Chaos Computer Club - archive feed

Infrastructure as Code, Continuous Integration und viele weitere Schlagworte sind in aller Leute Munde. Doch wie soll das Umgesetzt werden ?<P> Der Vortrag gibt eine Übersicht wie Systemkonfiguration mit Puppet für grosse und kleine Umgebungen umgesetzt werden kann. Der Vortrag gibt einen Überblick und soll helfen Zusammenhänge zu verstehen. Der Fokus wird auf dem Aufbau und der Architektur einer Puppet Umgebung sein. Einige Beispiele sollen die erweiterten Möglichkeiten von Puppet aufzeigen. Vorkentnisse: Keine about this event: https://programm.froscon.org/2022/events/2809.html

How much effort does it take to build a Libre bicycle computer? I found out so you don't have to. Jazda.org is a project I started this year. It combines my 3 favorite things: bicycles, Rust the programming language, and tiny, networkless electronics. I will introduce you to the concept of a bike computer, and show why building one differs from building your typical gadget. about this event: https://programm.froscon.org/2022/events/2851.html

This talk will cover topics related to providing relevant recommendations to users. We don’t aim to declare one recommendation method as the best but instead highlight different approaches to enriching recommendations by combining machine learning with graph databases. The methods we evaluate include: - Matrix Factorization with Graph Embeddings - Content-based TFIDF - Cosine Similarity with AQL and User Ratings The talk will briefly cover the methods and how we generated the distance metrics and provide notebooks that go into further detail. We will show how we integrated these findings into a frontend application for movie recommendations. The talk aims to show how pairing machine learning with graph databases can improve the quality of recommendations and offers some insights into the challenges of productionizing machine learning models. Topics: Machine Learning, Python, Data science, MLOps, Visualization about this event: https://programm.froscon.org/2022/events/2735.html

Bei der Eisenbahn wird seit ungefähr 150 Jahren Sicherheit (=Safety) gelebt. Seit kurzem kommen diese komischen Nerds und wollen der Bahn ihre Sicherheit (=Security) beibringen. Das muss ja schiefgehen, oder? Ein hoffentlich sehr unterhaltsamer Überblick über die Bahn und die Herausforderungen der Informationssicherheit bei der Bahn Die Eisenbahn ist der faszinierendste Verkehrsträger der Welt. Gleichzeitig ist das System Rad-Schiene die ökologischste Form des Transports wenn das Fahrrad nicht mehr ausreicht. Die Politik verlässt sich darauf, dass wir bei der Eisenbahn in den nächsten Jahren viel mehr Güter und Menschen umweltfreundlich an ihr Ziel bringen. Damit das klappt, müssen wir das Schienennetz in Deutschland umfassend digitalisieren, ohne die hohe Zuverlässigkeit und Sicherheit der Eisenbahn zu riskieren. Im Vortrag berichte ich aus der Praxis der Informationssicherheit der DB Netz AG, wie wir an der Zukunft des digitalen Schienennetz arbeiten und von 150 Jahren gelebter Fehlerkultur bei der Eisenbahn profitieren um die Zukunft der Bahn sicher zu gestalten. about this event: https://programm.froscon.org/2022/events/2758.html

The lack of data on the distribution of the water resources, possess a great challenge for the water resource investment and AI/ML-enabled advancements in the water sector compared to all other sectors like heath. This paper describes the methodology for combining different water mapping schemas to create comprehensive multi-platform water infrastructure data and enhance rapid updates to support a suite of water resource analytics and extended advanced technology explorations towards improved decision-making. Access to clean and safe drinking water is critical to public health and socioeconomic prosperity, yet an estimated quarter of the world’s population lacks such. This was evidenced by the unprecedented outbreak of the COVID-19 pandemic, which left communities extremely vulnerable to fatal illnesses due to the limited access to water for handwashing or lack of knowledge of the existence of the utility. Subsequently, the lack of data on the distribution of the water resources poses a great challenge to the water resource investment and AI/ML-enabled advancements in the water sector compared to all other sectors like heath. Influencing the frequency of water point data collection through crowdsourcing and volunteered geographic information, would greatly improve the availability of water point data, and contribute to the extended roles of water resource distribution, monitoring, and management especially in rural communities. Therefore, this paper describes the methodology for combining different water mapping schemas to create comprehensive multi-platform water infrastructure data and enhance rapid updates to support a suite of water resource analytics and extended advanced technology explorations towards improved decision-making. The recent technological advances including the web 2.0, cameras, smartphones and sensor networks continue to empower the development of empirical methods as well as the generation of big data and analytical platforms that provide predictive performance on the various socioeconomic needs for sustainable development. OpenStreetMap (OSM) is a crowdsourcing platform which offers a collaborative experience through its database, community, and wiki platforms, to create and update data relevant to support or transform various data deficiencies whether humanitarian or planning. However, the project’s data quality shortcomings often hinder simultaneous data integration with other analytical platforms such as the Water Point Data Exchange (WPdx) that would explicitly maximize the usage and application of these crowdsourced data. Through a project dubbed ‘Water Infrastructure Mapping Uganda’, a data model based upon open mapping methods and survey tools was developed to facilitate the mapping of water infrastructure data points and simultaneous updates of both the WPdx and OSM databases. The project engaged a comprehensive review of the OSM water tag, rural water infrastructure data standards and the WPdx database to generate a survey data form that supported one-time collection of a water point for both OSM and WPdx databases. Underlying the development of the data model/schema in the overall project, a design criterion was established which guided and justified the overall selection of the most relevant factors to include in the process that would eventually become detailed to communicate water infrastructure and functionality. The criteria were followed by an assessment of the; compliance [agreement of the tag], consistency [temporal and spatial representation of the tag], completeness [attribute description of the tag], and granularity [quality of the event information] of the OSM tag to support the development of the. osm language in the Kobo toolbox. Gulu district, located in the North of Uganda, was identified as a potential pilot area for improving the approach created by the project based on its rich WPdx footprint as well as a well-established OSM community of YouthMappers. Up to date satellite imagery of up to 50cm spatial resolution was acquired through the USAID GeoCentre to facilitate any visual detection of water points, and the digitization of base map data including, buildings, roads and waterways, to be employed in the field mapping exercise. A field mapping workflow was designed to facilitate the field-data collection employing the developed water infrastructure data model and Kobo toolbox. An API link was developed that simultaneously tapped the open-source field collected data into the WPdx database. Through the project, more than 15000 buildings, 1400square kilometres of roads and over 500 water data points were added to OSM as well as the WPdx database for the later data. Also, from the project, several observations were made regarding the improvement of such processes and the extension of the data model beyond one geographical area. The developed workflows characterized and provided a general improvement in the water infrastructure data quality especially for OSM

Einfaches, effizientes und schnelles Monitoring mit Open Source – das auch noch Spaß macht Beim Thema Monitoring haben die meisten gleich Nagios im Kopf, zusammen mit eingestaubten Konfigurationsdateien und der Unlust das irgendwie anpacken zu müssen. Bei openITCOCKPIT erfolgt die Konfiguration ausschließlich über die Weboberfläche, kann aber auch automatisiert über die API abgewickelt werden. Mit dem plattformunabhängigen openITCOCKPIT Monitoring Agenten ist das Basis-Monitoring in weniger als 2 Minuten eingerichtet. Die Kommunikation ist selbstverständlich verschlüsselt und danke Pull oder Push Modus lässt sich jedes System abfragen. Ein Monitoring-System besteht heutzutage aus vielen Komponenten wie Datenbanken, Grafana, Visualisierung usw. openITCOCKPIT übernimmt die Verwaltung aller benötigten Tools und ist gleichzeig 100% kompatibel zu Nagios. Vor Updates braucht man also keine Angst mehr zu haben. Somit liegt der Fokus für den Anwender beim Monitoring. openITCOCKPIT steht für Debian und Ubuntu als amd64 und arm64 Pakete zur Verfügung. Freiwillige vor! Gerne überwache ich wären der Demo euer Notebook (Linux, macOS oder Windows). about this event: https://programm.froscon.org/2022/events/2773.html

Using open source is easy! The great thing about open source is that you can adapt it, apply patches, include your own code snippets, … That is, until you realize that you now have more and more projects to maintain on your own. You need to re-patch new versions, import security patches into your local branch of the software or manually apply updates. In short, you are in maintenance hell. Using open source is easy! The great thing about open source is that you can adapt it, apply patches, include your own code snippets, … That is, until you realize that you now have more and more projects to maintain on your own. You need to re-patch new versions, import security patches into your local branch of the software or manually apply updates. In short, you are in maintenance hell. In this session, we will go through a few arguments for avoiding this maintenance nightmare. What can you do to minimize the constant time you have to spend on adopted software projects, how can you compromise if you need patches after all and (always important) how can you convince your managers that sometimes spending a bit more time right now is what you should do. Topics - Problems you see when maintaining custom patches - Upstream first: Getting your patches upstream and apply them with the next regular update - Arguments for management: Why it's better and cheaper - But what if I need to patch anyway? Techniques for slowly reducing local patches anyway about this event: https://programm.froscon.org/2022/events/2783.html

This talk is a hands-on introduction to fuzz testing. After a basic introduction to fuzzing we will give a live demonstration of our open source fuzzing tools, supporting C/C++, Java, JavaScript and Go. They will showcase modern state-of-the-art fuzzing approaches and demonstrate the different kinds of bugs one can detect. To get everyone on board we will take a short tour through the history and fundamentals of fuzzing before we look at the current state of fuzzing including code instrumentation for coverage guided fuzzing and bug detectors. We will find out what kind of bugs and vulnerabilities can be found with these techniques. We will do this by taking a look on how we use this modern approaches at Code Intelligence (Bonn, https://www.code-intelligence.com/) to make fuzzing as easy as writing unit tests, including demonstrations of our OSS tools Jazzer (https://github.com/CodeIntelligenceTesting/jazzer) and cifuzz (https://github.com/CodeIntelligenceTesting/cifuzz). about this event: https://programm.froscon.org/2022/events/2772.html

Tips und Best-Practises für PostgreSQL Administration und Betrieb für Leute deren Job eigentlich ein anderer ist. PostgreSQL ist die fortschrittlichste Relationale Open Source Datenbank. Es bietet eine große Anzahl an Features und ist dabei für die meisten Workloads relativ einfach zu managen, vor allem wenn ein Managed Service verwendet wird. Wenn allerdings kein DBA vorhanden ist muss trotzdem jemand nach den Postgres Servern oder Services schauen; andernfalls wird die Performance irgendwann einbrechen oder irgendwelche Limits/Fehler auftauchen. Dieser Vortrag gibt einen kurzen Überblick über PostgreSQL, welche minimalen anfänglichen Tuning-Maßnahmen ergriffen werden sollten und was die aktuellen Limits für einen hauptsächlich unüberwachten Betrieb sind. Er wird auch einige Best-Practices für Installation und Konfiguration aufzeigen und potenzielle Fallstricke aufzeigen, die man Vermeiden sollte. Er ist für System-AdministratorInnen, DevOps-Engineers oder Software-EntwicklerInnen gedacht, deren primäre Rolle nicht Datenbank-Management ist, die sich aber aus dem einen oder anderen Grund mit dem Betrieb von PostgreSQL-Instanzen befassen müssen. about this event: https://programm.froscon.org/2022/events/2787.html

This talk shows how to enhance GitOps by putting observability and Service Level Objectives in the center of the deployment process, based on CNCF projects like Argo and Keptn. GitOps has arrived and shapes today’s way of delivering applications on cloud-native systems. Although GitOps controllers maintain the technical state of an application, there are additional – not necessarily technical – things to consider when deploying applications. For instance, we might want to know if we have already consumed our error budgets before deploying a new service version and make this step a conscious decision. Additionally, we want to ensure that the response time of our service is as expected (and agreed with the customer) before shifting the traffic to a new service version in blue/green or canary deployments. This session starts with a concise overview of GitOps, the problems it solves, and other things to consider when deploying enterprise-grade applications using GitOps. After this talk, you will know how you can put observability at the center of your deployment process and how this works based on an example with ArgoCD and Keptn. about this event: https://programm.froscon.org/2022/events/2752.html

OpenStreetMap (OSM) data has the potential to facilitate bottom-up approach to transport planning which is essential for localized data-driven policy interventions. Given this, OpenInfra project is exploring the potential of OSM data in transport research with a focus on active travel. The exploration showed that currently missing data limits the applicability of OSM data. Nevertheless, we argue that the potential and relevance of OSM data can be demonstrated by recategorizing OSM data to provide more actionable insights to policy-makers. This, therefore, could encourage the uptake of open data leading to more transparent, reproducible, and participatory transport planning. One of the key domains in which OpenSteetMap (OSM) data has been utilized is transport research [1]. OSM has been used in agent-based transport simulation [2] and routing [3], including cycling [4], walking [5], wheeling [6], and blind pedestrian routing [7]. Another application of OSM data is in transport infrastructure planning. Nelson et al. [8] argue that OSM has the potential to become a primary source of data on infrastructure across the globe. Regardless of OSM’s potential to become a primary source of data on infrastructure, its potential in active travel infrastructure planning is yet to be realized. One of the potential reasons behind this lag might be linked to the perceived unreliability of open-access crowdsourced data [9]. The quality of OSM has received extensive examination [1] in which the question concerning data completeness plays a significant role because, it is argued, the mappers are not coordinated to guarantee systematic coverage [10]. To address this issue, Barrington-Leigh and Millard-Ball [11] assessed OSM road completeness and found that globally over 80% of roads are mapped. Problematically, however, their assessment focused on roads designed for motor traffic, thus excluding other modes of transport. This gap has been partially addressed by Ferster et al. [12]who examined and compared OSM cycling infrastructure in Canada. They have not, however, considered the infrastructure from the perspective of accessibility. Moreover, there seems to exist no equivalent study using OSM data in the context of pedestrian infrastructure planning. Nevertheless, open-access crowdsourced data, such as OSM, can support an increasing need for local evidence to inform transport policies. This is important in the context of the UK in which a shift from provision for motorised modes towards more sustainable active modes of travel, such as walking, wheeling, and cycling, takes place [13]. The importance of localizing interventions to meet the needs of local communities has been outlined in both policy [15] and academic [16] papers. A potential way to engage citizens in the decision-making is to encourage “produsage” – a model in which citizens both produce and use data [17]. Acknowledging the potential of OSM to boost citizen participation, OpenInfra project, run at the University of Leeds (UK), aims to address the gap of literature regarding the potential OpenStreetMap in transport research. The project started by examining the existing OSM tags relevant to active travel infrastructure in England with a focus on West Yorkshire, Greater Manchester, Greater London, and Merseyside. The data has been queried using osmextract [18], a package in R, and explored using exploratory data analysis (EDA) approach. A reproducible code containing all the figures discussed here can be found on GitHub: https://github.com/udsleeds/openinfra/tree/main/sotm2022 Given the extensive use of OSM data in transport research, it is not surprising that OSM provides a comprehensive active travel network, yet there is a lack of specification concerning the type of infrastructure that is present (e.g. is it a cycle lane or a cycle track?). For instance, cycleways and footways constitute about 1/3 of all the mapped highways on which one can legally walk, wheel or cycle but only a few percent of the cycleways and footways have tags detailing their type. The data gets even scarcer in the context of accessible infrastructure planning. For example, there is a lot of missing information on the presence and type of kerbs – a street element that might make the movement of a wheelchair user more challenging [19]. The missing data currently limits the use of OSM data in active travel planning, however this does mean that the use of OSM data should be dismissed. Following Nelson et al.’s [8] argument that it is important to make crowdsourced data more actionable, we decided to recategorize OSM data based on Inclusive Mobility (IM) [15], a guide that outlines the best practices in creating inclusive pedestrian infrastructure in the UK. For this, a function has been written (documentation can be found here: https://udsleeds.github.io/openinfra/articles/im_get.html). It takes an OSM dataframe, recategorizes its tags based on the definitions outlined in the guide, and return

Lighting talks registered during the State of the Map conference. ## Offline Web Mapping Server UNVT Portable The United Nations Vector Tile Toolkit. _by Shogo Hirasawa, Taichi Furuhashi_ ## Liaising OpenStreetMap (OSM) Community and Research Community with the Policy Makers: Reducing the Data Gap in Disaster Management _by Airin Akter, Shraddha Sharma_ ## Unique Mappers Network: The OpenStreetMap Community NGO in Nigeria _by Victor N. Sunday, Nwinkua Dumdibabari_ ## NOAH (Nationwide Operational Assessment of Hazards) Website, revamped! _by Feye Andal_ about this event: https://2022.stateofthemap.org/sessions/NNKX8K/

Sparked by concerns about OpenStreetMap's role in how the public accesses and recreates on protected lands, OpenStreetMap US volunteers, navigation app developers, national agencies and public land managers formed the OpenStreetMap US Trails Working Group in 2021. Bringing together a diversity of perspectives on trail mapping practices, trail safety, and protecting the environment, this group is working to address on-the-ground challenges, tagging schemes, authoritative data, and other topics related to mapping trails in OSM. Learn how this group is collaboratively developing solutions for responsible trail mapping in OpenStreetMap. about this event: https://2022.stateofthemap.org/sessions/CUV9H7/

Null Island is where the prime meridian meets the equator at (0,0) longitude and latitude. While Null Island is a fictitious, dimensionless, point object, its existence stimulates vigorous debate making it worthy of serious consideration. Many examples exists illustrating how Null Island impacts OSM discourse. Our study considers what the geographic oddity of Null Island means for OSM. The main contribution is a structured knowledge-based resource facilitating understanding of Null Island’s impact on OSM. This socio-technical and philosophical investigation of Null Island can become a catalyst for deeper discussions and debates in OSM around mapping practices. Null Island refers to the location where the prime meridian meets the equator at 0o longitude and 0o latitude. With coordinates (0, 0), it is the origin of the WGS84 geographic coordinate system. It has been argued that Null Island can be considered a real place that is a product of our digital age [1]. Null Island’s significance comes from the fact that it is erroneously associated with large amounts of geographic data that spans across geo-social media, location-based services and map databases. Even though Null Island is a fictitious, dimensionless, point object, its existence stimulates debate that elevates Null Island into a global issue worthy of serious consideration (a detailed description of associated issues is given in [1]). Members of the OpenStreetMap (OSM) project often interact with this location in various ways, and therefore understanding what Null Island means for OSM is relevant. We can find several examples of Null Island affecting OSM, such as a recent debate that arose in the talk mailing list in January 2022 with the title “Was the deletion of Null Island reasonable?” [2], where contributors argued for or against the deletion of Null Island. In addition, a web search for the term “Null Island” on the openstreetmap.org domain [3] reveals that Null Island was mentioned across the entire OSM ecosystem, including mailing lists, forums, user diaries, notes, features, changesets, wiki pages, help articles, blogs and even the Ruby on Rails codebase of the OSM website uses Null Island for testing (https://tinyurl.com/OSM-Ruby-Null). These suggest that Null Island already has a widespread reach within the OSM project. The purpose of this study is to consider both qualitatively and quantitatively what the geographic oddity of Null Island means for OSM. No research works exist which tackle this issue in depth. Previous studies mentioning Null Island do so in a simplistic way and use the term to refer to the (0, 0) location (see e.g. [4]–[6]). Only a few studies recognize it as a special location and unique phenomenon ([7], [8]), and to our knowledge, only one study tackles the issue in depth [1]. In addition to contributing a robust academic study of Null Island, this work will produce a structured knowledge-based resource for the community to understand Null Island’s impact on OSM. Building on [1] we investigate the various ways Null Island is represented in the OSM project subsequently contributing an evidence-based narrative history on the evolution of Null Island. This includes the qualitative review of various OSM communications channels (e.g. mailing lists, discussion boards and wikis) for mentions and references to Null Island. We believe these channels help provide insights about how the OSM community contextualizes, describes and deals with Null Island. The history of special map features related to Null Island, such as node #1 (https://tinyurl.com/osm-first-node) and the node located at (0, 0) (https://tinyurl.com/OSM-Center) will also be reviewed to illustrate what actions the OSM community took in terms of adding and removing Null Island to the database. In addition to these qualitative approaches, we utilize the ohsome API [9] to extract and analyze map edits made on or near Null Island, which provides a quantitative way to assess the frequency of erroneous data added to OSM near (0, 0) as well as the semantics of such data. Interesting patterns have already emerged from the preliminary analysis of data. The most recent mailing list debate mentioned above [2] can be summarized as follows. 17 individuals contributed 45 e-mails to the discussion between January 3 and January 10, 2022. One of the (very few) rules of OSM is that data should be verifiable, meaning that others can visit the real location of a map object and see for themselves if the data is correct. This is also known as the “ground-truth rule” [10]. Null Island as a fictional place violates this rule, therefore a popular stand in the debate is that it should not be part of OSM. This was explicitly expressed by five individuals, including a member of the authoritative Data Working Group. A counter argument is that Null Island is fundamentally similar to localities and neighborhoods, that might not exist as political or physical entities, but are known only informall

What must be mapped to make routing for prams and wheelchairs practical? Three years ago, the local meet-up in Dortmund, Germany, started a campaign to make step-free routing available for the general public. The lessons learned mean that such routing is possible, but there is a lot missing to map - both in Dortmund and in all other parts of the world. Map the essential where fellow mappers are sparse. And codify the full ground truth where the passion allows it. I hope to encourage mappers for the quest to get their neighbourhood ready for wheelchairs, prams and all the other pedestrians! Routing for pedestrians is a much broader challenge than the well-known car routing. Cars all over the world are mostly uniform, but pedestrians vary widely in their capabilities. This means that a lot of details that a sportive person might not even notice can be literally a roadblocker for people with prams, for wheelchair users or simply lesser-abled people with not enough strength for a complete stairway. Becoming a father has been a good opportunity to check in practice what is and what is not feasible for a pedestrian with a pushed vehicle. It turns out that the first step is to get aware of the various kinds of obstacles that get in the way. Beside the obvious steps and kerbs, there are impassable surfaces, too narrow or too steep sections. Or simply sidewalks missing completely on the ground. As of now, OpenStreetMap data does not even suffice to figure out where one or both sidewalks actually exist. This puts into perspective the discussions about how to map best details of both detached ways and sidewalks. A couple of tagging approaches are compared to allow educated guesses which level of detail will allow for good results rather in weeks and months than in years or decades. I even dare to give suggestions what tagging practices we should additionally adopt to be able to map faster. The background of this talk is an initiative from the Dortmund meet-up: For the large event Kirchentag 2019, we mapped at least the city center sufficiently well for wheelchair mapping. The whole city with its 1500 km streets has turned out to be simply too much. Given that a city with a local meet-up is in a relatively good position to be mapped, it was no surprise that also elsewhere the data is simply not yet good enough for wheelchair routing. The hope is that simple suggestions what helps is getting more traction than a sophisticated mapping hierarchy. about this event: https://2022.stateofthemap.org/sessions/LKZYJ7/

Urban green spaces serve people for active and passive recreation. On the basis of OpenStreetMap data, suitable green spaces are to be derived in order to incorporate them as recreation destinations in a location-based service (the “meinGruen” app) as polygons. The modelling approach focuses on activity-related barriers in the context of urban green, transitions between different land use classes, and public accessibility. The case study was implemented for the city of Dresden in Germany. In addition to important ecosystem services such as clean air or local climate regulation, green spaces provide peace and recreation, contributing to a good quality of life for the population. In high-density urban areas, publicly accessible green spaces are used for a variety of recreational activities, which has become even more important, not least because of the COVID-19 pandemic [1]–[4]. In this context, the research project "Information and Navigation on Urban Green Spaces in Cities - meinGruen" examined publicly accessible green spaces with regard to a variety of criteria in order to assess their suitability for the pursuit of leisure activities, such as going for a walk or playing soccer [3], [5], [6]. The aim of this study is to derive a suitable polygon dataset to describe the spatial distribution of publicly accessible urban green spaces. The presented approach favors the use of OpenStreetMap data and intrinsic knowledge. Advantages of the use of OpenStreetMap data are the global availability, the often high completeness in urban areas as well as the unified open data license ODbL 1.0. In this way, problems with data availability and heterogeneity due to different responsible authorities can be avoided. Ludwig et al. [7] describe an approach to mapping public green spaces based on OpenStreetMap and Sentinel-2 satellite imagery in which barriers and land use changes are considered based on a priori (expert knowledge) assumptions for polygon generation. In the approach presented here, spatial delimitation is to be refined by describing barriers by probability values. The term "barrier" is first analyzed in an interdisciplinary way in order to then work out its meaning for the spatial delimitation of a green space. Here, barriers describe the action space of a recreational activity. While there are a number of object types (such as walls, fences, rivers, roads or railroad lines) can be assumed to be barriers with certainty, there are others (such as paths or the change of land use) for which knowledge is still lacking. The study area includes the city of Dresden in Germany, plus a buffer of five kilometers. OpenStreetMap represents the main data source. For training and validation, official cadastral data (ALKIS) as well as a dataset on cadastral parcels owned by the city of Dresden were used. The methodology consists of six steps: First, according to defined rules, types of barriers were extracted from OpenStreetMap data. Second, we derived a land use layer without overlaps and holes from OpenStreetMap. Here, two options were compared regarding different target schemes for land use classification. Third, a mapping in terms of a “ground-truth“ in selected areas in Dresden followed in order to be able to evaluate the existence of a barrier on site for the extracted paths and changes of land use. Fourth, generic probabilities for the existence of a barrier were determined based on path type or land use change type. Fifth, a polygon mesh was created by applying thresholds to the determined barrier probabilities. Sixth, the generated polygons were enriched with attributes on the number of green space-related POI, such as benches, trash cans, or trees. Models for "greenness" and "accessibility" are thereby trained. For the technical implementation mainly Docker, PostgreSQL/ PostGIS, Python (Geopandas, Scikit-Learn) and Jupyter Notebook were used. Data import was performed by osm2pgsql and ogr2ogr. For mapping we used the app QField. Land use layers were successfully generated for the study area using a residual class. The results indicated that the land use classification according to the area scheme of the IOER-Monitor (option B) has a higher thematic accuracy with a maximum of 33 classes (433 original OSM tags were assigned) than the option A based on a classification according to osmlanduse.org/ Schultz et al. (up to 13 classes, based on 61 OSM tags) [8], [9]. The classes of arable land (A: 28.40% / B: 28.06% share of area) as well as forest (A: 21.81% / B: 23.33%) are dominant in both variants. While the residual class takes up 6.29% of the area in option A, it is only 4.88% in option B. For the “ground-truth”, a total of approximately 82.3 km of paths (with 408 line objects) and approximately 64.2 km of land use changes (1720 line objects) were evaluated for the presence of a barrier in two selected areas in Dresden. The land use changes are based on variant B. Data were coll

Du brauchst eine kleine plattformunabhängige Anwendung? Du kannst Python lesen und schreiben? Dann probiere doch mal web.py, ein kleines Web-Framework mit sehr niedriger Einstiegshürde! Mit wenigen Codezeilen sind auch komplexere dynamische Web-Anwendungen realisierbar, die alles mitbringen, um sie autark unter jedem gängigen Betriebssystem betreiben zu können. Der Vortrag erklärt die ersten Schritte. Zur schnellen Erstellung einer kleinen dynamischen Webseite, muss man nicht unbedingt auf einen der schwergewichtigen Webframeworks zurückgreifen [1]. Denn um dort zum Ziel zu kommen, ist meist eine längere Einarbeitszeit, die man mit dem Lesen von Dokumentationen und dem Ausprobieren von Tutorials verbringt, vorprogrammiert. Die Gefahr dabei auf halben Weg aufzugeben, ist recht hoch. Auf der Suche nach schlanken Alternativen, stolperte der Autor dieses Vortrages über einen etwas älteren Artikel im Linux Magazin 08/2006 [2]. Es wurde web.py, damals noch aus einer einzigen Python-Datei bestehend und in der Version 0.138 vorliegend, vorgestellt. Vor allem die 10 Zeilen Python-Code für ein „Hello World“, welche dann nicht mal einen installierten Webserver-Dienst auf dem Entwicklungsrechner benötigten, waren sehr beeindruckend. Und auch wichtig: die Entwick­lung von web.py ist nicht im Jahr 2006 stehen geblieben, wie ein Blick auf die Projektseite [3] be­weist. Also die ideale Spielwiese für einen angehenden (oder auch fortgeschrittenen) Python-Pro­grammierer. Neben den üblichen Dingen eines Webframworks, wie z.B. der Generierung von validem HTML-Code, Session- und Cookie-Managment sowie HTML-Templates, bringt web.py noch einige andere interessante Dinge mit. An vorderster Stelle stehen dort sicherlich solche Features wie z.B.: • komfortables URL-Handling • Generierung von Input-Forms und einfache Validierung der Eingaben • integrierte Anbindung an SQLite-, MySQL- und Postgres-Datenbanken • Unterstützung beim Debuggen der Web-Anwendungen durch detaillierte Fehlerausgaben • in die Anwendung integrierter Webserver • die Erstellung der Web-Anwendung erfolgt hauptsächlich in nativen Python (mit allen Vor­teilen, wie z.B. das Einbinden der unzählig verfügbaren Python-Module) Der Vortrages gibt eine Einführung in die Programmierung von dynamischen Webseiten mit dem Python-Modul web.py. Dabei wird auch verraten, welches Anwendungsszenario eigentlich den Aus­schlag gegeben hatte, sich mit diesem Thema zu beschäftigen. about this event: https://programm.froscon.org/2022/events/2747.html

Schaffen Sie mit OpenCanary eine Netzwerkalarmanlage, mit dem Sie Hacker abfangen können, bevor diese Ihre Systeme vollständig kompromittieren. Unternehmen brauchen in der Regel 6 Monate, um herauszufinden, dass sie Opfer einer Cyberattacke waren. Je länger es dauert, desto kostspieliger wird der Vorfall. Wenn man sich mit den digitalen Angriffen und Reaktionsempfehlungen beschäftigt, ist man als Organisation mit einer kleinen IT Abteilung schnell überfordert. Immer wieder liest man dann - Logdateien analysieren - Netzwerkverkehr überwachen - Intrusiondetection Software installieren Alles ist richtig, aber die Komplexität und die Kosten für solche Maßnahmen sind kaum zu stemmen. Hier kann die Opensource Software Opencanary helfen. Einfach gesprochen schafft OpenCanary einen Netzwerk-Honeypot, mit dem Sie Hacker abfangen können, bevor sie Ihre Systeme vollständig kompromittieren. Technisch bedeutet das: OpenCanary ist ein Daemon der Alarm schlägt, wenn ein Dienst (miss)gebraucht wird. Man kann dann festlegen, dass man zum Beispiel einen FTP Server, einen Fileserver oder einen Datenbankserver simulieren möchte und wohin eine Alarmierung gesendet werden soll. In dieser Session betrachten wir, was passieren sollte, wenn sich ein Angreifer im Netzwerk umschaut und versucht weitere Services anzugreifen. about this event: https://programm.froscon.org/2022/events/2732.html

Various container runtimes exist on Linux to run software without installing packages on the host system. The nature of containers implies separation of the host system which sometimes is a gap that needs to be bridged again. For systemd services the "portable service" format allows to run a service with its own dependencies bundled in a filesystem image. However, like a container it still does not make any CLI tools directly available to the host system. Therefore, a common solution is to copy a set of static binaries to the system to use the same deployment mechanism for the service and the CLI tools. The new systemd-sysext format allows to extend the host system through an overlay that integrates the bundled software similar as traditional packages do. The binaries and config files can be updated and managed through a single sysext image file. A version matching logic allows to ensure that a particular host system version is used for depending on certain features or for dynamical linking. We demonstrate how systemd-sysext helps to extend an immutable host system such as Flatcar Container Linux, both for third party user software as well as an internal building block for more modularity. Linux package managers provide a mature way to install, update, and remove additional software on a system. If using packages is not possible or wanted, containers get used, e.g., through Docker. Containers come with upsides, such as reduced dependency requirements and increased isolation, but also downsides because they don't integrate with the system as well as packages would do. There are workarounds like creating systemd services that start the container and expose expected APIs to the system and using a wrapper script to make the container behave like the CLI tool does if installed through a package. Another approach is to use statically linked binaries for the service and the CLI tools or at least for CLI tools to complement a container. The systemd project introduced support for “portable services” which addresses the integration of a service with the system. It provides a way to set up systemd services from a container-like filesystem image that contains the systemd service definition and the required binaries and dependencies. The recent systemd-sysext format aims to address the extension of the system with additional CLI tools. It works by managing overlay mounts of the sysext images on top of the “/usr” and “/opt” system folders. This has the benefit of bundling a set of binaries inside a single image file that can be added, updated, and removed atomically. There is also a version matching logic that enables safe usage of dynamic linking and depending on certain features of the OS by ensuring that a particular host system version is used. While the primary use case is for deploying additional tools, it can also be used to provide systemd services and their binaries or temporarily overwrite host system files at runtime. Using systemd-sysext for extending the system with additional systemd services requires a small workaround but allows to bundle a service and its CLI tools into a single sysext image. In result, it integrates well with the system and behaves similar to software installed through a regular package. There are many use cases possible for sysext images, and we will demonstrate some of them for Flatcar Container Linux, which has no package manager but at the same time needs to be open for user customization, selection of container runtimes, and optional cloud vendor tools. about this event: https://programm.froscon.org/2022/events/2775.html

batou ist ein Python-basiertes Deployment-Werkzeug, das sich für einfache und komplexe Anwendungen eignet. Im Vortrag werden die wesentlichen Grundideen hinter batou erläutert indem wir ein Deployment für die Konfiguration von Cumulus-Switches bauen. Eine besondere Eigenschaft von batou stellt dabei das fraktale Modell dar: es erlaubt den leichten Wechsel zwischen deklarativer Modellierung und imperativer Implementation, sodass man sich auch in komplexen Situationen auf Eigenschaften wie Idempotenz, Konvergenz, Konsistenz und Vorhersagbarkeit verlassen kann. about this event: https://programm.froscon.org/2022/events/2817.html

Barrierefreiheit für alle, immer und überall, zumindest in OSS, darum soll es in dem Vortrag gehen. Wer profitiert von gut nutzbarer Software? Welche Prozesse führen zu mehr Barrierefreiheit? Was können wir tun, um accessibility by design zu erreichen? Durch die zunehmende Nutzung von OSS auf dem Desktop an Schulen und Hochschulen wie im öffentlichen Dienst in Norddeutschland, werden immer mehr Mängel bei der Zugänglichkeit sichtbar. Bisher werden sie meist durch individuelle Workarounds oder nachträgliches Flicken versucht zu beheben. Meist nur mit mäßigem Erfolg, der durch das nächste Update wieder zunichte gemacht wird. Der Vortrag möchte daher zu einem grundlegenden Umdenken auffordern, zu einem umfassenden Verständnis von Barrierefreiheit als Selbstverständlichkeit. about this event: https://programm.froscon.org/2022/events/2781.html

The fitness of OSM for multi-label classification is proven. A workflow to enhance OSM-based multi-labels using machine learning is established. The results are provided to the OSM community via the HOT Tasking Manager. # Introduction Land-use and land-cover (LULC) information in OSM is a challenging topic. On the one hand, this information provides the background for all other data rendered on the central map and is used by applications like https://osmlanduse.org. On the other hand, this information has a difficult position within the OSM ecosystem. LULC information can be quite cumbersome or even difficult to map e.g. due to natural ambiguity. The growing tagging scheme provides a collection of sometimes ambiguous or overlapping tag definitions that are not fully compatible with any official LULC legend definition [1]. Furthermore, the data is highly shaped by national preferences and imports. This diversity of the LULC data in OSM is a fundamental principle of OSM that enabled the success of the project. Yet, this can create considerable usage barriers or at least caveats for data users unfamiliar with the projects' ecosystem. The remote sensing community for instance has started to use OSM LULC information as labels in their classification models. Frequently, OSM LULC data has thereby been taken at face value without critical reflection. And, while the quality and fitness for purpose of OSM data has been proven in many cases (e.g. [2,3]) these analyses have also unveiled quality variations e.g. between rural and urban regions. The quality of OSM therefore can be assumed to be generally high, but remains unknown for a specific use-case. The proposed work first assesses the impact of these challenges on a use-case of multi-label remote sensing (RS) image classification and then provides a machine learning (ML) based workflow to overcome and finally mitigate them. Multi-labels are a type of image classification where a satellite image is labeled with multiple containing LULC classes. In the presented study these labels are extracted from OSM and used to train the ML algorithm. # Methods and Results The fitness for purpose of OSM for multi-label RS image classification was tested on a Sentinel 2 scene with a resolution of 10m and four bands in south west Germany recorded in June 2021. The area was chosen for its estimated high completeness and low amount of imported data. OSM data was grouped by its tags into the four LULC classes 'forests', 'agricultural areas', 'build-up area' and 'water bodies'. 18 tags that could unequivocally be mapped to these classes were used and small elements below the image resolution or the classes minimal mapping unit were filtered. The chosen scene was then tiled into a 1.22 x 1.22 km grid of 8100 image patches. Zero to four labels were assigned to each patch, based on the OSM LULC elements therein. Evaluation was performed manually on 910 random patches, of which 80% had a correct OSM-based multi-label, thereby proving the assumed high completeness and quality in the region. The proposed workflow provides a method to enhance this OSM-based RS image multi-label classification and extend it to areas of lower OSM quality and completeness using ML (specifically deep learning (DL)). The main obstacle for ML and especially DL is the required amount of labeled training data. Volunteered geographic information (VGI) like OSM offers a potential solution to this challenge by providing an overabundance of LULC information that is suitable for this purpose if data quality is sufficiently high. The workflow uses the multi-label information extracted from OSM for training and then detects discrepancies between its predictions and OSM. Using this information and pinpointing the exact location of error within the patches provides valuable OSM data quality information. Apart from facilitating a fast quality estimation for large areas, the workflow can make its findings automatically available to the OSM community in a feedback loop using the HOT Tasking Manager framework. Thereby the valuable service by the OSM community of providing large amounts of free and generally high quality training data is recognised in the form of quality feedback including mapping hints to the OSM community. The five workflow stages are: 1) RS data collection and preprocessing, 2) OSM data collection and preprocessing, 3) LULC multi-label modeling, 4) OSM data issue flagging and 5) the community feedback loop. While each step is an atomic use case and application, the combination of all four steps creates a tool that is useful for the RS and the OSM community likewise. The tool is openly available at https://gitlab.gistools.geog.uni-heidelberg.de/giscience/ideal-vgi/osm-multitag under the GNU Affero General Public License v3 including example datasets. Manual input was kept as low as possible while enabling the 'human in the loop' to take full control over all input and

Using intrinsic quality indicators we explore how network quality, in terms of its suitability for navigation, varies across areas with relatively high and low corporate editing in OpenSteetMap. Our work shows areas with relatively high rates of corporate editing exhibit not only an overall increase in data quality, but also increased rates at which quality improves. OSM (OSM) contributors have traditionally lacked explicit monetary incentives for contribution [1]. Since 2016, a handful of large corporations (including Apple, Facebook, Microsoft, and Uber) have increasingly contributed data to OSM. Corporate editors (CEs) represent a distinct community as their editors are compensated and thus their contributions cannot be labeled as ‘volunteered’. Additionally, corporations employ large editing teams and new state-of-the-art editing techniques aided by artificial intelligence, making them capable of editing large swaths of information in relatively short time [2]. Corporate teams are often led by long-time OSM community members themselves, emphasizing the multifaceted nature of a rapidly growing open mapping platform [3]. While there has been some contention about the quality of edits done by CEs, corporations argue their contributions improve existing data [7]. Our study provides a preliminary quantitative evaluation of data quality impacts of corporate edits on OSM. We assess intrinsic data quality across five regions that have high levels of corporate contributions: Dallas-Ft. Worth, Egypt, Jamaica, Thailand, and Singapore. The quality of these regions is compared to that of Denmark, a region which has witnessed relatively less corporate interest, yet possesses a well-mapped OSM presence due to a well developed local mapping community [4]. These evaluations were performed using measures of intrinsic map quality. While the most straightforward evaluation methods involve comparing against extrinsic sources, such as either ground reference information or authoritative data sources; lack of data availability, licensing terms, and costs often render this comparison untenable [5,6,7]. A transferrable, data driven way of assessing quality remains using Intrinsic Quality Indicators (IQIs), a sub-field of OSM analysis which provides a variety of approaches for evaluating intrinsic OSM data quality. We chose to focus on IQIs that apply to networks, and to evaluate IQIs for land-based transportation networks within OSM. We analyzed networks for our specified locations for every other year between 2014 and 2022. OSM editing archives were processed using R to extract maps of the relative activity of corporate editors [8]. Our list of corporate editors was sourced from OSM’s publicly available list of corporate editors accounts. We extracted entire networks that represented the first day of each year of interest (2014, 2016, 2018, 2020, 2022) from OSM’s historical archives. For the purposes of this study, we extracted all networks where “OSM WAY = Highway”. We evaluated several IQIs for our areas of interest. We focused on completeness of network, both in terms growth over time and in terms of its navigability. We operationalized “completeness for navigability” as an intrinsic measurement by exploring the percentages of networks that possessed attributes necessary for GPS navigation – street names and speed limits. Navigability was assessed and compared across time points using Origin-Destination matrices. By creating a regular matrix across the area and calculating the ratio between a direct route between points, and a route navigated within our network, we calculated a ratio that can be compared across time to evaluate the changing efficiency of the navigable network. Additionally, when building routing networks, we discovered an additional IQI : the presence and qualities of topological islands within our network. That is, areas which are disconnected from the main network due to mapping errors or incompleteness. After mapping these metrics, we analyzed how they correlate with each other and how they change over time. Overall, IQI trends for the road network reveal consistent patterns across all measures and locations. There is a trend towards increasing data quality in terms of gradual increase of network length, completeness in terms of attributes (name, speed limit, and pedestrian access), the increasing efficiency of ODM routing ratios, and the increasing amount of places that have “navigable” attributes. Importantly, we found differences between our control location (Denmark) and our other areas of interest. The primary difference of note is not with regards to the quality of the data, but with respect to the rate at which data quality improves: Denmark’s rate of quality improvement is slower than other locations. The faster rate of quality improvement in the test areas highlights that the data creation and editing activity by corporate editors and other organized editors in these locations are helping narrow

This pilot project is connected to a larger initiative to open-source the assisted mapping platform for Humanitarian OpenStreetMap (HOTOSM) based on Very High Resolution (VHR) drone imagery. The study test and evaluate multiple U-Net based architectures on building segmentation of Refugee Camps in East Africa. Introduction Refugee camps and informal settlements provide accomodation to some of the most vulnerable population, the majority of which are located in Sub- Saharan East Africa (UNHCR, 2016). Many of these settlements often lack up-to-date maps of which we take for granted in developed settlements. Hav- ing up-to-date maps are important for assisting administration tasks such as population estimates and infrastructure development in data impoverished environments, and thereby encourages economic productivity (Herfort et al., 2021). The data inequality between the developed and developing countries are often resulted from a lack of commercial interest, especially with the recent trend of corporate OSM mappers (Anderson et al., 2019, Veselovsky et al., 2021). Such disparity can be reduced using assisted mapping tech- nology. To extract geospatial and imagery characteristics of dense urban enviornments, a combination of VHR satellite imagery and Machine Learn- ing (ML) are commonly used (Taubenböck et al., 2018). Classical ML based methods that exploit the textual (e.g. GLCM), spectral, and morphological characteristics of VHR imagery are based on the principles of Computer Vision (CV). Although many have shown promising results in satellite VHR (1m to 5m resolution) scenarios such as differentiating slum and non-slum (Kuffer et al., 2016 & Wurm et al., 2021), in VHR drone imagery (5cm to 10cm resolution) however, results might suffer from noise caused by environ- ment and drone-based specific problems such as motion artefacts and litter. Recent advances in CV based Deep Learning might be able to address these issues (Chen et al., 2021 & Carrivick et al., 2016). Purpose of the study The study is connected to a larger initiative to open-source the assisted mapping platform in the current Humanitarian OpenStreetMap (HOTOSM) ecosystem. This study is a pilot-project to investigate the capabilities of applying semantic segmentation using community open-sourced VHR drone imagery collected by the partner organisation OpenAerialMap. The study aims to rigourosly assess the various components and inputs that would contribute to the ML based mapping system, and to produce a detailed evaluation on class-based accuracy assessment (Congalton & Green, 2019). This pilot study focuses on 2 camps in East Africa, where data availability and the geography of the camps are within a similar savannah ecosystem. This enables highly-detailed method testing and analysis of transferability of the results between the two camps. Data and Methodology The first camp is located in Dzaleka, Dowa, Malawi, which is sub-divided into the Dzaleka North and Dzaleka main camp. The Dzaleka camps are home to around 40,000 refugees mainly coming from the African Great Lakes region. The Dzaleka North camp is characterised by a newer, spatially well- planned metal-sheeted roofs, while the southern main camp is characterised by complex, dense mud-walled building with stone-lined thatched-roofs (UN- HCR, 2014). The second camp, the Kalobeyei settlement is part of the sub- camp of Kakuma, located in the rural county of Turkana, North-West Kenya. The Kalobeyei settlement was home to approximately 34,849 refugees as of 2019. This camp is significantly more spacious and is characterised by spa- tiall well-planned metal-sheeted roofs (UNHCR & DANIDA, 2019). VHR drone imageries were provided for both camps and vector labels produced by HOTOSM volunteers were provided for the Dzaleka and Dzaleka North camp. Since CV based Deep Learning is very dependent on the quality of the labelled referenced data, especially when performing pixel-based semantic segmentaion, it is of crucial importance that care is taken when producing highly accurate labels that ensure sucessful training (Ng A., 2018). A large quanitiy of available labels did not have such a task in mind, imperfection in labelling around existing drone artefacts could cause the trained model to misclassify such pixels. In order to train a model which performs well on drone imagery, the motion artefact will be a signficant feature for the model to learn.he combination of data availability have allow a unique set of research questions concerning the input data quality and experiment setup to surface. Therefore, to test out U-Net and a few variation of the U-Net performance, an additional set of label data was created in order to supple- ment the imperfection in the labelled data of the Dzaleka camps. Initially, the models will be trained on the pixel-perfect and less complex Kalobeyei dataset, this will be then be followed by introducing the Dzaleka datasets of higher complexity. A compari

Albeit the manifold usage of OSM building footprints an adequate investigation into their completeness on the global scale has not been conducted so far. This talk investigates OSM building completeness within all 13,135 urban centers covering about 50% of the global population. The collaborative maps of OpenStreetMap (OSM) have become a major source of geospatial baseline data for humanitarian organisations, companies and public authorities. Describing the elements of spatial data quality (e.g. positional accuracy, completeness, temporal quality) for the OSM dataset is a key prerequisite to provide the potential stakeholders with the necessary information to decide on the fitness for use of a data set for their particular application [1]. Without information on spatial data quality there are serious barriers to the adoption and usage of new sources such as OSM. A large community of researchers has analyzed the quality of OSM data in comparison to authoritative reference data sets, by means of remote sensing and using intrinsic measures [2–4]. It has been acknowledged that the OSM data in general is strongly biased, in part due to a much larger contributor basis in countries in the global North as a consequence of socio-economic inequalities and the digital divide [5, 6]. Albeit the manifold usage of OSM building footprints an adequate investigation into their completeness on the global scale has not been conducted so far. This talk investigates OSM building completeness in regions home to a population of 3.5 billion people (about 50% of the global population). First, we propose a machine learning regression method based on generalized additive models (GAMs) to assess OSM building completeness within all 13,135 urban centers (as defined by the European Commission [7]). The analysis utilizes an extensive collection of open building data from commercial and authoritative sources as training data and builds upon very recent technological advances to utilize OSM full-history data for spatio-temporal data analysis on the global scale [8]. This allow us – for the first time – to present a comprehensive assessment of the evolution of urban OSM building completeness which encompasses all data contributed to OSM since 2008. For each urban center we calculated the OSM building completeness using the area ratio method which has been applied55 by several other researchers in the context of urban areas [9–11] . Several measures have been adopted to describe the temporal evolution of inequality in urban OSM building mapping on the global scale and per World Bank region. First, we analyzed the share of population living in urban centers with low completeness (<20%) and high completeness (>80%). Gini coefficient has been utilized to derive the degree of evenness of urban OSM building completeness following an approach proposed by Massey & Denton (1988) to study residential segregation 12 . Moran’s I has been selected as a measure of global spatial autocorrelation of urban OSM building completeness. Spatial autocorrelation has been proposed as an explicitly spatial indicator of segregation covering the dimension of clustering [12, 13]. These analyses has been conducted for annual snapshots from 2008-01-01 up until 2022-01-01. Overall, urban OSM building completeness is estimated at 38% globally. Our results emphasize that although the well-examined Global North - Global South bias in OSM still exists, over the past years mapping has spread substantially across the globe and within regions. The analysis of the spatial clustering of high completeness values and low completeness values disclosed that global spatial inequality in OSM building completeness has sharply increased between 2008 and 2014. This shows that although overall OSM building completeness became more even in the same period, mapping activity in that time favoured cities which were located close to other cities which were mapped already. One might interprete this as a reinforcing effect. Ongoing mapping in one area triggered even more mapping in surrounded areas. At the same time this also indicates that up until 2014 the expansion of OSM mapping to distant and un-mapped regions (likely to be located in the Global South) didn’t happen at a significant scale. Nevertheless, since 2014 Moran’s I global spatial autocorrelation declined and was measured at 0.55 as of 2022. Combined with the decrease of the Gini coefficient in the same time, this suggests that OSM building completeness has become more even because mapping activity has been expanded to regions which were previously mapped much less. In that regard, OSM building data as of today was much less segregated in terms of both dimensions (evenness and clustering) compared to the state-of-the-map in 2014. This process was to a limited extend positively influenced but humanitarian mapping activity organized through the HOT Tasking manager, but hardly influenced by corporate mapping activity. We devel

Human factor is one of the most crucial elements in crowdsourced mapping. This research explores how human bias affects the mapping process and whether such effects can be mitigated through targeted training with a behavioral experiment. The experiment uses a two-group randomized design. The treatment group receives more advanced training than the control group. There are two goals for the experiment. First, we aim to identify the common types of bias that amateurs from a specific demographic community have when using OSM. Second, we plan to explore whether training is helpful for reducing those biases and improve the quality of mapping. OpenStreetMap (OSM) is one of the VGI platforms that has been curated primarily by volunteers, which indicates that the demographic differences of the backgrounds of volunteers might affect their understanding of mapping and their mapping behavior. For example, contributors with varying skills and experiences of mapping and GIS software might choose different objects to map and trace them in different levels of detail. There is a wide range of factors that could have an impact on how and what individual contributors choose to map: age, gender, expertise, education, income, etc. This study focuses on digging into the mapping behavior of a specific demographic community - residents from Singapore. We have observed changes in terms of tagging and editing behavior in OSM before and after different levels of training. This study has important implications for OSM mapping, especially platforms such as HOTOSM which largely rely on faraway amateur curators to provide up-to-date geographical information of a specific area in case of events such as wars, natural disasters, crimes or humanitarian emergencies. about this event: https://2022.stateofthemap.org/sessions/RHF3UX/

The quality of OSM data is dependent on many different factors and is quite heterogeneous. Therefore, in both intrinsic and extrinsic quality parameter analyses, a common practice is subdividing the study areas into subareas. In this paper, we worked on a method for obtaining the optimal grid cell size for OSM data quality analysis. Furthermore, we proposed that if the quality is homogeneous in a region, it can be estimated using an IDW interpolation. . In this summary, we have done a preliminary analysis for a Brazilian city, Curitiba, with about 28,000 points of known accuracy. Knowing the quality of a given geospatial data allows measuring how much its use can be viable in specific applications and assist in decision making. ISO 19157 [1] established that the geospatial data quality indicators are positional accuracy, temporal accuracy, thematic accuracy, logical consistency, and completeness. These measures are represented by values that summarize the condition of a product as a whole. These values tend to be homogeneous throughout the evaluated area in traditional mapping. In contrast, in VGI, data quality can be affected by several conditions related to editing history, contribution period, and contributor profiles [8,9]. Given the mentioned aspects, data quality in VGI platforms tends to be heterogeneous, i.e., the results may show significant discrepancies according to the area assessed or even within the same region. Given the heterogeneity issues described, several researchers around the world have performed the quality assessment of these types of information based on the principle of subdividing the study area into cells [2,3,4,5,6,7]. Such a procedure has been used in extrinsic quality assessment processes based on ISO 19157 indicators or intrinsic parameters associated with the characteristics of the contributions and contributors. Given the results obtained, the representation of the quality of the data from sub-areas makes it possible to obtain analyses regarding the existence of patterns and establish relationships with other agents and their predominance. The discretization of space into rectangular or hexagonal grids is central to this type of analysis. The subdivision can occur regularly or irregularly. The units with irregular dimension cells allow us to perform analyses accepting other features or spatial phenomena that define these dimensions (e.g., neighbourhood border, a river or a railway track, areas with different population densities, and the dichotomy between rural and urban areas). However, these methods make operations difficult because they demand that the area value weigh the values; and the spatial analysis considering the neighbourhood is more complex. Units with regular-sized cells solve these two limitations. However, the problem of the grid of cells not conforming to spatial phenomena or features reappears. In order to conform to them, it is necessary to determine the optimal size of the cells. However, one issue remains little discussed: how to determine the size of such cells. Using too large a cell would treat unequal areas equally. On the other hand, using too small a cell and the increased computational cost of the process, ultimately, the ability to generalize the results is lost. Therefore, in this work, we seek to develop an interactive approach for determining the grid cell size calculation, initially using points of known positional accuracy. The hypothesis here is that when the analyzed subarea is of optimal size, one can interpolate the error within the cell via an IDW and generate minimal residuals at the control points. Furthermore, by consecutively subdividing the grid, the mean squared error versus cell size curve will approach stability, thus revealing the optimal size for a given region. Inverse distance weighted interpolation (IDW) calculates cell values using sample point sets. This method considers that the higher weights in the interpolation should be due to the proximity of the unknown value point. Thus, if we had a homogeneous behaviour of the quality parameter in an individual area, by interpolation, we could estimate the quality of the points where this value was unknown. The methodological procedures developed using python in the QGIS environment are: 1. For the study area, points of known positional accuracy are chosen (in our case, intersections of the road system), from which a random subset of 10% is separated as a control set. 2. Definition of a first grid. 3. The points are used for interpolation within each cell by the IDW method. The Root-mean-square deviation (RMSE) is calculated using the control points for each cell and the average of the RMSEs for the entire area; 4. Definition of a second grid with half the resolution of the first grid; 5. Repeat the process described in item 3 for the second grid; 6. Calculate the differences between the average error values of the second grid and the first grid and check their significance ; 7.

OpenStreetMap has many details about streets, but applications rendering or simulating lane-level detail face many challenges: determining lane properties along one street, calculating geometry of streets and junctions, handling motorway entrances, dual carriageways, dog-leg intersections, placement tags, and parallel sidewalks and cycleways. osm2streets is a new effort to produce a cleaned-up street network graph with geometry. It's a Rust library, designed to be integrated with browser apps like iD or native/Java apps like JOSM. The goal is to consolidate community efforts to solve these data transformation problems, and to produce high-detail vector maps and apps for improving lane tagging with immediate visual feedback. Start using this at https://osm2streets.org about this event: https://2022.stateofthemap.org/sessions/9NHQQM/