uk: Комп'ютерні науки

description: Efficient three-dimensional survey techniques and their comparison in open software in the archaeological test site of "Ninfeo maggiore" and "Ninfeo minore" of Formia (latina, Italy) The survey took place in part of the so-called Roman Villa of Caposele, also known as Villa Rubino (Giuliani and Guaitoli 1972; Cassieri 2015). The Villa, built by the Dukes of Marzano and subsequently passed into the hands of Charles of Ligny, Prince of Caposele, was purchased by Ferdinand II of Bourbon in 1845, with the aim of making it a luxurious summer residence. The building overlooks the inlet of Caposele, where there must have been a small harbour, and is squeezed between the Via Appia and the sea. To the west of the small port are the remains of an imposing structure with a central courtyard, datable to the 1st century B.C., which scholarly tradition has identified as Cicero's Academy or School, although it is probably a horreum, testifying to the utilitarian vocation of this area of the villa. In later phases, while retaining its intended use, the horreum would be incorporated into a residential building complex together with other structures further to the west that, too, may have served as warehouses in the earlier phase. To the east of the marina is the residential area, the area in which the survey operations were concentrated. Here, on a front about 140 metres long, there are a series of rooms with barrel vaults that were probably part of the basis villae of the building. In two of these rooms are the so-called minor and major nymphaea. The first consists of an almost quadrangular room with a roof supported by four Doric brick columns; on the back wall, in a large niche, spring water gushes out. The wall decorations include stucco, shells and incrustations of glass paste and small stones. The main nymphaeum, on the other hand, is divided into three naves and covered with a rounded coffered vault supported by Doric columns. The large niche at the bottom of the nymphaeum contains a pool of spring water; the floor is in white mosaic with polychrome dots. These nymphaeums constitute the focus of the intervention. In front of this front there was a very large fishpond, which ran into the sea for about one hundred metres in length, with a width of over 200. Because of its architectural features and good state of preservation, the central body of the monument has always been a great attraction for visitors and scholars, many of whom have left descriptions and drawings in their diaries. The two nymphaeums have to be surveyed both for conservation and study purposes and in order to allow a virtual visit, which is particularly important since they are located inside a private property. As already described, the structure is complex, with a succession of rooms and environments in an archaeological complex extending approximately 480 metres in an east-west direction and approximately 50 metres in a south-north direction. The survey of such an extension and such an articulation with consolidated techniques such as terrestrial laser scanning would probably have required days of work, and for this purpose we wanted to test the possible use of the most modern SLAM techniques, in particular using a GEOSLAM Zeb Horizon, totally transportable by an operator and with a range of up to 100 metres (https://geoslam.com/solutions/zeb-horizon/). In order to compare the times, modes, precision and accuracy of the point cloud thus obtained, we took advantage of the possibilities provided by the open software "Cloud Compare 2.11.3 64 bit version", which allows us to compare point clouds of different origins. Cloud Compare allows comparisons to be made with various methods of calculating distance and to estimate precision and accuracy separately, allowing one cloud to be fitted to the other or to be compared while remaining within their absolute coordinates. In the present experimentation it was therefore decided to survey both nymphaea with the "GEOSLAM", also surveying all the internal connecting rooms and corridors between these two environments. The whole survey was carried out in a few tens of minutes and therefore the survey continued over most of the exterior of the entire structure. The survey of the entire complex was not carried out because the main interest of this project was to test the SLAM technology and validate its precision and accuracy in comparison with more consolidated techniques. For comparison, only the major nymphaeum was surveyed with a more consolidated laser Faro" terrestrial scanning laser.In order to verify the validity of the Slam also on the external part, a survey was carried out using a DJI Matrix drone with laser scanning. Finally, the same survey was also carried out with an optical camera on the same Matrix drone and with the most widely used drone for photogrammetry, i.e. the "Phantom 4 pro", also by DJI. All the surveys were framed with respect to the same network of ground control points, in order to refer them to the same framing system and be able to assess their precision and accuracy. It should be noted that Slam was only able to station a few of the GCPs while, as can be easily guessed, the drones acquired practically all of them.

uk: вхід без авторизації


Daniele Codato

type: Person

cs: Informatika

de: Informatik

fi: tietojenkäsittelytiede

uk: Комп'ютерні науки

en: Computer Science

fr: Informatique

da: Datalogi

es: Ciencias de la Computación

nl: Informatica

cs: Technické vědy

de: Ingenieurwissenschaften

fi: tekniikka

uk: Інженерія

en: Engineering Sciences

fr: Ingénierie

da: Ingeniørvidenskab

es: Ingeniería

nl: Engineering

description: The last two decades have seen the development and diffusion of new technologies and digital ecosystems for managing geographic data. These include, among others, smartphones, drones and open access satellites on the one hand, and the web 4.0, GIS, WebGIS, geo-app and georeferenced data, both open-source or proprietary, on the other. This great variety of tools, accompanied by the sharing of new digital knowledge and skills, have made the creation and management of spatial information much more accessible than it was in the past. This has led to a proliferation of processes for exploring, creating and sharing geographical data from below as a way for citizens, that assume the role of neo-geographers or prosumers, to take part in decision-making in different kind of processes, such as territorial, environmental and climate change issues (Goodchild, 2009; Capineri et al., 2016; See et al., 2016). However, these are ongoing processes that have still to face technological, cognitive and economic barriers. Universities with the use of open-source geo-information and communication technologies (Geo-ICTs) in enhance geographical learning should be a primary actor in supporting students and citizens in developing their own spatial thinking in a more efficient and engaging way (Käyhkö et al., 2021). In fact, this is remarked also in objective 4 of the Sustainable Development Goals "to guarantee quality, inclusive and equitable education and to promote lifelong learning opportunities for all” and many universities have signed the Higher Education Sustainability Initiative (HESI) which commits them to integrate the concepts of sustainable development into the curricula. In this framework is involved also University of Padova (Italy) with its Jean Monnet Centre of Excellence on Climate Justice (Jean Monnet Erasmus+ project 2021-2023) led by the research group “Climate change, territories, diversities” (https://www.climate-justice.earth/). The Centre is trying to respond to the need of bringing the issues of Climate Justice and just transition from the EU Green Deal framework into the dialogue between the academic world, society, and policy makers. To do this, it is carrying out different research and didactical activities, among which the development of a MOOC (Massive Open Online Course) on GIScience for Climate Justice with the use of opensource and freeware Geo-ICTs, that will be freely available for all before the end of 2022. This MOOC will provide videos and materials about practical activities concerning climate change and climate justice issues, that the students can carry out autonomously using open-source and freeware tools. For every activity the workflow and a graphical abstract will be provided with aims and skills to be acquired and an introductive video with a real example of use and suggestions about how to build collective projects of citizen science. An auto-evaluation module will be available to students. MOOC will be tested with selected students and eventually adjusted before its online publication. A feedback and comment area to interact with staff members will be also available in the platform. The programme will follow learning by doing approach and is design to drive students through the main phases of a GIScience project: - The exploration and use of the European Platforms (e.g Earth Observation Portals, Joint Research portals, European Environment Agency portals, European Environmental Bureau) - The exploration and use of the Geonode on Climate Justice, the geo-platform of the Centre that will be available to everyone with all the information collected by the Centre and the possibility to create online maps and to upload and share data by interested users or association groups. - The Collection and sharing of environmental and social information using geo-app and webGIS (e.g odk collect app and ona platform) - The exploration and use of Google Earth Pro and the OpenStreetMap project Umap - The creation of storymaps to share climate change fighting initiatives and climate justice stories on the web (e.g knight lab storymap and geonode storymap tools) By completing the MOOC, students will learn how to autonomously update and increase their knowledge on climate change and climate justice issues, learning to navigate and use European platforms and portals and to search for the documentation available in the European and international institutions. Practical activities will improve skills of students and organizations of civil society to obtain and use data and information produced by European institutions, to produce and share their own data, and to prepare and manage collaborative projects for sustainability and environmental monitoring. Open-source software will be also the basis for the setup of the MOOC, from its preparation using open video editing and open document formats, to its publication using the Moodle of the University of Padova. In this contribution, the theoretical background, the entire methodology and workflow process for the preparation and dissemination of the MOOC will be presented and discussed, with the aim to disseminate and share this experience to actors interested in developing similar activities of using of Geo-ICTs for Good.

TIB AV-Portal

type: Service

type: WebContent

type: MediaObject

@language: en

cs: přihlášení není vyžadováno

de: keine Anmeldung erforderlich

uk: вхід без авторизації

en: no login required

nl: geen login vereist

name: Geo-ICTs for Good: a MOOC on GIScience for Climate Justice

type: Organization

name: Open Source Geospatial Foundation (OSGeo)

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


uk: Комп'ютерні науки

description: From QGIS to Python: comparison of free and open tools for statistical analysis of cultural heritage and data representation Thankfully to the European Commission initiatives such as INSPIRE (2007) and other governmental policies, spatial data are available publicly on different national, regional and municipality geoportals for further use. When it comes to the cultural heritage and Italian context, based on the decree of the Ministry of Culture (MiBACT, 2008), different activities concerning heritage has been assigned to the ICCD (i.e., Central Institute for Catalogue and Documentation) such as research and technical-scientific collection of the documentation and coordination of cataloguing of cultural heritage and its digitalization. These regulations allowed the public entities to share substantial information about geographical and spatial data with a wider audience. Specifically in the region of Lombardy, data about cultural heritage are catalogued in SIRBeC (i.e., Regional information System for Cultural Heritage) that has been promoted since 1992 and continues collecting, managing, and publishing a vast amount of information. Vector shapefiles are freely available for download on the Geoportale Lombardia. The scope of the research was collecting information about cultural heritage in Lombardy that is freely accessible online. Data downloaded are point and polygon features files of the position of the cultural heritage. Furtherly, the methodology developed deals with the use of QGIS, as the open and free software together with the Python console integrated into the software and finally using the online software of the integrated development environment (IDE) named Replit that is free, open, collaborative and in-browser Python coding application. The methodology is based exclusively on free and open sources, starting from the collection of data to their processing. Each vector file is enriched with the metadata in the attribute table but the methodology is providing a combination of software to obtain other data (e.g., coordination, area, etc.) and statistical analysis (e.g., ratio, percentage, position, distribution, etc.), which are the initial part of each elaborated cultural heritage project. Additionally, the methodology is discussing different approaches to reach the desired result and compares their differences. Firstly, the Python console in QGIS was examined, and metadata were extracted from the vector file to the .csv file to be used in Replit. The online codding application gave a higher degree of flexibility while coding, and it was possible to implement data extracted in a .csv file into a coding panel, using them to produce different statistical analyses. Furtherly, the methodology discusses the use of the plugin of QGIS called DataPlotly and data differences, from the representation to the utility level. Results through the Python Console in QGIS allowed the extraction of necessary data for further analysis, deleting the ones which are not needed. The good side of this approach is that metadata of the shapefile stay untacked, and the Python is simply extracting selected data in a new external file. There have been selected four categories of interest: Name, Category, Typology and Municipality of the cultural heritage. The area of interest was a northern part of Milan, in the province of Monza e Brianza which has a dense and diverse category of cultural heritage. Using the python code, these four categories are temporarily printed and saved in the console panel. Since there is no information about coordinates inside the metadata, there are two approaches that are tested to obtain them. The first one used was the QGIS integrated option "Add geometry attributes", which created the new shapefile enriched with the information about longitudinal and latitudinal coordinates. The second approach was extracting the coordinates through the Python console with the f.geometry() function. Information about the four categories selected and coordinates are printed temporarily in the console, and the user can control the order of the columns and delimited type, following the saving and extracting the .txt file. The second part of the analysis also discusses two methods that were tested for the creation of statistical analysis of extracted data and their representation, firstly in the QGIS plugin DataPlotly and then using Replit. Presenting statistical analysis in the form of different charts is available directly through the plugin. Nevertheless, when it comes to the great amount of data the plugin resulted not be very efficient for the representation nor easy to manage the view. Another constrain is that there is no option for exporting graphs in a .pdf file. On the other side, creating the charts through the Python packages such as matplotlib or pandas shows a better degree of control over a graph. The advantage is that there is a possibility of exporting it in many different files, such as a .pdf or .svg file. Additionally, through the Python in-browser application, there is a higher degree of control and change of the visual representation of charts.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Introduction: Legally defined appellation areas are used by governments throughout the world to demarcate geographic areas that produce agricultural products, such as wine, cheese, or preserved meats, with a specific quality or set of characteristics. In the United States, the American Viticultural Areas (AVAs) define wine growing areas that are distinctly different from others. These boundaries are created by the US Alcohol and Tobacco Tax and Trade Bureau (TTB) through a legal process and the definitions are published in the United States Federal Register in narrative form defined using United States Geological Survey (USGS) topographic maps for their landmarks. Despite their geographic definition, a full spatial dataset of these boundaries following the legal definitions did not exist until they were created by a team of researchers led by the University of California Davis’ (UC Davis) library. The purpose of the dataset is to produce open data suitable for use in research and cartography following a well-documented set of methods that represents the official boundary descriptions with as high fidelity as possible. Using the UC Davis AVA dataset alongside datasets defining environmental characteristics such as soils, climate, and elevation, we seek to understand how the characteristics present within the AVA boundaries are similar to each other using a hierarchical clustering process. Through this case study, we will describe the UC Davis AVA boundary dataset and demonstrate a use case for the data. Data: The UC Davis AVA dataset was created by digitizing the boundary narrative onto the USGS topographic maps described in the legal documents (officially known as the “approved maps”) for each AVA by a team of collaborators at UC Davis, UC Santa Barbara, and Virginia Tech University, as well as community volunteers. For each boundary, we recorded attributes including an identifier, the official name of the AVA, any synonyms for the name, the dates the AVA officially was recognized, the start and end date for the given polygon, who petitioned to define the AVA, which TTB staff member wrote the official documents, the list of approved maps, the list of maps used to digitize the boundary (to record any necessary substitutions), and the official boundary description. In addition to the currently defined boundaries, we also created a boundary polygon for the previous iterations of any boundaries that have undergone revisions. The dataset is stored in geojson format in a publically available GitHub repository and updated as AVAs are created or amended. For each AVA, we summarized the environmental data over the area of the polygon. The PRISM dataset (from Oregon State University) provided the climate data (30-year climate normals for precipitation and temperature) and elevation data in raster format with an 800m cell size. For each variable, we calculated the mean and the range within the AVA boundaries. We also plan to expand this analysis over the coming weeks to include additional environmental characteristics available from PRISM, such as vapor pressure and solar radiation that would be important considerations for grape growth, as well as soil data from the United States Department of Agriculture’s (USDA) SSURGO (Soil Survey Geographic) soil dataset. SSURGO is a spatially-enabled dataset of soil characteristics for the United States. It includes geologic soil series names as well as the soil’s chemical attributes. Analysis: For each attribute, the value at each AVA was assigned a z-score, calculated as the mean of the attribute field subtracted from the value and divided by the standard deviation of the field. This was done to normalize the data and reduce the effect of differing scales of measurements (for example, depth of precipitation compared with temperature in degrees Celsius). To assess how similar any given AVA is to other AVAs, we performed a hierarchical clustering analysis using R’s hclust() hierarchical clustering function. This tool uses a dissimilarity matrix to assign each polygon to a hierarchical series of groups based on how similar (or dissimilar) each polygon is to each other. The results can be displayed in a dendrogram to visualize the structure of the classes. The classes can also be used to create a map of the AVAs to help interpret the groups. Results: Preliminary results group AVAs into clusters that appear to be somewhat based on geographic regions, but not entirely. When the dendrogram is cut into 6 groups, the AVAs in the eastern half of the country primarily fall into one group, however, the western AVAs comprise the remaining 5 groups. This could be driven by the higher degree of variation in elevations, precipitation, and temperature in the west. In the southwest, the AVAs appear to correspond to one group, however, the west coast states have many groups, including some AVAs that correspond with the eastern group. Expanding the analysis to include additional environmental factors will likely clarify some of these groups, perhaps defining more variation in the east. This paper will include maps and diagrams that clearly show the relationships between the groups. Discussion: Investigating the relationship between the AVA boundaries is an important exercise. With the availability of the AVA boundaries as a geographic dataset, we are now able to combine this data with other existing open datasets to better understand the relationship and differences between these areas. All of the datasets used in this analysis are freely available and demonstrates not only the usefulness of the UC Davis AVA dataset but also the depth of the work possible with open data. This particular exploration builds on work I have published with colleagues investigating the Sierra Foothills AVAs in the state of California and the emerging wine growing region in the state of Arizona.

uk: вхід без авторизації

name: Classifying American Viticultural Areas Based on Environmental Data


uk: Комп'ютерні науки

description: Map renderers play a crucial role in various applications deployed in Web, desktop, mobile, and embedded environments. For instance, we rely upon them to travel, commute, find the best hotels and restaurants, and locate our closed ones. More digital applications emerge in various areas, such as urban planning, transportation, or even pandemic monitoring, as they get adopted. Beyond digital environments, it is worth noting that maps also get printed in books, reports, or pieces of urban furniture. In this context, code portability, i.e., the ability to use the same codebase on various platforms, is a common problem. For instance, Mapbox and Maplibre both maintain a JavaScript codebase for the Web (e.g., maplibre-gl-js) and a C++ codebase for native platforms (e.g., maplibre-gl-native). These codebases enable their renderers to run in all major browsers (thanks to WebGL), in the main desktop and mobile environments, on servers (e.g., for headless rendering), and in cars, planes, or embedded settings. Guarantying that these renderers behave similarly and produce the same outputs on all these platforms is hard, costly, and slows down the ability of development teams to innovate and improve renderers. In this paper, we review the most popular map renderers from a portability point of view. We show that the existing codebases written in Javascript, C++, and Java fail at least in one area or another at producing a portable map renderer. Additionally, we present a state of the art for code portability, and we describe emerging standards and technologies that promise to enable truly portable and high-performance map renderers written in C++ or Rust to emerge. Among these emerging technologies, we find: - *Rust -* Rust is a high-level programming language designed for safety and high performance. The project started at Mozilla and is now developed by the Rust foundation. Its compiler targets native architecture, enabling it to compile applications for desktop (x86) and mobile (arm) environments. Additionally, the Rust compiler can target WebAssembly, a binary instruction format that can run on web browsers with near-native speeds. This not only enables Rust applications to run in native environment but also to be included as a library in Web applications. As a result, the same codebase can be used anywhere with only a few modifications. - *WebGPU -* WebGPU is a 3D low-level API that runs on top of DirectX, Metal, Vulkan or OpenGL depending on the platform and gives the developer access to the GPU. It is developed by the W3C GPU for the Web Community Group with engineers from Apple, Microsoft, Mozilla, Google, and others. It is considered the successor of WebGL version 2. Contrary to WebGL version 1 and WebGL version 2, which were solely designed for the Web, WebGPU implements a standard header file (webgpu.h) that makes it cross-platform. Based on the emerging technologies identified in the review, we study the feasibility of creating a truly portable map renderer. We present maplibre-rs, a proof-of-concept released under the terms of the Apache Software License, that can render vector tiles natively and in the browser. We describe its overall architecture and highlight some of the challenges encountered while devising a portable solution that transforms vector tiles into 2d and 3d objects. These challenges include: - *Rendering 2d vector tiles in a 3d environment -* The vector tile specification describes simple 2d objects encoded in grid coordinates, such as points, lines, polygons, multi-polygons, and polygons with holes. Several steps enable to convert these 2d objects into 3d objects that can be rendered in a scene, including: the conversion of grid coordinates into 3d scene coordinates; the tessellation of polygons to display surfaces in the 3d environment; the extrusion of buildings based on their number of storeys with an attribute stored in the vector tiles. - *Using WebGPU as a portable 3d rendering pipeline -* WebGPU exposes a wide variety of features to render 3d scenes. Among them, we explore: the rendering of the 3d objects with the WebGPU Shading Language (WGSL) based on styling rules and object attributes; The navigation within the 3d world with the camera, user inputs, rotation on 3 axes, levels of details and occlusion culling; The configuration of the graphic card, graphics API and more. - *Devising a portable network library -* Rust does not provide a network library that works both natively and in the browser. We created a uniform interface to download vector tiles to address this issue. This interface, based on the facade pattern, uses macros to select the proper implementation at compile-time depending on the targeted architecture. The native implementation relies on the HTTP package of the standard library. The WebAssembly implementation relies on Fetch API bindings. Finally, we present our future work and explore possible ameliorations. Overall, this review and feasibility study gives an exciting glimpse on a possible future for map renderers, where the same code can run natively and in a browser.

uk: вхід без авторизації

name: Maplibre-rs: Toward portable map renderers


uk: Комп'ютерні науки

description: Land surface temperature (LST) in urban areas is an important environmental variable considered a reliable indicator of the urban heat island (UHI) phenomenon. LST is affected by various factors such as solar irradiance, cloudiness, wind or urban morphology. Traditionally, LST is observed and recorded by thermal remote sensors. For example, thermal satellite sensors are very popular for assessing the UHI effect on a global scale such as MODIS, Sentinel 3, ASTER, Landsat 7 ETM+, or Landsat 8 TIRS. However, these sensors provide rather low spatial (60 m to 1000 m) and temporal resolutions (several hours to days) of satellite observations that limit the accurate estimation of LST in urban areas for local studies and specific time periods (Mushore et al., 2017), (Hu and Wendel, 2019). Airborne or terrestrial remote sensing can be viewed as another option for capturing higher spatial resolution of thermal data but it is not feasible to be used for large urban areas with increased periodicity. However, the increasing availability of the high-resolution geospatial data and adequate modeling techniques provide an alternative approach to high-resolution estimation of LST in urban areas. Several studies showed the potential of geographic information system (GIS) tools, digital surface models (DSM) and 3-D city models for the estimation of solar radiation in urban areas (e.g., Hofierka and Kaňuk, 2009; Hofierka and Zlocha, 2012; Freitas et al., 2015; Biljecki et al., 2015). Solar irradiance is a key factor affecting LST during daylight periods, especially under clear sky situations. Nevertheless, LST assessment requires a physical model combining surface-atmosphere interactions and energy fluxes between the atmosphere and the ground. Properties of urban materials, in particular, solar reflectance, thermal emissivity, and heat capacity influence the LST and subsequently the development of UHI, as they determine how the Sun’s radiation energy is reflected, emitted, and absorbed (Hofierka et al., 2020b; Kolečanský et al., 2021). It is clear, that the problem complexity requires a comprehensive GIS-based approach. Our solution is based on open-source solar radiation tools available in GRASS GIS, a 3D city modeling and spatially distributed data representing thermal properties of urban surfaces and meteorological conditions (Hofierka et al., 2020a, 2020b; Kolečanský et al., 2021) . The proposed LST model is calculated using the methodology implemented in GRASS GIS as a LST module written using a script (shellscripts, Python). In these scripts, the r.sun and v.sun solar radiation models in GRASS GIS were used to calculate the effective solar irradiance for selected time horizons during the day . The solar irradiance calculation accounts for attenuation of beam solar irradiance by clouds estimated by field measurements. The proposed LST model also accounts for a heat storage in urban structures depending on their thermal properties and geometric configuration. The 2D LST model uses the output of the r.sun solar radiation model and a DSM representing urban surfaces and the 3D LST model uses the output of the v.sun solar radiation model and a vector-based 3D city model. Computed LST values for selected urban surfaces were validated using field measurements of LST in 10 locations within the study area with acceptable accuracy. The proposed approach has the advantage of providing high spatial detail coupled with the flexibility of GIS to evaluate various geometrical and land surface properties for any daytime horizon. The methodology can be used for evaluation of proposed UHI mitigation measures such as increasing albedo of urban surfaces or expanding green areas including green roofs and trees.

uk: вхід без авторизації

name: Assessing land surface temperature in urban areas using open-source geospatial tools


uk: Комп'ютерні науки

description: The challenges posed to the current urban mobility model by pollution-related and urbanisation issues have resulted in significantly increasing the importance of urban resilience. Mobility management, pandemics’ spreading, equal access to services and climate crisis are just some of the crucial issues that falls within the definition of urban resilience. One very promising solution aiming to solve many of these issues has been presented in 2016 by Professor Carlos Moreno under the name of “15-minutes city”. The paradigm is based on the idea that every citizen should be able to reach the essential services (supermarkets, shops, parks, etc) walking not more than 15 minutes from their home. The model is being tested in some metropolitan cities around the world (e.g. Paris). However, reorganizing the city so that it presents a 15-minutes structure is not an easy task. It requires large resources and a careful planning based on data, to make sure that the project undertaken will actually have a positive effect on the urban mobility and no asset is wasted on useless projects. The Business Innovation team of Dedagroup Public Services used Open Street Map data to develop an index to detect the local level of proximity within the city, showing both the areas that already conform to the 15 minutes model and the ones that do not, where taking action would improve the quality of life of the citizens living there. The presentation will be focused on this proximity index, describing the assumptions behind its definition, such as the choice of city services to be considered essential, the nature of the road network used to compute walking distances and the area tiling chosen for the task. The index will be then showcased on the city of Florence, together with an analysis of the city from a proximity point of view and a what if scenario: how would the index change if the municipality (and other relevant stakeholders) decided to make interventions on low proximity areas? The case of Ferrara will be also presented to show that the proximity index can be the basis for further analyses: coupling the index with resident population count can help to spot the areas that are both under-served and highly populated, that are the ones where more people would benefit from improvements.

uk: вхід без авторизації

name: How much “15-minutes” is your city? Using open data to measure walking proximity


uk: Комп'ютерні науки

description: The use of open source software for monitoring bee diversity in natural systems: the BEEMS project This work wants to highlight the results obtained during the BEEMS (Monitoring Bee Diversity in Natural System) project, which the main goal was to answer the following question: Which biotic and abiotic indicators of floral and nesting resources best reflect the diversity of bee species and community composition in the Israeli natural environment? To this end, the research was oriented towards the cost-effectiveness analysis of new aerial geomatics techniques and classical ground-based methods for collecting the indicators described above, based only on open-source software for data analysis. The study involved the Israeli and Italian teams, focusing the attention on two complementary study systems in central Israel, the Alexander Stream National Park, an area undergoing an ecological restoration project in a sandy ecosystem, and the Judean foothills area, to the South of Tel Aviv. In each study system, different surveys of bees, flowers, nesting substrates and soil, using classical field measurement methods have been conducted. Simultaneously, an integrated aerophotogrammetric survey, acquiring different spectral responses of the land surface by means of Uncrewed Aerial Vehicle (UAV) imaging systems have been performed. The multispectral sensors have provided surface spectral response out of the visible spectrum, while the photogrammetric reconstruction has provided three-dimensional information. Thanks to Artificial Intelligence (AI) algorithms and the richness of the data acquired, a methodology for Land Cover Classification has been developed. The results obtained by ground surveys and advanced geomatics tools have been compared and overlapped. The results are promising and show a good fit between the two approaches, and high performance of the geomatics tools in providing valuable ecological data. The acquisition of the indicators identified in the planning phase took place through several measurement campaigns conducted in the period between February 2020 and April 2020 located in two areas of interest in the Israeli territory. A total of 934 and 543 wild bees were collected in the two systems under study, respectively. From a geomatics point of view, 8 flights were carried out in the Alexander Stream National Park on 24 February 2020, acquiring approximately 65 GB of 8-bit multi-band images in tiff format. In the Judean foothills area, 11 flights were carried out on 26 February 2020, obtaining approximately 77 GB of tiff images. In addition, in order to obtain a correctly geo-referenced 3D model, a total of 54 Ground Control Points (GCPs) were acquired, of which 27 in Alexander Stream National Park and 27 in the Judean foothills, with a multi-frequency, multi-constellation GNSS geodetic receiver in RTK mode. On the basis of the technical requirements necessary to carry out this project, very high-resolution digital maps (orthophotos, digital terrain models - DTM) were produced through the application and optimisation of photogrammetric and structure from motion (SfM) processes performed on data from different imaging sensors (RGB, multispectral), considering only open-source software. Therefore, considering all the previously defined aspects, in order to plan the data acquisition, the research group defined the flight parameters and instruments, both in terms of aircraft and sensors to be installed onboard, necessary to achieve the project objectives. All the digital cartography generated has been defined in the Israeli reference system, i.e. in WGS84 with UTM 36N cartographic projection. The results are shown in Tables 2 and 3 for the Alexander Stream National Park and the Judean foothills, respectively. The production of very high scale digital cartography allowed the extraction of the necessary data for training the proposed Artificial Intelligence model. These data were applied to two different approaches for automatic land cover classification. The first approach was based on unsupervised classification at the pixel level, while the second approach is based on object classification, i.e. vector polygons describing the boundaries of a real object. The algorithms operate differently on these two types of data, in fact in the pixel-based approach they are applied at the level of the single pixel, while in the object-oriented approach they are applied to groups of homogenous pixels for a given feature. The implementation of all the training and validation phases of the proposed models was based on Python programming language using open libraries for data management (shapely, raster) and learning (sk-learn). The segmentation of the input data is fundamental in the approach in order to define the objects to be classified, therefore the Orpheo Toolbox library was applied. The object-oriented approach was applied for the Alexander Stream National Park site while the pixel-based approach was applied on the Judean Foothills area. For pixel-based classification, a clusterization algorithm, KMeans, was used in an unsupervised manner. The KMeans algorithm clusters the data by attempting to separate the samples into n groups of equal variances, minimising a criterion known as within-cluster sum-of-squares. The algorithm was optimised through a trial-and-error procedure that led to the identification of initialisation parameters. For the object-oriented classification, we proceeded to apply automatic segmentation algorithms based on the analysis of multi-band spectral variability. In particular, the algorithm used is the Large-Scale Mean-Shift segmentation algorithm, which produces a clustered image in which the pixels around a target pixel that present similar behaviour from both the spatial and spectral points of view are grouped together. Then, the procedure vectorizes these clusters and the operator associates a label to each of them for the generation of the dataset. After subdividing the data into training and testing elements, the Random Forest algorithm was used for both approaches and proved to be the most effective in performing the assigned task. The classification results were carried out using different validation metrics such as Precision, Recall, F1 score, etc. that will be presented.

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description: The growth of OpenStreetMap communities (OSM) in Tanzania is taking shape as most organizations, institutions, and communities in general, are recognizing the importance of using and contributing to OpenStreetMap data. To support the growth of OSM communities in Tanzania, OpenMap Development Tanzania with her partner the Humanitarian OpenStreetMap Team (HOT) awarded microgrants to seven OSM communities in Tanzania - See the supported communities here. The grants provided are supporting these communities to leverage the use of OSM and mapping to help solve different community challenges by facilitating training/workshops, purchasing tools and equipment, supporting staff, and other logistics. Most communities work in peripheral regions with a minimal understanding and use of open data and mapping technologies like OpenStreetMap, OpenDataKit QGIS, etc. The first phase of project implementation ended with great successes and lessons learned from these communities. The general success of the microgrants so far include the following: 1. Transforming communities from using traditional data collection to digital open source tools such as ODK and Kobocollect has greatly improved data management and analysis. For instance, Agri Thamani Foundation and LAVISEHA are among microgrant recipients who are new to OpenStreetMap and other open mapping technologies for generating open data; however, they now use different tools, i.e. Kobotoolbox, OpenDataKit, OSM, and Tasking Manager, to collect data for their interventions in nutrition and gender-based violence. 2. Connecting OSM communities in Tanzania and encouraging collaboration in various tasks and opportunities. The grant provided an opportunity for local OSM communities in Tanzania to work together; a good example is Hope for Girls and Women in Tanzania, giving training to LAVISEHA on how communities can use open source tools like ODK to report the cases of GBV for rescue. 3. Creating awareness about OSM and other communities through participating and presenting in conferences and events such as the State of the Map Africa, Community webinars, etc. 4. Over 16000 building footprints were mapped and 380 km of roads were uploaded to OpenStreetMap by grantees 5. Supporting the growth of youth mappers chapters - Three grant recipients are youth mappers from three different universities who are using the grants to solidify the chapter and get exposure to projects while also applying for other funding to expand their projects Although the microgrant is expected to end in June 2022, OMDTZ is committed to supporting these communities through training and different engagements to ensure they achieve their goal of using open data for decision-making. Together we can add more people to a map by supporting all communities in mapping.

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name: The Growth of OSM Communities in Tanzania Through Community Microgrants


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description: Street-level photographs of New York City from the early 1900s show how people used to live, from their clothes and vehicles to their stores and advertisements. Several open source projects have mapped archival “street view” images of New York, relying on various collections of photos with locations. These interactives, primarily built with Mapbox GL JS, are instructive when visualizing a newly-digitized archive, in this case a set of over 60,000 photos from the construction of the NYC subway between 1900 and 1950 with approximate coordinates. 1. “Street View, Then & Now: New York City's Fifth Avenue” (publicdomain.nypl.org/fifth-avenue) compares 1911 wide-angle photographs from the New York Public Library to 2015 Google Street View imagery. A mini-map shows each photo’s location and field of view, and a visitor to the site can “go south”, “go north”, or “cross the street” using the arrow keys. The project came out of the NYC Space/Time Directory (spacetime.nypl.org), an initiative to communicate the history of the city using historical maps, geodata, and open source tools. Code: GitHub: nypl-publicdomain/fifth-avenue. 2. “1940s.NYC” (1940s.nyc) places digitized photos of most buildings in the five boroughs of New York City, collected from 1939 to 1941 by the Tax Department with help from the Works Progress Administration, on a map. Zooming in loads georeferenced scans of historical maps, and clicking on a marker opens a panel displaying the historical photos. “80s.NYC” (80s.nyc) remixed the site, using more recent images from the Department of Finance. Code: GitHub: jboolean/1940s.nyc, GitHub: bdon/80s.nyc. 3. “A Stroll Down Flatbush Avenue circa 1914” (stroll-down-flatbush.chriswhong.com) strings together 65 photographs, captured approximately every 50 feet, from the New-York Historical Society’s “Subway construction photograph collection, 1900-1950.” (digitalcollections.nyhistory.org/islandora/object/nyhs%3Asubway) Geometries for the photos, which are often set at nearby intersections, were manually modified, and a mini-map showing those points is navigable with the up and down keys. Code: GitHub: chriswhong/stroll-down-flatbush. The entire subway construction photograph collection contains nearly 100 times as many photos as shown along Flatbush Avenue with associated latitudes and longitudes across New York City. What are the best practices for mapping these geotagged archival photos, with imprecise and duplicate locations, as well as rich text metadata like titles, topics, dates, and descriptions?

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name: Mapping Historical "Street View" Images of New York City: Visualizing Geotagged Archival Photos


The Africa Knowledge Platform

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description: The Africa Knowledge Platform is a one-stop-shop for the European Commission (EC)’s scientific knowledge on Africa. Developed by the Joint Research Centre, this open, visual, and interactive platform brings together a wealth of geospatial scientific information on Africa’s social, economic, territorial and environmental development. The Africa Knowledge Platform is developed using open-source geospatial technologies and the whole platform is open to the public, so that its potential value extends to academia and to stakeholders from the public, private and non-profit sectors in both Europe and Africa. The platform brings together information across 62 topics within 10 broad themes: natural resources, sustainable growth and jobs, food and agriculture, climate change, human demography, health, security, economy, energy and digital transformation. This covers all 17 of the United Nations Sustainable Development Goals. We will be taking a tour and in-depth look at the workflow used to develop and publish the platform and the technologies behind it.

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description: In the last decades the European mountain landscape, and in particular the Alpine landscape, has dramatically changed due to social and economic factors (Tattoni et al. 2017). The most visible impact has been the reduction of the population for mid and high altitude villages and the shrinking of part of the land used for agriculture and grazing. The result is a progressive reduction of pastures and meadows and the expansion of the forested areas. Forest plots become also more compact, with the loss of ecotones. The study of this phenomenon is important not only to assess its current impact on the ecological functionality of forest ecosystems including biodiversity and natural hazards, but also to build future scenarios, taking into account also the climate change issues. The limit of the mountain treeline is gradually shifting upwards and the monitoring and modeling of these changes will be crucial to plan future interventions and try to implement effective mitigation plans. For these reasons, a dataset describing the forest, meadows and pasture coverage for the Trentino region, in the eastern Italian Alps, has been created. A set of heterogeneous sources has been selected so that maps and images cover the longest possible time span on the whole Trentino region with the same quality, providing the necessary information to create a LULC (Land Use/Land Cover) map at least for the forest, meadows and pasture classes. The dataset covers a time span of more than 160 years, with automatic or semi-automatic digitization of historical maps and the LULC classification from aerial images. The first set of maps includes historical maps from 1859 to 1936, with an additional map from 1992 which was not available in digital format and has been digitized for this project: Austrian Cadastral (1859, 13297 sheets, scale 1:1440), Cesare Battisti’s map of forest density published in his atlas ”Il Trentino. Economic Statistical Illustration” (1915, single sheet, 1: 500 000), Italian Kingdom Forest Map (IKMF) (1936, 47, 1:100 000) and Map of the potential forest area and treeline (1992, 98, 1:50 000). A new procedure has been developed to automatically extract LULC classes from these maps, combining GRASS and R for the segmentation, classification and filtering with the Object Based Image Analysis (OBIA) approach. Two new GRASS modules used in this procedure have been created and made available as add-ons on the official repository (Gobbi et al., 2019).. The second set of maps are aerial images, covering the time span from 1954 to 2015. The four sets which differ for mean scale, number of bands, resolution and datum: "Volo GAI" (1954, 130 images, mean scale 1:35 000, B/W, resolution 2m, Rome40 datum), "Volo Italia" (1994, 230, 1:10 000, B/W, 1m, Rome40), "Volo TerraItaly" (2006, 250, 1:5 000, RGB+IR, 0.5m, Rome40) and "Volo AGEA" (2015, 850, 1:5 000, RGB+IR, 0.2m, ETRS89). The "Volo GAI" imagery set has been ortho-rectified using GRASS, images in the other sets were already ortho photos. The aerial images were classified with OBIA to create LULC maps, with particular focus on forest, meadows and pasture classes. The same training segments were used across the 4 sets and the custom classification procedure has been scripted. The number of training segments ranges from 1831 for the 2015 dataset and 2572 for the 1954 imagery set. The evaluation of the results of the classification for all the maps and images has been carried out with a proportional stratified random sampling approach. A procedure has been scripted in GRASS to select 750 sampling points, distributed in each stratum (LULC class) proportionally to the area of the class. The resulting points have been manually labeled and used to assess the classification and filtering (where present) accuracy.[c] For the historical maps, the application of the custom filtering procedure has increased the accuracy from a minimum value of 67% (for the IMF map) to 93% (for the same map), with a maximum of 98% for the cadaster map. For the imagery datasets the accuracy (percentage of points correctly classified) was between 93% and 94%, with the latter value corresponding to the higher resolution 2015 imagery dataset. Higher accuracy, up to 95% was obtained for the forest class, which is the main focus of the study. The analysis of selected landscape metrics provided preliminary results about the forest distribution and pattern of recolonization during the last 180 years. A comparison between the capabilities of FOSS4G available systems for landscape metrics was performed to evaluate the best analysis tools (Zatelli et al. 2019). Finally, these time series of LULC coverage were used to create future scenarios for the forest evolution in a test area of Trentino in the next 85 years, using both the Markov chain and the Agent Based Modeling approaches with GAMA (Taillandier et al. 2018). Given the large number of maps involved, the great flexibility provided by FOSS for spatial analysis, such as GRASS, R, QGIS and GAMA and the possibility of scripting all the operations have played a pivotal role in the success both in the creation of the dataset and in the extraction and modeling of land use changes. The development of new GRASS add-on modules, based on the scripts created during this study, is planned.

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name: Modeling of forest landscape evolution at regional level: a FOSS4G approach


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description: Environmental monitoring management of waste from large excavations due to infrastructure buildings Large infrastructure building like the Florence Railway Station designed for high-speed rails requires a proper management of the huge quantity of waste originating from excavation activities. Such waste amounts require large areas for disposals, making abandoned areas or exhausted quarries and mines ideal sites for hosting the excavated wastes. A rectangular area of 500x70m delimiting the railway station has been excavated in two steps causing the removal of a 10m-thick soil layer per step: the amount of construction waste, as stated in the approved management project by public authorities involved in environmental management plans, would be used for the environmental restoration of an area of 400x350m located near a former exhausted lignite quarry) located in the proximity of the Santa Barbara village near Cavriglia (Arezzo). The Tuscan Regional Environmental Agency (ARPAT) have been involved in monitoring both the terrain transportation and disposals’ operations according to the approved management plan: while the Environmental Evaluation Office (VIA-VAS) was responsible of the waste sampling for further chemical analysis to assess the acceptable waste chemical composition, the Environmental Regional Information System Office (SIRA) was asked to evaluate volume balancing between all the waste management cycle, with included: (a) waste extraction from railway station site building, and (b) waste disposal final destination (exhausted Santa Barbara lignite quarry). A phase difference terrestrial LiDAR have been used in acquiring the 3D point cloud at the railway site at the following stages: (a) initial stage, before excavation activities’ starting (b) step 1 stage, after the first 10m-thick layer excavation (c) step 2 stage, after completion of excavation works. Various tests have been performed to assess the optimal number of scans allowing to obtain the required precision of the final 3D model, stating from more than 100 scans for the survey for the initial stage to about 50 scans used for (b) and (c) stage surveys. Each survey was referenced by using a local coordinate system materialized during the survey; each target was then referred to the mail local reference system used in the railway station project by the owner’s topographers with a total station. Scan alignment and 3D cleaning (point clouds and meshes) was made using proprietary licensed software, while volume differences evaluation was made in QGIS 3.x environment; as for the scan alignment phases (3D point clouds’ alignment), available open-source platforms have been tested and evaluated. Both scan alignment and 3D cleaning, while manually executed, have been proven to be time-consuming operations even using proprietary-licensed sofware. As for Santa Barbara quarry, an initial RTK RPAS was performed before grass and small vegetation removal to evaluate the potential of RPAS over the survey area in speeding survey activities with respect to the terrestrial LiDAR in open areas: the RPAS survey demonstrates that such technology, compared to terrestrial LiDAR surveys in open areas, is much less time consuming in both acquisition and processing time, making it the best choice for surveys in open areas where extreme precision (sub-centimetric) is not required. Due to work progresses in filling activities at Santa Barbara site, i.e. the partial cleaning of one of the defined file subareas followed by its filling with excavated wastes, the initial stage of waste filling was surveyed in five times, one for each of the defined subareas. Each subarea survey, due to its limited dimensions (120x50m), instrumentation and personnel availability at the time of vegetation cleaning, have been surveyed with the terrestrial LiDAR, while for the final survey over the whole quarry area the RTK RPAS have been used. LiDAR surveys have been processed according to the tested methods in railway station surveys processing; RPAS RTK survey data, too, have been processed with the same proprietary software. Terrestrial LiDAR surveys were referenced in a local coordinate system by using a local coordinate system materialized during the survey; each target was then referred to the mail local reference system used in the quarry filling project by the owner’s topographers. The RPAS models, in geographic coordinates, were then aligned to the terrestrial LiDAR surveys in order to evaluate the global waste volume disposed onsite. Comparison operations between excavated volume at the railway station site and the exhausted lignite site showed good agreement, even by taking into account a standard transformation coefficient between compact soil and excavated waste. Terrestrial LiDAR scan alignment and point clouds/mesh cleaning activities have been very time-consuming, so that usage of automatic processing pipelines testing by mean of open source software is in progress: environmental monitoring of waste management over large areas, if properly managed with (semi) automatic processing, would be less time-consuming stating to actual testing. National projects of large processing infrastructure (‘Mirror Copernicus’) would see a leading role taken by our office in building a fully-operational prototype of a pipeline for scan alignment and point cloud/mesh processing to evaluate waste extraction in large building sites.

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description: Multiobjective analysis of open areas invaded by forest with open source software: the case of the SATURN project In northern Italian mountainous regions, forests are invading pastures and abandoned cultivated surfaces leading to an important land-use change phenomenon and reducing those open areas that are fundamental for ecological purposes [1]. The research here presented, focuses on a multiobjective and contemporary assessment methodology of two or more multicriteria analyses applied in the identification of the most suitable areas for agricultural purposes between those surfaces that have been invaded by forests carried out using Free and Open Source Software for Geospatial (FOSS4G) software. The analysis of the areas was determined by taking into account their intrinsic characteristics and their spatial location in relation to the territory and started from previous studies on land use in the Autonomous Province of Trento (Italy). The pilot areas are three municipalities that are part of Trento’s Province: the municipality of Trento - the Province’s capital, the municipality of Pergine Valsugana and seven municipalities that are part of the Piana Rotaliana region. Almost 88% of the Municipalities are located at an altitude of more than 600 m above sea level reflecting the peculiar topography of the province made up of valleys and high mountains with high percentages of steep slopes [2]. In Trento, the overall density is 742 inhabitants per square kilometers and the pressure on urban and peri-urban areas is nine times higher than the rest of the province [51]. 20% of Trento’s territory is classified as agricultural and 50% as forest or pasture land. About 70% of the territory is covered by silvopastoral -agricultural areas, the remaining 30% is categorized as urban. The repartition of the province’s surface is similar to the one of the city of Trento: 61% of the territory is covered by forests, 33.6% by agricultural areas, and only 5% by other types of land use. Collective bodies and public actors manage most of these silvopastoral -agro-forestal areas whose ownership is collective and is managed following the “uso civico” rights, a customary right embedded within the properties of communities and villages [52]. Therefore, profit is not their main aim. This study has been part of the SATURN European project [3] funded by EIT Climate-KIC (November 2018-December 2021). Three city-regions have been involved: the Trentino region in Italy, Birmingham in the United Kingdom, and Gotheborg in Sweden. The project aimed to reintegrate natural resources into cities' climate change adaptation strategies and to expand and nurture its model by creating a broader initiative involving an increasing number of stakeholders. Geospatial data set was georeferenced and managed with GRASS and QGIS and the files were collected combining data freely available at the Autonomous Province of Trento as well as self produced during the project. The comparative analysis and methodology were carried out by means of QGIS 3.8 Geographic Information System that has been used to complete the analysis in order to develop a methodology that can be widely used by territorial operators and Public Administrations. Through a series of multi-criteria analyses [4] of the agricultural and ecological vocation of a given region, and more specifically of abandoned agricultural areas, it was possible to create initial maps assigning values according to specific considered aspects. To lead these analyses, it was necessary to collect and select a significant amount of georeferenced data and then standardise them. Synthesis analyses have been useful to compare the ecological and agricultural aspects and to integrate them in synthesis maps, which can be used in the future for land management and planning. In order to validate the model and to verify the results, on-site inspections were carried out both in Valsugana and in Val d'Adige. Technicians and experts have been involved in the research through focus groups, organised within the SATURN project, which allowed some general criticalities of the territory to emerge, and through the completion of a questionnaire proposed within the thesis work. Through these questionnaires, it has been possible to identify the most important criteria for assessing a plot of land from an ecological and agricultural point of view. The obtained results showed how the classical approach, based on single criteria analysis, differ from the multicriteria approach for its potential to produce a more precise and clearer classification output of the aspects considered, showing the two multi-criteria analyses and their dependence on a single final map. Significant advantages have been taken from the use of this method in terms of data and information exchange between the stakeholders and in terms of a deepen understanding of the characteristics of the areas that have been analysed. The proposed methodology and the script that has been developed can be used in order to better plan forest management and as a basis for future territorial plans. Moreover, the multicriteria approach, which initially provides for a separate analysis of the research layers and then integrates them into a single final output, may represent a starting point for ecosystem evaluations. Preserving the ecosystem of an area, or rather the mosaic of ecosystems that make it up, is in fact of fundamental importance, as is succeeding in creating an eco-sustainable environment. In order to achieve this, it is necessary to have a spatial planning process that is as accurate as possible and that evaluates all the ecological criteria in a diversified manner with respect to the criteria of the object of research, so as to be able to identify key elements. The model presented can be replicated by changing the current research object, i.e. agricultural assessment, and keeping the ecological assessment instead. Future development will foresee the transformation of the Python script into a plug-in for Qgis, guaranteeing greater functionality for those who wish to use it.

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description: ## Introduction NoiseCapture is an Android application developed by the Gustave Eiffel University and the CNRS as part of a participatory approach to environmental noise mapping. The application is open-source and all its data are free. The study presented here is a first analysis of the first three years of data collection, through the prism of noise sources. The analysis only focused on the labels filled in by the users and not on the sound spectrum of the measurement, which will be studied later. The aim was to determine whether known dynamics in environmental acoustics could be recovered using collaborative data. This preparatory work having to be consolidated and extended thereafter, and with the will to include this study within the framework of the Open Science, an attention was brought on the reproducibility aspect of the analysis. This one was entirely realized with free software and literate programming techniques. The context of the study, the tools and techniques used and the first results obtained will be presented as well as the benefits of using literate programming in this type of preparatory work. ## Data An article presenting this dataset was published in 2021 (Picaut et al. 2021). It details the structure of the database and the data, the profile of the contributors and the contributions but does not analyze the content of the data. This is what this article proposes to begin. The data used in this study correspond to contributions made between August 29, 2017 and August 28, 2020. During this period, nearly 70,000 unique contributors allowed the collection of more than 260,000 tracks for a total of about 60 million seconds of measurement. A trace is a collected recording, it contains the sound spectrum (1 second, third octave) recorded by the phone coupled with its GPS positioning (1 second). This information can be enriched by the contributor with labels. There are 18 labels and the user can select one or more of them for each of the traces made. They are detailed in (Picaut et al. 2021). The preliminary work presented here focuses on the analysis of the proportion of certain labels in the global sample at certain temporalities. In addition to data from the collaborative collection, some additional data were used to limit the study area. We chose to limit the geographical scope of this preliminary study to metropolitan France because this area contains the largest number of recordings. The climate and sound dynamics are known and documented there. To facilitate the reproducibility of spatial filtering, it was decided to use open data sets from recognized sources: the Natural Earth database (Patterson and Kelso 2021) and the Admin Express database from the National Institute of Geographic and Forest Information (Institut Géographique National 2021). ## The study ### Tools #### PostGIS The data are provided as a dump from a PostGreSQL/PostGIS database (Ramsey and Blasby 2001). Several scripts perform much of the attribute and spatial filtering. These filterings are saved in a materialized view whose data will be analyzed with the R language. #### R The R language (R Core Team 2021) is a programming language for data processing and statistics with many libraries dedicated to geospatial data. Rmarkdown allows to mix code and text in markdown for the dynamic production of graphs, tables and documents. It is one of the recommended means for literate programming. #### Git Git is a Distributed Version Control System (DVCS) (Chacon and Straub 2014). It enables collaborative and decentralized work. The choice of Git was natural as different collaborators are present on several sites (Nantes, Lyon, Paris) and Git is already used within the UMRAE laboratory. ### Implementation The data are provided in the form of a PostGreSQL/PostGIS dump. A server has been set up and the data loaded. A materialized view was created in order to provide a stable access to the data corresponding to the defined criteria. These criteria are both attributive (filtering of certain tags, minimum and maximum durations, etc.) and spatial (located in France, reduced trace area, etc.). A Rmarkdown document establishes the connection with the view and then performs the operations allowing to analyze the data. A document mixing narrative, figures and code allowed the resumption and continuation of the analyses shown here. ## Results The study concerns tracks bearing a tag, registered in metropolitan France. It focuses on the proportion of a certain tag in relation to all the tags for a given period (time of day, season, etc.). In the sample studied, it is possible to note a prevalence of the tags _roads_, _chatting_, _animals_ and _wind_. The tags _air_traffic_ and _works_ are also well represented. A first axis of analysis concerns the time distribution of the tags. Animal noises (tag animals) are more frequent in the morning and especially one hour before sunrise. This is a common dynamic for bird song. We also observed peaks in human activity, especially commuting. The next temporal axis was the seasonality, especially those of animal noises, with a more intense activity in European spring and summer. This phenomenon could also be observed in the recordings. We also noticed that music was less present in autumn than in other seasons and that it is mostly present at late hours.

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name: Crowdsourced acoustic open data analysis with FOSS4G tools


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description: Remote sensing via orbiting satellite sensors is today a common tool to monitor numerous aspects related to the Earth surface and the atmosphere. The amount of data from imagery have increased tremendously since the past years, due to the increase in space missions and public and private agencies involved in this activity. A lot of these data are open-data, and academics and stakeholders in general can freely download and use it for any type of application. The bottle-neck is often not data availability anymore, but the processing resources and tools to analyse it. In particular multi-temporal analysis requires stacks of images thus digital space for storage and processing workflows that are tested and validated. Processing image by image is often not a viable approach anymore. Several solutions have been created to support centralized and automated processing of multiple images. Software as a service (SaaS) is becoming more common among users. The most popular to this day is probably Google Earth Engine (GEE), which gives users Petabytes of data at their fingertips, access to processing resources and an interface that provides a large number of tools for data processing via Javascript or Python programming environments (Gorelick et al., 2017). What took before days if not months can now be run in a few minutes or hours. GEE is available and free for academics as of today, but it must be noted that it is not to be taken for granted in the future. Other initiatives such as Copernicus RUS project that has closed at the end of 2021 also provided access to data (Copernicus data) and computing resources, to promote uptake of Copernicus data via educational and research activities. Moving towards SaaS solutions usually requires a provider that puts software on the cloud and a channel, usually a web portal, for accessing data and tools. The R CRAN programming environment has all the “ingredients” that are needed to create such SaaS in a local machine or on a server. We propose and discuss here a solution, called InforSAT, that was created ad hoc for centralizing satellite imagery processing, taking advantage of a remote server with multiple processors and thus also parallel processing solutions. The R Shiny package was used for connecting online widgets for user interaction with R tools for specific processing of imagery that is done via other specific packages. To this date only Sentinel-2 Level 2C data are considered, but the system is scalable to other sensors and processing levels. The tools that are available to this day are focused on multi-temporal analysis, to support the academic community involved in particular in vegetation analysis, whose phenology has notable changes inter- and intra-annually. The tools are available via a web portal to reach research teams that are not so familiar to satellite image analysis, to allow simplified extraction of multi-temporal data from Sentinel-2 images. Figure 1 shows the interface and figure 2 the result of extracting a boxplot of vegetation index values over a specific time window. All image data are stored in a user-defined folder on the server, and a script checks weekly (or at other user-defined intervals) for new Sentinel-2 images and automatically downloads them and stores metadata in an R list structure. The metadata stores image paths, bands and also histograms of values for each band, to use for defining color-stretching parameters during image rendering on the browser. Regarding visualization, users can render real-color and false-color composites defining their own band combinations, and can also create and raster layer with the values of common vegetation indices or define their own index by providing an equation on the interface (see Figure 1). The images to be rendered on the user browser are processed on-the-fly from the original JPEG2000 format, also for calculating the index raster and the color-composites. Each index raster is calculated every time the user re-draws actively the raster, by sampling the original image with points that correspond to the screen pixels, reprojected from screen coordinates to image coordinates. Depending on the screen size and on the area, these are around one million points, that are then converted to an image and rendered on screen with a fixed scale that depends on the expected minimum and maximum values of the index (e.g. for the normalized vegetation index that would be between -1 and 1) or a scale that automatically stretches between the 10th and 90th percentile of the frequency distribution of the real values. The color-composites are automatically drawn at any scale using the intrinsic overviews for each Sentinel-2 band that are present from the JPEG2000 format. Regarding multi-temporal analysis, users can define one ore more polygons over the area and for each polygon extract single pixel values (digital numbers – DN) and aggregated zonal statistics for each and all available images in a few seconds, with or without using parallel processing mode. Users can download the multi-temporal data, i.e. the DN values, in table format for further analysis. The table is in long format and has a column with a timestamp, one with polygon ID and one column for each band with values. In both visualization and multi-temporal analysis, users can decide a threshold for masking according to cloud and snow probability, which are available products from the sen2cor processing of Sentinel-2 to level 2C. In the near future this solution will be integrated in an R package, allowing users to easily download, install and replicate their own portal locally or in their own server.

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name: InforSAT: an online Sentinel-2 multi-temporal analysis toolset using R CRAN


keywords: foss4g2022 generaltrack OpenData

uk: Комп'ютерні науки

description: Geospatial datacubes--large, complex, interrelated multidimensional arrays with rich metadata--arise in analysis-ready geopspatial imagery, level 3/4 satellite products, and especially in ocean / weather / climate simulations and [re]analyses, where they can reach Petabytes in size. The scientific python community has developed a powerful stack for flexible, high-performance analytics of databcubes in the cloud. Xarray provides a core data model and API for analysis of such multidimensional array data. Combined with Zarr or TileDB for efficient storage in object stores (e.g. S3) and Dask for scaling out compute, these tools allow organizations to deploy analytics and machine learning solutions for both exploratory research and production in any cloud platform. Within the geosciences, the Pangeo open science community has advanced this architecture as the “Pangeo platform” (pangeo.io/). However, there is a major barrier preventing the community from easily transitioning to this cloud-native way of working: the difficulty of bringing existing data into the cloud in analysis-ready, cloud-optimized (ARCO) format. Typical workflows for moving data to the cloud currently consist of either bulk transfers of files into object storage (with a major performance penalty on subsequent analytics) or bespoke, case-by-case conversions to cloud optimized formats such as TileDB or Zarr. The high cost of this toil is preventing the scientific community from realizing the full benefits of cloud computing. More generally, the outputs of the toil of preparing scientific data for efficient analysis are rarely shared in an open, collaborative way. To address these challenges, we are building Pangeo Forge (pangeo-forge.org/), the first open-source cloud-native ETL (extract / transform / load) platform focused on multidimensional scientific data. Pangeo Forge consists of two main elements. An open-source python package--pangeo_forge_recipes--makes it simple for users to define “recipes” for extracting many individual files, combining them along arbitrary dimensions, and depositing ARCO datasets into object storage. These recipes can be “compiled” to run on many different distributed execution engines, including Dask, Prefect, and Apache Beam. The second element of Pangeo Forge is an orchestration backend which integrates tightly with GitHub as a continuous-integration-style service. We are using Pangeo Forge to populate a multi-petabyte-scale shared library of open-access, analysis-ready, cloud-optimized ocean, weather, and climate data spread across a global federation of public cloud storage–not a “data lake” but a “data ocean”. Inspired directly by the success of Conda Forge, we aim to leverage the enthusiasm of the open science community to turn data preparation and cleaning from a private chore into a shared, collaborative activity. By only creating ARCO datasets via version-controlled recipe feedstocks (GitHub repos), we also maintain perfect provenance tracking for all data in the library. You will leave this talk with a clear understanding of how to access this data library, craft your own Pangeo Forge recipe, and become a contributor to our growing collection of community-sourced recipes.

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name: Pangeo Forge: Crowdsourcing Open Data in the Cloud


European (Inspire) Data Tour

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description: This talk provides concrete tips on how to improve your open data accessibility and discovery. We use real world analysis of what Europe has today, rather than specifications, guidelines, or theory. We recently investigated the linkage between Metadata (CSW Dataset and Service Metadata records) and actual downloadable/viewable data (WFS, WMS, WMTS, and Atom). We also looked at other linkages between the documents (for example, metadata document links, "operatesOn" links, Inspire "ExtendedCapabilities", and other MetadataURL links). Following links isn't as simple as just taking the given URL and resolving it - we will look at "fixing" the URL as well as setting request headers. We will also investigate comparing two different metadata documents (from different URLs) to see if they are "the same" even if they aren't really equivalent. If you are responsible for an INSPIRE catalogue or web service, attend this talk to learn what works (and does not work) based on real world analysis rather than theory. Or just attend to be sure you did not show up in the examples.

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Classroom GIS in South Africa

keywords: foss4g2022 generaltrack Education

uk: Комп'ютерні науки

description: The introduction of technology in the education sphere has brought about improvement regarding the quality of education young and old individuals receive. A country’s development plays a huge factor in the quality of services its people receive, therefore not every country will receive the same quality of services. Classroom GIS has changed how Geography as an academic subject is taught. It has sparked interest in the practical component of the subject and gives more understanding to the strong relationship between theory and map work. This leads to the concept of spatial thinking and how it has allowed geography educators around the world, some without basic GIS education, to see the importance of including more GIS concepts in the high school geography curriculum. Several GIS software packages are available that educators can use to teach their students. But taking into account the availability of resources when focusing on the African continent, it is probable that free software and hardware plays a key role in the development of GIS concepts being included in the geography curriculum. It is affordable, and learning resources are readily available, in terms of tutorials, documentation and more. “This inclination towards GIS textbook lecturing has largely jeopardized the quality of GIS education”. - (Fleischmann and van der Westhuizen, 2020 found in The Journal of Geography Education in Africa) Advocacy to include GIS practices and strategies in geography education across Africa have been documented, but has not received the necessary exposure it needs from its governments. The majority of GIS teaching has been textbook-based, making the introduction of GIS technology and education a frightening phase that educators may not want to engage in. To overcome the fears behind understanding and grasping basic GIS concepts in the classroom, interactive GIS tutorials may help to remove these fears and make the adoption of GIS simple, especially within countries where service delivery (education services) is poor. QGIS for example is an open-source GIS software that has been around for 20 years, it has shown tremendous improvements and upgrades throughout its 20 years. Its user-friendly capabilities have improved, making it ideal to introduce more geography educators and learners to the software. It has tutorials and material that is suited for individuals from different walks of the profession. The tutorials are interactive and allow first-time users to readily engage with the material. For both learner and educator to understand the material and not get overwhelmed. The key factors to understand are; GIS can be implemented into the African geography school curriculum, open-source software is key to overcoming limitations such as lack of resources and geography educators are willing to take on GIS with sufficient training. More urgent research is needed on reliable and sustainable methods and practices of teaching GIS in a secondary school classroom.

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keywords: foss4g2022 generaltrack Stateofsoftware

uk: Комп'ютерні науки

description: At the beginning of 2022, HOT_tech started a collaboration with Kathmandu Living Labs on the Tasking Manager. This followed our ambition to facilitate an open collaborative process in building and improving our open source technology by forming a ‘collective’. The idea of a ‘collective’ is to bring people with shared purpose together on shared ground to achieve a shared goal. A lot to be shared! In this context the shared goal is the development of the Tasking Manager. Our vision is for creating with, for, and by the community for making the product more impact-driven and user friendly. The Tasking Manager has proven itself over the years to be not just a software but a platform to bring the different individuals, communities, organizations who share a common goal towards not only humanitarian effort and crisis response but identifying local resources and needs through mapping and data. So whether you are a mapper, a validator, a designer or an open source developer with an interest in the Tasking Manager, you can join the collective. During this talk we will share our journey in building the collective. You will hear an open and honest reflection on what worked, what didn’t work, what we have learned and what we hope to do going forward. We want this talk at FOSS4G to open a conversation with other open source and geospatial communities on best ways for designing, creating and implementing open, diverse and inclusive spaces for thriving and healthy collectives and communities.

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name: Open Tech Collective: sharing HOT's journey


uk: Комп'ютерні науки

description: The GeoNode, according to the project's website, is a platform for managing and publishing geospatial data. It brings together mature and stable open source software projects into a consistent, easy-to-use interface, allowing non-specialist users to share data and create interactive maps. In Brazil there is a growing use of GeoNode, observed mainly in governmental institutions and universities. One of the main ways of installing and configuring GeoNode is the so-called Geonode Project. It consists of a custom Django Project template, which contains, in addition to the main project files, a set of Dockerfiles of GeoNode components, such as GeoServer, Nginx (reverse proxy) and PostGIS. From a detailed analysis of the components of the GeoNode Project created, it was found that the original dockerfiles contain a series of security holes and also unnecessary packages for the execution of the stack, not recommended for production environments. A Dockerfile that follows best practices eliminates the need to run privileged containers (as root), the use of unnecessary packages, leaked credentials, like mail passwords or database DSNs, or anything that could be used for an attack. Removing known risks in advance will reduce security management work and service overhead. The objective of this talk corresponds to discuss the possible security holes found in the Geonode Project and, with the application of best practices in Dockerfiles, to make it leaner and safer for production environments. For demonstration purposes, there will have a project to be used as an example and will be hosted at GitHub: geonode-br/hardening-geonode-docker.

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name: Hardening a GeoNode Project – Some considerations about container security and optimization


uk: Комп'ютерні науки

description: UN Maps is a program led by the United Nations Department of Operational Support in support of several peacekeeping and political missions such as UNSOS, MONUSCO, MINUSCA, MINUSMA and UNISFA. By leveraging internal and crowdsourcing capabilities, UN Maps aims not only to enrich topographic and operational data in UN mission areas but also to provide peacekeeping and humanitarian actors with topographic maps, operational geo-information, search and navigation tools, and imagery and street-level base maps, leveraging OpenStreetMap, the Wikipedia of maps. In order to achieve its goals, the UN Maps Initiative is building a thriving community around the collection, validation, usage, and dissemination of open geospatial data. This community is called UN Mappers. It benefits from the established crowdsourcing activities, such as mapathons, training opportunities and other collaborative events involving several stakeholders as the UN staff on the field (peacekeeping and agencies, funds and programs), academia (high schools and universities in Africa, EU and US), local communities and remote volunteers. Together, the UN Mappers community give substantial support not only in the production of maps and web services but also in the development of innovative applications using virtual reality and data analytics. Some of the results obtained with open data to these applications will be presented during the intervention. Furthermore UN Mappers are working on translating and updating OSM documentation material in all 6 UN official languages, which is distributed with open license.

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name: UN Maps: OpenStreetMap supporting Peace and serving Humanity


uk: Комп'ютерні науки

description: The National Land Survey (NLS) of Finland maintains a registry of aerial imagery in Finland containing metadata of imagery since 1932. As part of the NLS' strategy of moving towards FOSS software, a novel registry management tool is being developed as a QGIS plugin. This talk describes the process of designing and implementing the new registry management software and explores the suitability of QGIS as a platform for creating highly customised spatial data management tools. While the registry management tool is developed for QGIS, the registry is migrated from Oracle to a PostGIS database, following a redesign of the data model. In 2020, the NLS announced it aims to build its technological environment based on open source technologies. As a result, there is an ongoing effort of re-designing and implementing various existing processes and systems using open-source technologies. One such system is the national Aerial Image Registry. The registry is managed by a group of NLS employees and metadata is used in various workflows for publishing new data products from captured images and planning new aerial imaging missions. The current registry management software is a technically dated solution based on Visual Basic 6 and an Oracle database. Key features of the new registry management tool include the ability to query the image registry and show the search results on map, editing and archiving existing data in the registry, importing new data to the registry, creating data extracts to PDF maps and spatial formats, and validating plans for aerial imaging missions. QGIS provides an user-friendly platform that is already familiar to many GIS experts that can be easily extended with plugins that provide custom functionality and features. The NLS has prior experience of designing and developing tailored QGIS plugins to support their unique workflows, including plugins for maintaining the topographic database of Finland and the national point cloud registry. These projects have been well received by users and developers alike, and the positive feedback has encouraged the NLS to continue developing tailored QGIS plugins for its specific needs.

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name: Maintaining a national Aerial Image Registry with QGIS


Mapping impacts of Covid-19 in Nairobi

uk: Комп'ютерні науки

description: The Covid-19 pandemic has had many impacts beyond health - economic, social, etc. The Cities Covid Mitigation and Mapping (C2M2) project, from the US Department of State's MapGive initiative, sought to map and help direct policy around these secondary impacts of Covid in several countries globally. Map Kibera and GroundTruth Initiative worked to track these impacts in Nairobi, focusing on the themes of education, water and sanitation. This talk will present the outcomes of the project, which focused on the mapping in OSM of schools, water points, and toilet facilities in the informal settlements of Kibera and Mathare. These updates to existing OSM data help show how the pandemic affected these sectors by looking historically at changes. Additionally, individual surveys about access to water during shortages and impacts of school closings and disruptions help paint a picture of how Nairobi's lower income residents have been particularly impacted by the pandemic. There is also a strong gender component to the impacts which will be highlighted. The project used a combination of tools, which will also be presented: Kobo Toolbox for mapping and individual survey collection, OSM for map data, and data analysis in QGIS. The Kenya team was supported by many other team members from the C2M2 project for data analysis. Additionally, participants in Africa included Bukavu in the DRC and Pemba in Mozambique; we will briefly share their map outcomes as well. The "Africa Hub" which included Nairobi, Pemba and Bukavu showed that across the continent, economic and social impacts of Covid-19 on vulnerable groups were particularly challenging.

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uk: Комп'ютерні науки

description: Sea water turbidity analysis from Sentinel-2 images: atmospheric correction and bands correlation Sea water turbidity is a measure of the amount of light scattered by particles in water. It is due to the presence of suspended particles, which it is operationally defined as the fraction in water with less than 2 µm in diameter. Plankton can also generate turbidity, but high turbidity events are dominated by high concentrations of inanimate inorganic particles. High levels of suspended sediments in coastal regions can occur as consequence of high sediment load from rivers, from bottom sediment resuspension due to wave actions or due to anthropogenic activities, such as dredging operations or bottom resuspension from ship propellants. The increase of turbidity can determine negative environmental effects both on the biotic and abiotic marine ecosystem. In highly anthropized coastal marine systems, like harbours, sediments represent a sink for contaminants and resuspension can contribute to propagate pollution to unpolluted areas (Lisi et al., 2019). Many marine water quality monitoring programmes measure turbidity. Traditional methods (e.g., in situ monitoring) offer high accuracy but provide sparse information in space and time. Earth Observation (EO) techniques, on the other hand, have a potential to provide a comprehensive, fast and inexpensive monitoring system to observe the biophysical and biochemical conditions of water bodies (Caballero et al., 2018; Saberioon et al., 2020; Sagan et al., 2020). Hence, some of the authors are developing a semi-empirical model for predicting water turbidity by combining Sentinel-2A data and machine learning methods using samples collected along the North Tyrrhenian Sea (Italy). Field data collected at the study site from April 2015 to December 2020 were made available by ARPAL, even though most of these data refer to low turbidity events. In the framework of this research activity, Sentinel-2A multispectral optical images, freely available within the EU Copernicus programme, are elaborated. It’s well known that such products are provided at Level-1C (L1C) Top of Atmosphere (TOA) and at Level-2A (L2A) Bottom-Of-Atmosphere (BOA). L2A BOA reflectance products are preferred as they are already corrected for effects of the atmosphere. However, the official L2A data are available for wider Europe from March 2018 onwards. The necessity to use the complete on-site dataset to calibrate the predicting model, and not only data after March 2018, required the identification of the most appropriate algorithm for atmospheric correction of L1C images relative to study area between 2015 and 2018. Hence, a comparison between the available L2A BOA product ant the corresponding L1C image corrected in different open source environment was performed. In particular, the free and open source QGIS and GRASS GIS, and the Sentinel Application Platform (SNAP), provided by ESA/ESRIN free of charge to the Earth Observation Community, published under the GPL license and with its sources code available on GitHub, were used. Both image-based method, i.e. the Dark Object Subtraction (DOS) method in QGIS, and physically-based methods, i.e. the Second Simulation of Satellite Signal in the Solar Spectrum (6S) method in i.atcorr module of GRASS GIS and the Sen2Cor algorithm inside SNAP, were applied (Lantzanakis et al., 2017). The great advantage of the DOS method is that it focuses only on the spectral and radiometric characteristics of the processed image, hence it doesn’t require remote or in-situ atmospheric measurements. But the performed correction doesn’t seem so accurate. Instead, the physically-based approach requires atmospheric measurements and parameters, that are difficult to be identified so to be coherent in space and time with the processed image. The most complex physical parameter to set is Aerosol Optical Depth (AOD), which is a dimensionless parameter related to the amount of aerosol in the vertical column of the atmosphere over the target station. It usually range from 0 to 1, with values less than 0,1 that corresponds to a clean atmosphere with high visibility, and values higher than 0,4 that corresponds to hazy atmosphere with very less visibility. AOD is spatially and temporally very variable. It can be estimated from AERONET (AErosol RObotic NETwork), a federation of ground-based remote sensing aerosol networks with more than 25 years of data. A station which measured the Aerosol Optical Depth at 500 nm at Level 2 (quality-assured) at the same time as the scene was taken, is not always available nearby the site under study. Hence the evaluation of AOD variability in time and space was analysed for the area and the events of interest, so to identify the proper values. Expecially i.atcorr seems very sensitive to the set values of AOD. Once the proper method for atmospheric correction was identify, it was applied to the L1C images relative to the collected field data from April 2015 to March 2018. Then, the correlation between the in-site dataset and the individual bands known to be most sensitive to water turbidity, i.e. blue (B2), green (B3), red (B4) and near infrared (B8 and B8A) bands, was analysed, finding good results for the visible bands, and a weak correlation with NIR bands. In addition, indexes defined by the ratio between the three visible bands were checked to see which combination could best highlight the turbidity of the water from the Sentinel-2 images. Preliminary results seem to confirm that the identified EO technique could provide a fast and inexpensive monitoring system to observe sea water turbidity along the Northern Tyrrhenian Sea (Italy).

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