Chaos Computer Club - archive feed

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

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

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

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

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.

Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times and high memory footprint. For both you have to pay to the cloud providers of your choice. That's why most developers tried to avoid using Java for such use cases. But the times change: Community and cloud providers improve things steadily for Java developers. In this talk we look at the features and possibilities AWS cloud provider offers for the Java developers and look the most popular Java frameworks, like Micronaut, Quarkus and Spring (Boot) and look how (AOT compiler and GraalVM native images play a huge role) they address Serverless challenges and enable Java for broad usage in the Serverless world. Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless Community. Java is known for its high cold start times and high memory footprint. For both you have to pay to the cloud providers of your choice. That's why most developers tried to avoid using Java for such use cases. But the times change: Community and cloud providers improve things steadily for Java developers. In this talk we look at the features and possibilities AWS cloud provider offers for the Java developers and look the most popular Java frameworks, like Micronaut, Quarkus and Spring (Boot) and look how (AOT compiler and GraalVM native images play a huge role) they address Serverless challenges and enable Java for broad usage in the Serverless world. about this event: https://programm.froscon.org/2022/events/2729.html

MySQL 8.0 wird etwa alle drei Monate akualisiert. Dabei kommen immer wieder kleiner (und größere) Funktionen dazu die teils erheblich Admins das Leben vereinfachen (MySQL Dump vs. MySQL Shell Dump) In diesem Vortrag schauen wir uns die Änderungen der letzten zwei (Corona) Jahre in der Tiefe an... MySQL 8.0 wurde bereits in 2018 als finale Version zur Verfügung gestellt und geht bald ins 5te "Lebensjahr". Allerdings wurde mit praktisch jedem Update/Patch auch neue Funktionen und Erweiterungen mit ins Produkt eingebracht. In diesem Vortrag wollen wir uns die Änderungen während Corona mal etwas genauer anzuschauen: - MySQL InnoDB ReplicaSet - Binlog Compression - Consistant reads for InnoDB Cluster - MySQL Shell, Cloning, Shell Dump, Table Utils - Invisible Columns - InnoDB ClusterSets für DR Anwendungsfälle - MySQL Operator für Kubernetes - MySQL Cloud und MySQL HeatWave für real-time Analysen - Instant add/rename/drop column - uvm. about this event: https://programm.froscon.org/2022/events/2764.html

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/

In this work, we present analyses using a series of comparative data insights that help to better understand the potential and implications of integration between knowledge graphs and OSM. OpenStreetMap(OSM) is one of the richest and most diverse sources of geographic information. However, it lacks a fundamental property vital for spatio-semantic analyses: hierarchical structure and semantic linkage. OSM provides links to existing knowledge graphs (structured data that conforms to a specific ontology) e.g., via the wikidata=* tags. The usage of these link-tags is currently limited to a small percentage of both OSM and Wikidata objects. Efforts were undertaken to enhance the geographic linking, linking nearby objects of the same type and semantic linking [1-3]. On the side of the hierarchical and semantic structuring of OSM, the WorldKG knowledge graph[4] provides a semantic mapping of a large subset of OSM. While the free and open OSM tagging scheme is a fundamental part of the OSM project that enabled its success, WorldKG overcomes the inherent lack of structure this tagging scheme represents, paving the way for a knowledge-graph integration of the OSM dataset. Still, open knowledge graphs and OSM are not fully integrated. The following analyses provide a series of comparative data insights that help to better understand the potential and implications of integration between knowledge graphs and OSM. In this work, OSM is compared to Wikidata, one of the largest open knowledge graph projects from the Wikimedia Foundation that provides structured storage to other Wikimedia projects such as Wikipedia. Wikidata can, in many aspects, be compared to OSM by its community structure, its free and open nature, and simple contribution framework. In this work, the two datasets are first compared in size, data structure, and distribution. Later, we extend our analyses with a community comparison. The presented analyses also examine how two separate online communities with similar interests have evolved. Grasping the size of the two projects is a straightforward task and visible on their websites: OSM features around 1 billion elements [5], while Wikidata is much smaller with over 97 million objects, of which approximately 9 million have geographic coordinates. The topic of railway stations was chosen because these objects have a comparable definition and are well represented in both datasets with ca. 130k and 100k elements in OSM and Wikidata, respectively, indicating integration potential. In OSM, railway stations are mapped by the tags 'railway=station' or 'railway=halt'. In Wikidata, the 'instance of (P31)' property containing 'Q55488' value represents Railway Station (object type). By defining generalizable comparison indicators, the presented work provides a framework and source code (available at https://gitlab.gistools.geog.uni-heidelberg.de/giscience/ideal-vgi/osm-wikidata-comparison under the GNU Affero General Public License v3) for VGI project description, comparison, and monitoring. Similar approaches have been established for OSM contributors [6], for single OSM elements [7], and for small geographic regions [8]. For data collection in Wikidata, Wikidata API (https://www.wikidata.org/w/api.php) and Wikidata SPARQL endpoint were used. For Wikidata objects mapped with 'Railway Station', their revision history containing user information, timestamps, and a number of properties was collected. Overall contributions were collected from all users who have contributed to at least one object typed 'Railway Station'. OSM data collection was done using the ohsome API (https://ohsome.org) to extract all railway stations mapped in OSM, including their history and all edits made by the users who edited these railway stations. In addition to a general comparison between the datasets, we derived five sets for a more detailed comparison: OSM with links to Wikidata (59,441 elements), OSM without links to Wikidata (74,659), Wikidata that have links from OSM and are typed as railway stations (45,050), Wikidata without links to OSM but with geocoordinates (54,594) and Wikidata without links to OSM and without geocoordinates (6,714). Our first analysis regarding the growth rate of the two sources showed that OSM has reached a saturated state regarding the number of railway stations, where only a few stations were added since mid-2020. Wikidata, on the other hand, still experiences a stable number of new stations that are added to the project. The two datasets depict no clear temporal correlation hinting towards two independent communities, meaning that edits in OSM are not followed by edits in Wikidata and vice versa. Despite the similar size of the two datasets at a global scale, the two datasets show significant discrepancies on a country level. For example, in China, Wikidata features only 39% of the stations present in OSM while having more than double the amount of stations in

Klassische Fileserver können in vielen Fällen durch Web-gestützte Dateimanagement-Tools ergänzt oder auch gänzlich ersetzt werden. EGroupware hat einen Dateimanager integriert und bietet viele Möglichkeiten zur Dateiverwaltung inklusive Filesharing und Online Office-Integration. Dieses Vortrag greift diese einzelne Anwendung aus EGroupware heraus und zeigt die Funktionen und Einsatzmöglichkeiten im Detail. Der in EGroupware integrierte Dateimanager kann als (alleiniger) Fileserver eingesetzt werden und bietet viele Möglichkeiten zur Dateiverwaltung inklusive Filesharing und Online Office-Integration. Dieses Vortrag greift diese einzelne Anwendung aus EGroupware heraus und zeigt die Funktionen und Einsatzmöglichkeiten im Detail: * Dateiverwaltung im Dateimanager * Sharing (intern/extern) * Collabora Online-Integration * Zugriff per WebDAV * Benutzerdefinierte Felder, Berechtigungen, … * Umwandeln => PDF, jpg * Mounten von externen Verzeichnissen * Mobiler Zugriff * Automatische Ordner für EGroupware-Objekte * Was ist ein VFS? * Sicherung der Daten * Standard-“System“-Verzeichnisse * [EPL-Funktion] Versionierung, Abonnierung * [EPL-Funktion] Zusammenspiel mit Kanban * [EPL-Funktion] Anzeige Ordnernamen Vorkenntnisse sind nicht erforderlich. Hilfreich sind aber erste Kenntnisse zu EGroupware. Dazu stehen zwei **Videos von den Tux-Tagen 2020** zur Verfügung: EGroupware (User-Sicht) und EGroupware extended (Admin-Sicht) https://help.egroupware.org/t/de-tux-tage-2020-vortrage-zu-egroupware-videos-und-vortragsfolien-verfugbar/75555 Weitere Videos/Folien zu EGroupware und drum herum: https://help.egroupware.org/t/de-ubersicht-vortrage/76255 about this event: https://programm.froscon.org/2022/events/2789.html

Der Vortrag startet mit einem Einstieg zur Rechenzentrumsautomatisierung mit Foreman und Katello. Danach wird das "Foreman SCC Manager" Plugin vorgestellt, welches die Bereitstellung von Softwarepaketen und Errata für verwaltete Systeme, auf welchen SUSE Linux Enterprise Server läuft, vereinfacht. "SCC" steht hier für SUSE Customer Center. Im Zuge unseres Open Source-Engagements wird dieses Plugin von der ATIX AG laufend gewartet und weiterentwickelt. Im Moment findet eine umfassende Umgestaltung der Produktseite statt, welche wir Ihnen vorstellen werden. = Foreman SCC Manager: Rechenzentrumsautomatisierung von SLES Der Vortrag startet mit einem Einstieg zur Rechenzentrumsautomatisierung mit Foreman und Katello. Danach wird das "Foreman SCC Manager" Plugin vorgestellt, welches die Bereitstellung von Softwarepaketen und Errata für verwaltete Systeme, auf welchen SUSE Linux Enterprise Server läuft, vereinfacht. "SCC" steht hier für SUSE Customer Center. Im Zuge unseres Open Source-Engagements wird dieses Plugin von der ATIX AG laufend gewartet und weiterentwickelt. Im Moment findet eine umfassende Umgestaltung der Produktseite statt, welche wir Ihnen vorstellen werden. Für den Vortrag benötigen Sie kein spezielles Vorwissen, von Vorteil sind aber Erfahrungen im Bereich der Systemadministration und der Automatisierung von IT-Umgebungen. Im Vortrag wird das Foreman Web UI gezeigt und wie mit Hilfe des Foreman SCC Managers SUSE Produkte anlegt werden können. == Foreman und Katello Foreman ist ein Lifecycle-Management Tool für physische und virtuelle Systeme. Sie können damit wiederkehrende Aufgaben automatisieren, Systeme ausrollen und konfigurieren, und versionierte Inhalte wie Softwarepakete und Errata auf angebundene Systeme verteilen. Katello ist als Foreman Plugin für Inhalte wie Softwarepakete, Puppet Module, oder Errata verantwortlich. Diese können manuell hochgeladen werden oder -etwas bequemer- aus online verfügbaren Quellen periodisch synchronisiert werden. == Foreman SCC Manager Die ATIX AG hat das Foreman SCC Manager Plugin entwickelt, um Foreman Instanzen mit SCC Accounts zu verbinden. Damit wird die Verwaltung von SUSE Inhalten signifikant vereinfacht. Dies ist unabkömmlich für alle, die Foreman und Katello zum Ausrollen und Verteilen von Inhalten an Systemen mit SLES nutzen. SCREENSHOT "foreman_scc_manager_list_of_suse_products_in_katello.png" Das Plugin fügt der Foreman Web UI eine neue Seite für SUSE Subskriptionen hinzu, wo ein SCC Account hinterlegt werden kann. Sie können damit SUSE Produkte auswählen und diese mit einem Mausklick nach Katello übertragen. Ohne den SCC Manager müsste jedes Produkt inklusive den Repositories einzeln und manuell in Katello angelegt werden. Stattdessen gibt es mit dem SCC Manager eine graphische Auswahl von den in Ihrem SCC Account verfügbaren Produkten. Im Anschluss können die importierten Repositories entweder -bei Bedarf- händisch oder -automatisiert- regelmäßig synchronisiert werden. == Redesign als offener Prozess Wir als ATIX AG (https://atix.de/) sind Teil der Open Source Community um Foreman. Für uns ist Open Source viel mehr als das bloße Hochladen von Code auf Github. Stattdessen treten wir in den Dialog mit der Community, mit Nutzerinnen und Nutzern, und Testenden, um das Foreman SCC Manager Plugin optimal weiterzuentwickeln. Wir stellen das Redesign des Plugins vor und zeigen Einblicke in die überarbeitete Art der Produktauswahl. Wir freuen uns auf Ihr Feedback. about this event: https://programm.froscon.org/2022/events/2771.html

Koennen uns Werkzeuge aus dem Ethical Hacking oder Bug Bounty Hunter Bereich bei unserer taeglichen Arbeit mit Webapplikationen unterstuetzen? Koennen uns Werkzeuge aus dem Ethical Hacking oder Bug Bounty Hunter Bereich bei unserer taeglichen Arbeit mit Webapplikationen unterstuetzen? Im Umgang mit gekauften Closed Source Web Applikationen die auf Basis von WebSphere Application Server und Kubernetes laufen, geht es in vielen Support Fällen um die Reproduzierbarkeit und Javascript Analysen im Browser. Im Gegensatz zu 2010 tauchen viele Fehler nicht mehr im Serverlog auf, sondern muessen auf den Clients im Browser untersucht werden. Welche Tools verwende ich taeglich um automatisiert zu pruefen ob Updates keine Fehler verursachen (Load Testing), Browser sessions aufzuzeichnen oder zu untersuchen (Intercept Proxies), Kommandozeilentools um Browserantworten weiterzuverarbeiten (JSON). Die Automatisierung von Installationen und Updates hilft gerade bei der Reproduzierbarkeit enorm (Ansible, Terraform). Gerade bei gekauften Produkten bekommt man oft nur verzoegert Updates und muss sich um Containerupdates auch mal selbst kuemmern. Wie sieht man ohne Dockerfile welche Aenderungen in einem Container bei der Erstellung gemacht wurden? Trivy und Dive helfen beim Finden von Vulnerabilities und Dive bei der Analyse der Container. about this event: https://programm.froscon.org/2022/events/2782.html

Sidewalks are a relevant part of the living space in urban environments, but there are still few mapped sidewalks. In recent years, the mapping of sidewalks has grown in importance among the OSM and academic communities. To cover up this gap, we propose a Github-hosted, fully open-source QGIS Plugin entitled "OSM SidewalKreator" to automatically draw for OSM the geometries of sidewalks crossings and kerb crossing interfaces. Furthermore, the tool gives the user the capacity to control the process. Then, deepen, improve, and increase the amount of sidewalk mapping in OpenStreetMap to improve accessibility and mobility worldwide. Sidewalks are a relevant part of the living space in urban environments. The existence of sidewalks and their condition is fundamental to locomotion in general and is critically important in mobility groups such as cyclists, wheelchair users, blind people, the elderly, and children. Also, the displacement along sidewalks can ensure safety from traffic, contributing to the well-being of citizens [1]. There is still an open debate about the best way to represent sidewalks in the OpenStreetMap community. Some users claim that they should be represented only as tags of the streets, using compound tags such as "sidewalk:left/right:surface=*", arguing that over-representation can pollute the map and create unnecessary complexity [2]. Biagi [3] and many OSM users nowadays [4] have been showing the representation of sidewalks as separated geometries as allegedly their best representation in OSM. There are many listed advantages [4]: crossings may be represented as lines, with the kerb interfaces as points (8 in a regular 4-way intersection); the actual traversing length will be represented (as it will include block corners and crossings); independence from the digitizing direction, as "left" and "right" may swap if someone inverts the way direction; ease of representation for pedestrian islands. Moreover, some cases cannot be represented correctly using the tag scheme or will need some cumbersome solutions, as it shall represent the portion of the sidewalk that is orthogonally projected from the street. Therefore, if a property is different on both sides, one may need to split the highway into four segments to represent it correctly. There are also other issues, e.g. geometric properties such as the distance from the sidewalk to the street will stay unclear. Regardless of the form chosen for representation, there are still few mapped sidewalks. For example, according to Taginfo [5], as of April of 2022, there are approximately 201 million ways with the "highway" key, but only 16.8 million (7.61%) are tagged as "highway=footway", considering the key "footway", there are only 4.8 million (2.45% of 201 million) ways tagged as such (58% sidewalk, 41% crossing), considering the tag "sidewalk=*", there are only 2.6 million (1.3% of 201 million) ways tagged. So, considering that most features are located in urban environments [6;7], where the major part of streets may have a sidewalk, the sidewalks are underrepresented in both schemes. Historically, it has been an issue, as[8] showed that in Berlin, only 5.6% of the Highways have a "sidewalk" tag, growing to only 8.2% in 2017 [9]. Recently, the mapping of sidewalks has grown in importance among the OSM and academic [10;11;12;13] communities. For example, the Open Sidewalks Initiative [14]. They are both a community and a project, providing dedicated mapping with an elaborate scheme on how to map in a pedestrian-centered way, but only manually. Drawing sidewalks and crossings is time-consuming and can be error-prone. This effort can be inferred by examining the OpenSidewalk's own Tasking Manager [15], where in the most near-completion project [16], each task has taken 9.4 minutes to be mapped, but 22.7 minutes be validated. Thus, considering just crossing mapping, for the 1046 existing tasks, it will take approximately 163.9 hours of mapping and 395.7 hours of validation. This total encompasses an area of just approx. 6.17 km2, only 0.65% of the urban area of the city of Sao Paulo, for example. To cover up this gap, we propose a Github-hosted, fully open-source QGIS Plugin entitled "OSM SidewalKreator" [17], which aims to automatically draw for OSM the geometries of sidewalks, crossings and kerb crossing interfaces, along with the basic descriptive tags. This tool gives the user the capacity to control and supervise the entire process. It contains a user-friendly GUI (Guided User Interface) that enables and disables the buttons according to the step in the process. The plugin methodology, encompassing the steps that the plugin runs through are basically: 1) Fetch Interest OSM data (highways and optionally buildings and addresses, when available) from a bounding polygon given by the user; 2) Generate a table for standard widths for the values for t

The United Nations Global Service Center (UNGSC) is developing Virtual Reality applications utilizing OpenStreetMap data. Through a Virtual Reality (VR) pilot project, UNGSC aims to provide UN peacekeepers on the field with 3D digital replicas of the cities in which they are operating, building a sandbox that enhances operational planning with simulations. In order to develop such applications, OpenStreetMap buildings are collaboratively edited, validated and ingested. Field mapping and street-level imagery are also extremely important to add details to the rendered buildings. There could be the possibility to organise a live demo through VR headset. The United Nations Global Service Center (UNGSC) is developing Virtual Reality (VR) applications utilizing OpenStreetMap data. Through the Virtual Operation Center (VOC) pilot project, UNGSC aims to provide UN peacekeepers on the field with 3D digital replicas of the cities in which they are operating, building a sandbox that enhances operational planning with simulations. Under the umbrella of the UN Mappers community, the areas of interest get mapped by means of Tasking Manager projects. Differently from other building mapping campaigns, those are different, as higher quality of the footprints is needed, as well as better placement at the ground floor level and understanding of tall buildings. Furthermore mapping can be complemented with field mapping with street-level imagery as well as smartphone applications, in order to add details on the buildings characteristics as height, levels, roof shape, color and material. After validation of all the edited data, the buildings are ingested into Virtual Reality software development tools as ArcGIS CityEngine and Unity to develop the sandbox application accessible through VR headset. Buildings get eventually rendered utilizing OSM tags which define the 3D model of each object. The activity has been already tested and developed in Mogadishu, Somalia, and Tripoli, Libya, where the mapping took place engaging with the local communities (OSM Somalia and OSM Libya). At the end of the talk, there could be the possibility to organise a live demo through VR headset. about this event: https://2022.stateofthemap.org/sessions/GNG37Y/

A recent release of new scientific datasets generated from OpenStreetMap exemplifies the need for analysis-ready repositories of OSM data that require minimal pre-processing. We created the Analysis-Ready Daylight OpenStreetMap Distribution to provide researchers with the opportunity for simple cloud-based SQL queries of nearly 1B OSM features. We demonstrate the capabilities with intrinsic and extrinsic data coverage assessments of OSM buildings globally. Despite being one of the most open and freely available spatial datasets, OpenStreetMap (OSM) data accessibility remains a challenge. Data accessibility measures how easily end-users can access and use a given dataset for their needs [1]. Because OSM data is intended to be rendered as a map or ingested into routing engines, it is often not easily consumable by data analysts. Pre-analysis workflows require OSM data to be downloaded, parsed, and converted into more common formats, which means that novice end-users of OSM may lack the experience to readily access and use OSM data in decision-making. Incorporating communities into spatial decision-making processes, such as mapping, is important because a). community members are experts on their communities and b). have a larger stake in final solutions which directly impacts their lived-experiences[2]. OSM empowers a variety of communities, including local governments[3], digital humanitarian groups[4], and even student groups [5], to help navigate and understand places of respective importance. Research by Nirandjan et al. recently lowered barriers to using OSM data as a reference dataset of critical infrastructure [6]. After categorizing and quantifying particular types of OSM features, the authors released the data in formats more common in geospatial analysis, such as GeoTiffs [6]. This article’s popularity (ranked 90th percentile on the publisher’s website) demonstrates the importance of making OSM data—and datasets derived from OSM—more accessible by means of familiar data structures compatible with common tools. If OSM data were more accessible for analysis, could we see it used in more geospatial research and innovation at large [7]? While many community-maintained tools exist to convert, extract, and download OSM data, each requires domain knowledge of the unique OSM data structure (nodes, ways, and relations). Furthermore, working at the country or planet-scale requires extensive computational resources. To further lower the barriers to entry for OSM data analysis and extraction, we created the Analysis-Ready Daylight OpenStreetMap Distribution (ARD-OSM). ARD-OSM is published on the registry of open data (RODA) on Amazon Web Services (AWS), where it is freely available to anyone [8]. This dataset containing 1B OSM features is optimized for use with Amazon Athena, a serverless interactive query engine on AWS. Additionally, ARD-OSM has resolved the OSM data format into common geometries such as points, lines, and polygons. Data also includes pre-computed valuable attributes such as length, surface area, quadkeys, and geographic bounding boxes which are stored as additional metadata. To demonstrate the analytical capabilities of this dataset, next, we will perform a global OSM building density assessment. Building density is a common metric in OSM quality research, often used to assess map coverage and completeness, such as studied by Yeboah et al. [9]. Measuring building density requires counting all of the buildings within a defined unit of spatial analysis. We use zoom-level 11 map tiles to create an analysis grid that encompasses the global built environment in fewer than 1M tiles. Then, we divide the building count by the area of each map tile to obtain the number of buildings mapped per square kilometer. Since every feature in ARD-OSM includes the zoom-level 15 quadkey of the map tile in which it exists, we can use a SQL GROUP BY expression instead of a geospatial operator for aggregation. Here is the short query used to count the number of buildings in each zoom-level 11 map tile: ```sql SELECT substr(quadkey, 1, 11) as z11_tile, count(id) as number_of_buildings FROM analysis_ready_daylight WHERE tags[‘building’] IS NOT NULL AND release = ‘v1.12’ GROUP BY substr(quadkey, 1, 11) ``` In May 2022, running in AWS region us-east-1, this query took 15 seconds and cost just USD $0.10. The results of this query show the density of mapped buildings in OSM to be highest in Europe with additional areas of high density where Humanitarian mapping campaigns have been active such as Nepal, South Eastern Asia, and isolated parts of Africa. This is consistent with the findings of Herfort et al. [10]. How should these densities be interpreted? Do denser regions have higher levels of building completeness in which most or all buildings are mapped? Building density is an intrinsic data quality measure, to further contextualize these findings, we need to perform an extrinsic assessment by comparing our results again

The opening session of the Academic Track at the State of the Map 2022 conference. about this event: https://2022.stateofthemap.org/sessions/RBZHX7/

In den letzten Jahren wurde die openSUSE-Infrastruktur deutlich verbessert. Aber ist sie jetzt perfekt? Natürlich nicht - sonst hätten die Heroes (= openSUSE Admin-Team) Langeweile, und ich könnte diesen Vortrag nicht halten. Wo Leute arbeiten, passieren natürlich auch lustige[tm] Dinge. Bringt bitte Euer eigenes Popcorn mit ;-) about this event: https://programm.froscon.org/2022/events/2780.html

The Small Device C Compiler (SDCC) is a free compiler targeting various 8-bit systems, including common microcontroller (µC) architectures. The SDCC-STD-UX project funded by the Bundesministerium für Bildung und Forschung aims to improve standard compliance in SDCC, in particular support for current and future C standards. SDCC targets common 8-bit µC architectures, such as the MCS-51, STM8, S08, Rabbit and Padauk, as well as some architectures now mostly relevant to retrocomputing or -gaming, such as Z80 and SM83. As a C compiler, SDCC aims to support current and future C standards, but is not up to the level of GCC or clang. For debug information, there is some basic support for ELF/DWARF. This summer, the Bundesministerium für Bildung und Forschung via the Prototypefund funds the SDCC-STD-UX effort to improve standards compliance and usability in SDCC. This ngoing effort has already resulted in substantial improvements in support for C standards that will be in the future SDCC 4.3.0 release. about this event: https://programm.froscon.org/2022/events/2819.html

Tui Widgets ist eine von Grund auf neu entwickelte c++ Library. Sie stellt flexibel anpassbare Elemente zur Entwicklung von Terminal User Interfaces bereit. Dabei legen wir Wert auf eine Benutzerinteraktion, die sich analog zu Desktop Anwendungen verhält. Dieser Vortrag gibt eine Übersicht über das Framework und demonstriert anhand einer Beispielanwendung, wie das Framework verwendet wird. Auf der vergangenen FrOSCon hat Martin breites termpaint vorgestellt. Darauf aufbauend zeigen wir euch dieses Jahr Tui Widgets. Es kombiniert klassisches Look and Feel in der Tradition von Turbo Vision mit QtCore als Basis für Widgets und Event-Loop. Dabei werden die Widgets über Layout-Manager angeordnet, die auf Veränderungen der zur Verfügung stehenden Fläche reagieren können. Es stehen Elemente für Texteingabe, Radio- und Checkboxen, Buttons, Listen und Menüs genauso wie Fenster zur Verfügung. about this event: https://programm.froscon.org/2022/events/2743.html