vandalism detection methods, and additionally how the user embeddings can further be applied to detect different communities of mappers. We validated the embedding model with a prepared vandalism corpus that we are also releasing to the OSM community. With more than 11B edits from 1.6M unique mappers and openly editable by anyone, the OpenStreetMap (OSM) database inevitably contains vandalism. Our approach to detecting it leverages the analytical power and scalability of machine learning through OSM user embeddings. Embeddings are effective in capturing semantic entity similarities that are not explicitly represented by the data. Since word embeddings were first introduced based on the assumption that words adjacent to each other share similar meanings [1,2], the concept of embeddings has been extended beyond word representations to any entity, so long as one can produce a meaningful sequence of the entities. Therefore, we build OSM user embeddings with mappers as entities by constructing sequences of mappers based on shared editing histories and similar behaviors. **Methods** _Creating a Vandalism Corpus_ Development of automated vandalism detection methods in OSM has been slow in part because there is no published corpus of bad or vandalized edits from which to train and validate [3]. Vandalized name attributes are especially problematic because this text is rendered on the basemap. The most infamous instance of this type of vandalism was the changing of "New York City" to an ethnic slur; this name attribute was subsequently rendered on maps drawing from OSM data [4]. As part of this work, we construct and make available the first OSM vandalism corpus for the name attribute of OSM features. Potential examples of vandalism are collected from the OSM Changeset Analyzer (OSMCha) web-based validation tool. These records are then manually reviewed by the Facebook mapping team to identify egregious name changes. Negative samples (non-vandalism) were randomly sampled from a previously validated vandalism-free snapshot of OSM. All of our examples are extracted from OSM data only, no external or conflated data sources. _User Embeddings_ To construct meaningful sequences of OSM users where adjacent users share similar mapping patterns, we analyzed the edit history of every OSM object and the temporal/semantic editing patterns of individual mappers. These sequences were then fed into a word2vec skip-gram model to train OSM user embeddings. **Shared object editing histories** are sequences of OSM users who have edited the same object, in chronological order of editing. These sequences represent mappers who share interest in the same objects on the map. This yields 2B sequences of mappers. **Semantic and temporal mapping patterns** are sequences of OSM users that have shared editing characteristics with regard to how and when they edit the map. Starting with _changesets_, we extract the following keys for each OSM element edited in a given changeset when present: `addr:country`, `admin_level`, `amenity`, `building`, `highway`, `natural`, `place`, `source`. Additionally, we extract the following metadata: the presence of `name` tag, the `version` number, the editing software (e.g. iD editor, JOSM), and any hashtags (possibly denoting specific mapping campaigns). Finally, we group all of these edits by two types of temporal patterns: first, the date of the changeset, and second, the hour of the week of the changeset, per year (with 168 hours in a week, we aggregate across each _week-hour_ in a given year). This yields 30M sequences of mappers. **Results** _Community Detection_ OSM is comprised of many distinct groups of mappers; considering each of these groups a different sub-community makes OSM a "community of communities" [5]. The creation of the temporal and semantic editing patterns was specifically designed to create sequences of mappers with high likelihood of belonging to the same community. One type of easy-to-identify communities are corporate editing teams: groups of employees that are paid to edit OSM [6]. Results of corporate editing team detection can be easily validated against published lists of known editors. The five largest corporate mapping teams are Apple (>1,200 mappers), Amazon (>700), Grab (>550), Facebook (>250), and Kaart (>200). These counts are based on extracting affiliation from a mapper’s OSM user profile, looking for sentences such as “I work for Amazon" and are likely an under-representation [7]. To validate the performance of the model’s ability to successfully identify members of an editing team based on editing semantics, we used the cosine similarity to compare users. First, we identified the 100 most similar users to the _top 10 most active mappers_ in each company (by number of changesets). Next, we confirm how ma

Organized mapping and institution / corporate based mapping keeps rising in the OpenStreetMap community. This talk is inspired by my observations whilst performing Quality Assurance as part of my personal OSM contributions in Ghana; highlighting some problems, solutions and recommendations. Many contributors be it, a hit-and-run, enthusiast or institution are using our ONLY OpenStreetMap data, and the community saves so many things in our daily lives. The quality of data added to OpenStreetMap is of high importance if we have to keep saving lives and all other things. Data quality can not be ignored, this is very much discussed on various mailing lists, channels and whenever we speak about OpenStreetMap, it is likely to be the first question we expect from our audience who had no idea about OSM in the first place. Contributing to OpenStreetMap mostly in Ghana, I have come across a couple of mapping activities that I am sometimes not sure what to do or say and left with only one option; want to know :) ? This presentation will explore some practices in relation to contributing to OpenStreetMap and focuses on Ghana as the Area of Interest. It will demonstrate some of my observations in Ghana: * how they came about and how I will deal with them or have resolved them and; * how most of these can be improved and avoided in the future. At the end of this talk I hope to have convinced you enough to take full responsibility whenever you make a changeset; Quality or Quantity. about this event: https://2021.stateofthemap.org/sessions/EP7JNK/

It Consists in a proposal for a QGIS Plugin for Spatio-temporal analysis of OSM data quality in an area of Brazil. The development of methodologies to evaluate geospatial data quality is one of the most important aspects to be considered while obtaining these data. For the developing countries, such as Brazil, the lack of investment for the maintenance of the topographic mapping, especially on a big scale, is a recurrent challenge to the National Mapping Agencies (ANM) [1]. For example, studies reveal areas in Brazil that have never been mapped and that the topographic mapping in the 1:25.00 scale is nearly 5% of its extension [2]. The technological advances enabled a series of methodologies for obtaining geospatial data [3]. One example is presented as Volunteered Geographic Information (VGI) [4]. In this case, the update of information may occur faster and with a reduced cost in detriment to the traditional structures of topographic mapping [5]. A successful case of VGI is the OpenStreetMap (OSM) platform, which presents the growth in the number of contributors and contributions or mapped features. To comprehend the behaviour of the OSM features and their integration potential to the topographic mapping, different surveys worldwide have put efforts to evaluate its quality, whether by its extrinsic [6, 7] or intrinsic [8] aspects. In this regard, some studies have evaluated the quality of OSM's features by combining extrinsic and intrinsic aspects, like [9], that evaluated the positional precision of OSM based on the combination of the edition's history. Besides that, the most recent researches have focused on comprehending spatial and temporal aspects of events in OSM contributions [10], as well developing add-ons for evaluating data quality, as is presented by [11], that developed a QGIS toolbox to evaluate parameters of the intrinsic quality of OSM features. The literature identifies as one of the main challenges for the integration processes, the heterogeneity of the data. Once the quality may vary according to the study area, the indicator used of even the spatial variations through time in the same region. In this context, to understand the adjustment of OSM's resources to the topographic mapping, it is crucial to connect aspects related to the quality and heterogeneity of data. Researches like [1] argue that, based on the obtained quality, the resources resulting from VGI may be used to integrate, detect changes or report errors. Therefore, classifying resources from OSM according to their usability in a certain region becomes essential, especially in developing countries like Brazil. Besides that, the research that explores issues of quality, heterogeneity, and contributions patterns of OSM is still not widespread in developing countries [12]. Given the importance to classify OSM features according to their usability for a given region, especially in developing countries, few researchers explore quality, heterogeneity, and contribution pattern issues of OSM in Brazil. we proposed a hypothesis that understanding aspects of the extrinsic and intrinsic quality of the quality of OSM features, related to spatiotemporal aspects of contributions in developing countries, will help decision making regarding the influence of the dynamics of insertion of features concerning quality. Thus, this research has as an objective to evaluate the extrinsic quality of OSM features for the county of Salvador-Bahia-Brazil (the northeast region of the country). Therefore, we investigated indicators of positional accuracy, thematic accuracy and completeness, the visualisation of heterogeneity of data, and the analysis of the edition history. To accomplish the evaluation of extrinsic quality, the OSM features were compared to the topographic mapping of the country regarding the Cartographic and Cadastral System of the County of Salvador (SICAD, 2006) and features from the Urban Development Company of the State of Bahia (CONDER). The analysis of positional and thematic accuracy was made through procedures of feature sampling. The analysis of completeness occurred from comparing the total of available features. The verified categories were features from the road system, religious, educational, and health buildings. We divided the municipality of Salvador into sub-regions to identify different local patterns of quality in the analysis of thematic accuracy and completeness. The visualisation allows obtaining the data's heterogeneity through a plugin developed in the software QGIS, making the planimetric positional evaluation for point and line features. The statistical procedures for developing the plugins were realised based on the Brazilian law to evaluate geospatial data quality analysis [13] and based on the method of double buffer proposed by [14]. The plugin is available, and it is possible to be accessed in the online repository https://github.com/eliasnaim/AcuraciaPosicional_PEC-PCD. Even though the fina

This talk presents results of an experiment conducted on the temporal accuracy of OpenStreetMap, and provides insights into the temporal dynamics with which changes in real-life appear in OSM. The ability to provide timely information compared to traditional collection methods of geographic information is generally considered as one of the main advantages of volunteered geographic information (VGI) since its emergence in the 2000s (Goodchild, 2007). In addition to several anecdotal examples illustrating how VGI data can provide more up-to-date information than authoritative sources, the literature provides ample evidence on the usefulness of VGI in applications that require timely geodata, such as disaster management (Horita et al., 2013; Neis & Zielstra, 2014). For example, the Haiti earthquake relief effort in 2010 laid the foundations of how remote contributors of OpenStreetMap (OSM) and other platforms can make a difference and aid responding humanitarian agencies after a crisis (Zook et al., 2010). The Humanitarian OpenStreetMap Team has made numerous contributions and helped save lives at numerous instances ever since (Herfort et al., 2021). However, apart from these examples, the temporal dimension of VGI has not received much research attention outside the application of disaster management, and there is a huge gap between assessing temporal accuracy and other factors of data quality, such as spatial accuracy (Antoniou & Skopeliti, 2015; Yan et al., 2020). Aubrecht et al. (2017) highlighted the lack of formal acknowledgment of temporal aspects in the concept of VGI and proposed a framework called ‘Volunteered Geo-Dynamic Information’ to fully integrate spatial and temporal aspects of VGI. Other works utilizing the temporal component in VGI often focus on the behavior of contributors rather than the currency and temporal validity of map features they contributed (Bégin et al., 2018; Haklay et al., 2010; Neis & Zipf, 2012), or studied the evolution of data over time (Girres & Touya, 2010; Zielstra & Hochmair, 2011). While these approaches are useful, by nature they cannot provide a quantitative measure of how current OSM (or VGI in general) is. Arsanjani et al. (2013) noted during their investigations that the temporal accuracy of OSM could not be measured using their traditional extrinsic method, because OSM data was compared to authoritative data that did not contain temporal information (i.e. most recent street configuration regardless of when road segments were built or renovated). Another project, ‘Is OSM up-to-date?’ recognizes the lack of information on temporal accuracy and developed a tool that uses an intrinsic approach to visually show features that potentially contain outdated information (Minghini & Frassinelli, 2019). However, by nature, an intrinsic approach can also not provide an absolute measure of how up-to-date OpenStreetMap is. This research attempts to fill a gap in the literature by conducting an experiment on the currency of VGI. Using OSM data as a case study, it will measure the temporal accuracy of selected map features. This research overcomes previous limitations by using official data provided by the Florida Department of Transportation (FDOT). The dataset contains details about state-funded highway construction projects, including the date these projects were completed, therefore accurately measuring the temporal accuracy of OSM features is possible by comparing dates projects were finished with the time at which corresponding OSM edits in the database were made. This time difference describes how long it took for the OSM community to adapt to real-world changes and update the map database accordingly. The historical version of highway construction projects was filtered to projects completed between May 15, 2016 and April 1, 2021. Further, only a subset of projects were used, that resulted in either 1) new infrastructure (new roadways, roundabouts or highway ramps), 2) new lanes in existing roadways (excluding bike lanes), and 3) new bike lanes or paths. Other construction projects, such as traffic improvements, road resurfacing, regular maintenance (e.g. bridge rehabilitation), etc. were excluded, since a useful, high-quality road network database can be maintained without the addition of these information, therefore, they are less likely to migrate into OSM. The methodology uses augmented diffs from the Overpass API to find all changes that occurred on OSM highway features (creation, modification and deletion) and are spatially and temporally close to construction projects. These changes are then matched with a record from the highway construction dataset. Irrelevant changes (i.e. changes made to other highway features) are removed. This is done by manually interpreting and evaluating changes and construction projects using a description field (e.g. “SR 61 WAKULLA SPRINGS RD @ CR 2204 OAK RIDGE ROAD INTERSECTION - ROUNDABOUT”). The data extracti

We look at interactions between Corporate and Non-Corporate Editors as reflected through co-editing patterns in the OSM data. We use Social Network Analysis on 12 networks generated from four different locations and 3 different timepoints and our results show the vibrant co-production of OSM data generation. There are interactions between all editors but Corporate Editors tend to interact at a higher rate with each other. The seniority of editors and the interactions also differ between Corporate and Non-Corporate Editors. OpenStreetMap (OSM) data is produced by a vibrant online community of mappers. To be more specific, OSM data produsers represent a plethora of individuals with different motivations, methods of data contribution, and usage (Budhathoki & Haythornthwaite, 2013; Coleman et al., 2009). Thus, OSM contributors have been aptly described as a community of communities (Solis, 2017). In recent years, corporate editing teams have introduced a new dynamic in the discussion on communities in OSM; editing teams hired by corporations, such as, Apple, Facebook, Microsoft, Uber, are capable of contributing thousands of changesets a day (Anderson et al., 2019; Anderson & Sarkar, 2020). Additionally, corporate editors (CEs) tend to focus their editing on particular types of map features. These two attributes of corporate editing can lead to CEs breaking off into a siloed group of their own with little or no interaction with the rest of the editors on the map. Previous research on the OSM community using similar methods showed there was limited collaboration between editors with most objects being edited only a few times (Mooney & Corcoran, 2012). Senior editors in particular perform a majority of the mapping work on their own, but do interact with others through co-editing (Mooney & Corcoran, 2014). Since these studies were performed, the OSM community has grown significantly and the community dynamics have also evolved with more individual and organized participation (e.g. CE). Here, we use a data driven approach to characterize the interactions between the CEs and the rest of the OSM community. We define interactions through editing patterns. That is, we construct a network of interactions where each node represents an editor, and two nodes are connected if they have edited the same map object. If the mapper of node A edits an object last edited by the mapper of node B, then an edge connecting these nodes exists and is directed from A to B. We utilized the OSM-Interactions tilesets to construct these networks (Anderson, 2020). These vector tiles contain the editing history of all highway and building objects at zoom level 14. They include minor changes to the geometry of objects in which only nodes are moved, but the parent way is left untouched. In this way, we are capturing the complete history of map objects in OSM, as opposed to just changes to the basic OSM elements (primarily nodes or ways). In keeping with the objects which are primarily edited by CEs, we focused only on highway and building objects for construction of the network. The nodes are further annotated with a binary category representing whether they are a CE or not. We classify a mapper as being a CE or not by comparing usernames in the network to the disclosed lists of usernames on a corporation’s OSM wiki or Github page. We focus on 4 locations: Egypt, Jamaica, Thailand, and Singapore. We create networks for each of these locations at 3 timepoints, 2015, 2017, and 2019 to characterize the changes between over time. Thus, we constructed and analyzed 12 networks. The locations were chosen as they all have different groups of CEs active. Across all networks, the Largest Connected Component (LC) accounted for 93.6% of all nodes highlighting significant interactions amongst all mappers. Within the LC, the rate of growth of CE nodes exceeds the rate of growth of non-CE nodes at rate of 11:1 between 2015 and 2019. However, both types of editors (CE and non-CE) have a comparable number of in and out degrees in each place, indicating that they edit other people’s work and have their work edited at a similar rate. In terms of who edits whose work, CE’s edit other CE’s work most often, but interaction between CEs and non-CEs have also grown through time, keeping the network connected. With regards to age of the mappers (calculated in terms of their enrollment date in OSM) and the volume of edits they perform, younger mappers in both groups tend to edit others' work at a higher rate than senior mappers, but there is more variation in these statistics for non-CE mappers. This is a finding contrary to previous research on editing interaction patterns mentioned above. Additionally, characterizing the time between edits show that edits made by CE’s persist for a slightly shorter duration than edits made by non-CE, primarily due to other CEs editing the same object soon after. In conclusion, the editing networks highlight the vibra

OpenStreetMap (OSM) constitutes a new open geographic database and offers several possibilities of adding local knowledge. While the importance of local knowledge is largely acknowledged in the OSM community, relatively few scientific studies have evaluated them. This study presents a framework to measure local data contribution in OSM in three case studies. The results highlight a framework for measuring local data in OSM as well as the distinct mapping stories of local OSM communities. OpenStreetMap (OSM) has proven to be a valuable source of spatial data for many applications, including humanitarian aid. Information on buildings and roads - that can be provided by remote mapping - is of highest concern for many humanitarian applications. However, further information - that can only be mapped on the ground - is of high importance for finer scale humanitarian action. Road surface information, type of material and information on the use of a building (health site, school,...) is highly relevant. OSM offers several possibilities of adding local knowledge [1]. Recent works deals with analyzing and classifying data production in OSM [2] and intrinsic analysis has gained popularity as an indicator for measuring quality of OSM data [3–6]. Nevertheless, relatively few scientific studies have touched on "local knowledge" and local data in OSM in sufficient detail. The question of how much local knowledge is added and what kind of local data is added remains unanswered. Addressing this question is important since only local knowledge provides access to the plethora of contextual information that is necessary for many purposes. The term "local knowledge" is often debated in the OSM community due to its ambiguity. Consequently, it is hardly taken into account by researchers when evaluating OSM [1]. This study presents a metric to measure local data contributions in OSM and analyzes temporal patterns of local contributions at three case studies. The aim of the metric is to identify archetypes of places representing a variety of contextual information. Firstly, we evaluated Rebacca Firth's framework on OSM contribution types that focused on the humanitarian context (see Twitter post: https://t.co/rDaSraiVZF). Secondly, we discussed with local community working groups how to measure local data contributions ("What exactly are local OSM data to you?"). The outcome of the community discussion provided valuable information to design a generalized workflow for measuring local data contribution in OSM. Subsequently, we identified aspects on which the local communities agreed with respect to perception of local data. Based on this first insights, we developed a classification schema for measuring local data in OSM that is "fit-for-purpose" for local OSM communities. This schema consists of four main levels and assigned OSM tags that could be used as indicators for each level. Thirdly, we explored the temporal evolution of local data in OSM for three unique regions. These regions mapping activities are influenced by local mapping organizations. (i) Ramani Huria in Dar es Salaam, Tanzania, focusing on flood resilience (ii) Crowd2MAP mainly operating in the Mara region, Tanzania and focusing on identifying features that can support the fight against girls and women at risk of female genital mutilation, and (iii) Power mapping project by Youth Mappers in the Koindugu, Sierra Leone, focusing on mapping electrical grid infrastructure. We used the ohsome API to access the full history of OSM. We determined the density and the ratio (as the sum of all OSM tags to the number of OSM elements) per month for each region and localness level. The outcome of the community discussion showed that local mappers/editors had different perceptions about local knowledge. The type of local data produced depends on: (1) the context within which the data is produced and (2) the character/interest of the individual mapping them. However, the local data produced could be broadly categorized as "core" or "specific". The "core" category consisted of the objects that cut across almost all projects or activities (e.g., buildings, roads, place names and administrative boundary) and the category "specific" were special elements mapped as a results of a particular interest or aim of the project (e.g., culvert, drains, access types, parking type). To develop a metric for local data analysis, we classified OSM data into four main levels based - level one consists of objects that can be derived easily by remote mapping from satellite images such as roads and building (this is information that does not require local knowledge), level 2 focuses on place names and administrative boundaries which are frequently imported, level 3 focuses on the presence of general (e.g., residential and commercial) or specific amenities (e.g., school, clinic, and point of interest) and level 4 focuses

Machine Learning is incredibly popular at this time among researchers working with OSM data and on OSM-related problems. But what impact has this work on ML had on the OSM database or OSM community? We investigate the impact on OSM, if any, the ML work within the academic research community has had over the last few years. # What has machine learning ever done for us? Peter Mooney and Edgar Galvan,<br /> Department of Computer Science, <br /> Maynooth University, Maynooth. <br /> Co. Kildare. Ireland. <br /> [email protected]; [email protected] <br /> ### Introduction and background Recently, machine learning (ML) and artificial intelligence (AI) based approaches are being applied frequently to many different types of problems in OpenStreetMap (OSM). Indeed, ML and AI have being used extensively by the research community for a plethora of applications and problems both related and unrelated to OSM. Wagstaff (2012)[1] suggests ML offers "a cornucopia of useful ways to approach problems which defy manual solutions". In specific relation to the geospatial domain, ML approaches have been reported at least as early as a decade ago with work by authors such as Werder et al. (2010)[2] on interpretation of buildings in settlements and detecting road intersections from GPS traces by Fathi and Krumm (2010)[3]. Around this time, interest in the combination of ML and OSM began to emerge. Funke et al. (2015)[4] argued that many aspects of OSM data might be suitable for "extrapolation or classification using ML". Many examples have emerged with ML approaches being used to consider problems such as: predicting or recommending tagging for objects, object classification based on contextual or proximity information, tag usage checking, automated mapping approaches, to mention some problems. Jennings et al. (2019)[5] showed that Facebook’s recent mapping campaign in OSM used ML to detect road networks from satellite imagery which are then validated by OSM editors and the local OSM communities. Examples also exist where OSM is used in ML approaches for other geospatial classification problems (Wu et al. (2020)[6], Jacobs and Mitchell (2020)[7]) while authors such as Feldmeyer et al. (2020)[8] used machine and deep learning algorithms with OSM for developing socio-economic indicators. Audebert et al. (2017) provided additional examples and argued that OSM's richness means it can be used in difficult problems such as semantic labeling of aerial and satellite images. In addition to the observations by Vargas-Munoz et al. (2021) in their recent review of ML approaches in OSM, we can usually observe ML and OSM interaction in one of three ways: (1) ML approaches are used to improve or correct OSM data, (2) instances where OSM is used as a means of training ML models for some specific task such as building segmentation, road speed estimation (Keller et al, 2020 [10]) or land use classification (Schultz et al. 2017 [9]) or (3) where the contribution patterns of OSM contributors are analysed using ML techniques as in work such as that by Jacobs and Mitchell (2020)[7]. In this submission we ask the following question. With all of the many applications and integration of ML and AI with OSM, over the past number of years, how many of these applications and approaches have been adopted or used by the OSM community? Furthermore, what are the benefits or impact of these efforts from the research community with ML and AI approaches to the OSM project and OSM community? We believe that there is significant scope for ML researchers to make impactful and helful contributions directly within OSM on problems such as tag updating and correction, added intelligence within OSM editing software, intrinsic quality analysis, etc. ### Methodology and Findings achieved A systematic review of approximately 60 peer-reviewed academic journal and conference papers will be reported. These papers are selected on the following basis that the paper(s): (1) clearly outlines an ML or AI approach using OSM data, (2) tackle a problem known in the OSM community such as tag prediction, contribution patterns, or geometry correction. Paper metadata such as title, keywords, and abstract contents are used to select the papers. Manual checking of the papers is also undertaken to ensure that the content of each paper relates to our selection criteria. A classification of these papers will be developed based on the following set of questions: * What are the most common ML approaches used by researchers for the three instances outlined above? For example, Learning Problems (supervised, self-supervised, reinforcement), Statistical Inference (Inductive, Deductive), etc. * What are the most common types of problems in OSM tackled by ML approaches? For example, automated tagging, contribution pattern analysis, intrinsic quality analysis, object classification, etc. * Are the approaches reproducible and replicable for other re

NLMaps Web is a web interface for querying OSM with natural language questions such as “Show me where I can find drinking water within 500m of the Louvre in Paris”. They are first parsed into a custom query language, which is then used to retrieve the answer by queries to Nominatim and Overpass. Nominatim and Overpass are powerful ways of querying OSM, but the Overpass Query Language is somewhat impractical for quick queries for unfamiliar users. In order to query OSM using natural language (NL) queries such as “Show me where I can find drinking water within 500m of the Louvre in Paris”, Lawrence and Riezler [1] created the first NLMaps dataset mapping NL queries to a custom machine-readable language (MRL), which can then be used to retrieve the answer from OSM via a combination of queries to Nominatim and Overpass. They extended their dataset in a subsequent work by auto-generating synthetic queries from a table mapping NL terms to OSM tags – calling the combined dataset NLMaps v2. [2] The proposed purpose of these datasets is training a parser that can parse NL queries into their MRL representation, as done in [2-5]. The main aim of my Master’s thesis was building a web-based NLMaps interface that can be used to issue queries and to view the result. In addition, the web interface should enable the user to give feedback on the returned, either by simply marking the parser-produced MRL query as correct or incorrect, or by explicitly correcting it with the help of a web form. This feedback should be directly used to improve the parser by training it in an asynchronous online learning procedure. After observing that parsers trained on NLMaps v2 perform poorly on new queries, an investigation into the causes for this revealed several shortcomings in NLMaps v2, mainly: (1) Train and test split are extremely similar limiting the informativeness of evaluating on the test split. (2) Various inconsistencies exist mapping from NL terms to OSM tags (e.g. “forest” sometimes mapping to natural=wood, sometimes to landuse=forest). (3) The NL queries’ linguistic diversity is limited since most of them were generated with a very simple templating procedure, which leads to parsers trained on the data not being very robust to new wordings of a query. (4) In a similar vein, there is only a small amount of different area names in NLMaps v2 with the names “Paris”, “Heidelberg” and “Edinburgh” being so dominant that parsers are biased towards producing them. (5) Some generated NL queries are worded very unnaturally making them counter-productive learning examples. (6) Usage of OSM tags is sometimes incorrect, which affects the usefulness of produced parses. The detailed analysis is used to eliminate some of the shortcomings – such as incorrect tag usage – from NLMaps v2. Additionally, a new approach of auto-generating NL-MRL pairs with probabilistic templates is used to create a dataset of synthetic queries that features a significantly higher linguistic diversity and a large set of different area names. The combination of the improved NLMaps v2 and the new synthetic queries is called NLMaps v3. A character-based GRU encoder-decoder model with attention [6] is used for parsing NL queries into MRL queries using the configuration that performed best in previous work [5]. This model is trained on NLMaps v3 and used as the parser in the newly developed web interface. Mainly through advertising on the OSM talk list and the OSM subreddit, 12 annotators are hired from all over the world to use the web interface to issue new NL queries and to correct the parser-produced MRL query if it is incorrect. They are assisted by completing a tutorial before the annotation job and by help compiled from taginfo [7], TagFinder [8] and custom suggestions for difficult tag combinations. The collected dataset contains 3773 NL-MRL pairs and is called NLMaps v4. With the help of NLMaps v4, an informative evaluation can be performed revealing that a parser trained on NLMaps v2 parses achieve an exact match accuracy of 5.2 % on the MRL queries of the test split of NLMaps v4 while a parser trained on NLMaps v3 performs significantly better with 28.9 %. Pre-training on NLMaps v3 and fine-tuning on NLMaps v4 achieves an accuracy of 58.8 %. Since the thesis’s goal is an online learning system – i.e. a system that updates the parser directly after receiving feedback in the form of an NL-MRL pair –, various online learning simulations are conducted in order to find the best setup. In all cases, the parser is pre-trained on NLMaps v3 and then receives the NL-MRL pairs in NLMaps v4 one by one, updating the model after each step. The most simple variant of the experiment uses only the one NL-MRL pair for the update, another variant adds NL-MRL pairs from NLMaps v3 to the minibatch and a third variant additionally adds further “memorized” NL-MRL pairs from previously given feedback to the minibatch. The main findings of the simulation are that all variants improve

Between December 2020 to March 2021, the OSM Kenya team ran a project whose focus was on growing the OSM community locally: both in terms of membership and diversity. The project was supported through the Facebook and HOTOSM community impact microgrants. In this session, we will share about the community, project: from ideation(community health), motivation to implementation. This will include the activities involved, our experience, challenges that we encountered, and the lessons learned. Being one of the awardees for the 2020 Community Impact Microgrants by HOTOSM and Facebook, the OpenStreetMap community in Kenya ran a virtual three-month training program for women and girls interested in OSM. While the project was implemented towards the end of 2020, the ideation process began at the start of the year when discussing community health and brainstorming on activities to conduct virtually due to COVID-19 restrictions. This included carrying out a community survey, identifying gaps and challenges, and figuring out what next. Our key focus was community growth, especially in terms of diversity and membership. We had some virtual activities that introduced new mappers to the community but still saw no diversity improvement. We, therefore, designed a women-only training program that would run for eight weeks. We were able to train a group of 35 beginner-level and intermediate-level mappers on all aspects of open mapping. Activities included hands-on workshops, presentations, and mapathons. During this period, we had some successes, learned a lot, but we also had a good share of challenges. We hope that by sharing our experience, other communities interested in implementing similar programs can learn more about the same; we also hope to have discussions that would improve future iterations of the program. about this event: https://2021.stateofthemap.org/sessions/L9XART/

Mappers see the OpenStreetMap Standard layer every time they view OpenStreetMap.org, but who else is using the layer? With usage logs, who is accessing what is broken down for this important OpenStreetMap service. The OpenStreetMap Standard layer is one of the high profile OpenStreetMap services, but not many people know who uses it, and where. This talk covers the goals of the service, it’s history, how it's designed, and dives deep into how it's used. Using improved logging functionality, we’ll get answers to these questions and more. We’ll cover the policy and technical basics of the service, showing how changes make it to your browser through the various elements of the stack and what we set out to do by running the standard layer, including policy and history. Diving deep into the usage, we’ll look at where people are viewing, where they’re from, daily and weekly usage patterns, and then into what sites and apps people use to view the layer, including the long tail of small sites. You’ll see how different uses put different loads on the OSMF servers, and how load doesn’t come from where people think it is. Many people just want to see their edits on the map, and we’ll cover that too, looking at how they show up and what to do if they don’t. Along the way we’ll learn how to see your changes on the map and see heatmaps, treemaps, and find out just what’s up with the teapots. about this event: https://2021.stateofthemap.org/sessions/GGLYCK/

Pedestrians have been neglected. We’ve seen monumental progress in digital maps, but much of this has been road centric. In this presentation we download data from OpenStreetMap that relates to pedestrians to see how much it differs from the reality on the ground. We contrast different types of cities, seek to understand why pedestrian data is lacking, and look at solutions such as Mapillary that can help make OpenStreetMap more pedestrian friendly. The evolution of digital maps of the last 20 years has been nothing short of incredible. The experience for the end consumer has continued to improve, with better map data, more intuitive interfaces, and greater portability. A lot of the developments have focused on in-car navigation, with Google Maps, Apple Maps, HERE Maps, and TomTom dedicating significant resources to the space. Even in our beloved OpenStreetMap, vehicle based navigation claims centre stage. In this presentation we’ll explore the state of pedestrian data in OpenStreetMap, how it differs between cities, why it’s important to think about, and how we might collectively improve the quality of pedestrian data. To begin with, we’ll take a look at data downloaded with Overpass Turbo. The data represents nodes, ways, and areas with pedestrian relevant tags such as highway=footway and sidewalk=both. Our analysis focused on five cities with differing characteristics: - Folsom, USA - Heidelberg, Germany - Melbourne, Australia - Stone Town, Tanzania - Yesan, South Korea These cities differ in population, cultural characteristics, urban planning, history, and topography. We’ll explore what kind of OpenStreetMap tags have been used in each city, how close this matches the state of pedestrian infrastructure, and how the cities compare to one another. We’ll then look at some reasons why pedestrian data has been neglected including the limitations of satellite imagery, commercial incentives, and data collection methods. In the final part of the presentation we’ll propose and hopefully discuss some of the tools that could help including Mapillary, StreetComplete, GoMap!!, and Vespucci. about this event: https://2021.stateofthemap.org/sessions/RFGVSG/

The code samples, status, recent development and roadmap of the open-source community driven project for rendering of (not only) OpenStreetMap vector tiles in a web browser (GL JS) and with native code (Android, iOS, etc). After Mapbox announced the closure of Mapbox GL JS, their JavaScript library for displaying maps using WebGL, the community around Hacker News gathered on Slack and GitHub and made a collective decision to maintain and further develop the last open-source version of the software and build a 100% free alternative of the project. This is how the MapLibre was born. As a group of individuals, we coordinate the effort and synchronize contributions from multiple teams (MapTiler, Amazon, Facebook, Elastic, Stadia, Microsoft, Jawg, GraphHopper, Toursprung, etc) - working on JavaScript and Native code implementation of the renderers and related ecosystem. Multiple releases have been published, the project has CI checks for contribution, regular steering committee meetings, updated support for TypeScript, several bindings such as ReactJS, the Metal rendering on iOS is implemented (as Apple decided to deprecate OpenGL ES), and many issues and bugs has been fixed. There is plenty of ideas what to do next - from implementation of 3D terrain rendering, to support of non-Mercator map projections, or tighter integration with Leaflet, and much more. Let's explore the current status of the project, learn how to use MapLibre in your own software with practical code samples, and how to join and contribute to the collaborative development and participate on a shared roadmap. about this event: https://2021.stateofthemap.org/sessions/RC7S8K/

Since moving to Ireland from Germany in December 2016, I have been mapping heritage features on the island increasingly. During lockdown, I started a YouTube channel to enable other people who are interested in Irish history to learn about OpenStreetmap, because it is still quite unknown in those circles. After moving to Ireland and shortly after becoming a member of a local historical society, I started mapping historical features like ringforts, castles, church ruins and graveyards. As a member of that group, I noticed that the awareness of OpenStreetMap and OpenData is very low in this group and in Ireland in general. One of the excuses often given by these groups is the demographic which is less computer literate than my generation. However, after uploading a few OSM tutorials onto YouTube, I received good feedback and two female acquaintances of that age group started actively contributing to OSM. Motivated by those developments and by looking for something productive to do during the third COVID lockdown, I started a YouTube tutorial series on OSM, uMap, overpass turbo and fieldpapers to show the benefits of OSM to historically interested individuals and groups. Within only a few weeks, I had gathered a small following and continue getting positive feedback from people who discover OpenStreetMap for their personal or group projects. I believe that giving the public concrete application examples of OSM rather than trying to convince them by dropping terms like OpenData and OpenSource into the conversation, is a way to promote OpenStreetMap to an audience that is keen to learn and to apply the knowledge. It is a time consuming process, but necessary for a shift in awareness about OpenStreetmap. about this event: https://2021.stateofthemap.org/sessions/ZCUPCF/