uk: Комп'ютерні науки

description: "Kivy makes Pythonistas happier. Why? Cause with Kivy you’ll drop any non-pythonic way to develop mobile and desktop apps, or it’ll help you to start a new career in app development, with Python. We will talk about GUI apps development with Kivy while keeping a focus on all the tools in the Kivy ecosystem which are making it possible to create, build and distribute fully-featured apps on all the supported platforms (Android, iOS, Linux, macOS, and Windows). After the talk, you’ll know how to: – Persuade your boss (or yourself) to drop non-pythonic ways to develop Apps – Access Java classes from Python (yeah, sometimes it happens) – Access Objective-C classes from Python (is not that bad, but we love Python) – Package Kivy apps for Android, iOS, Linux, macOS, and Windows. – Architect Python cross-platform apps, and the mistakes to avoid – Drive the future of Python on mobile platforms." This talk will go through some common pitfalls of running machine learning in production settings. We will start with the requirements and work through the data acquisition and model-building phase. We explore beyond the current MLOps hype and try to understand what it takes to run a successful project that is ready to ripe like a fine wine rather than old milk. Before being a Software Developer, I have been (not so secretly) tech-addicted, especially when it came to computers. Now, a few years later, during the week I code everything (more happily if it comes to Python), and during weekends I help to maintain Kivy as I’m proudly part of the Kivy Core Developers team. When I’m not in front of my laptop screen, you can find me traveling (hopefully more, in future). I love to listen to EDM music while stuck in traffic or during open-air festivals." Talk recorded at the Swiss Python Summit on September 21nd, 2023. Licensed as Creative Commons Attribution 4.0 International.

uk: вхід без авторизації

name: Kivy: Cross-platform App development for Pythonistas


Pavel Sulimov

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: "With the increasing popularity of large machine learning models capable of solving complicated tasks in the sphere of natural language processing, computer vision, etc., the need for distributed computation has rocketed significantly. We would like to provide the "surgery" of parallelization methods from one of the most popular deep learning frameworks - PyTorch. Particularly, we would like to demonstrate two main approaches: data parallelization (when the single module is trained asynchronically in streams) and model parallelization (both horizontal – with several models trained simultaneously, and vertical – when the model parameters are split into groups). Moreover, we will guide through the cases of different resources availability, i.e. what could be done when having only CPUs, a single GPU, or multiple GPUs. Our showing is to be done on an example of urban planning problem solution, where we are creating synthetic cities with deep convolutional generative adversarial neural networks. These models have complicated architecture and billions of parameters when generating images starting from mid-resolution like 256x256, which makes them perfect instances for distributed computation demonstration.

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: Asynchronous Multiprocess Large Model Training on PyTorch

Swiss Python Summit Association

type: Organization

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


uk: Комп'ютерні науки

description: Python is currently a powerhouse when it comes to web development. But, how did it all start? We explore Python's humble web development beginnings, from CGI to WSGI and the eventual rise of large frameworks like Django, and smaller ones like Flask and FastAPI.

uk: вхід без авторизації

name: A Short History of Python Web Frameworks


uk: Комп'ютерні науки

description: "In an era where autonomous robots, such as Boston Dynamics' quadrupedal robot, Spot, are capable of navigating complex environments, it is crucial to ensure the safety of an operator. Traditional control mechanisms, such as a remote control, may not be feasible or safe in harsh or hazardous conditions. Addressing this, we present a novel Python-based voice control module for Spot. Our module enables hands-free operation of the robot, allowing it to execute verbally issued commands. To enhance the interaction between an operator and a robot, we've integrated an additional text-to-speech synthesizer, establishing a two-way communication channel. Our solution leverages state-of-the-art Python libraries for speech-to-text translation and lightweight command extraction, which significantly extends the possibilities of interaction. As a result, Spot can perform basic tasks such as standing up or navigating to specific coordinates using only voice commands. This novel approach, promotes safety and efficiency in operating autonomous robots, opening up new possibilities for their use in challenging environments.

uk: вхід без авторизації

name: Voice Control in Action: Operating a Quadrupedal Robot


A walk with CPython

uk: Комп'ютерні науки

description: "Did you know that Python has a compiler even though it’s an interpreted language? In this talk, we will embark on a step-by-step exploration of a simple program, unraveling the inner workings of CPython—the default reference implementation of Python. We’ll begin with the compiler, which performs the task of converting Python code into OPCODES. Next, we’ll explore the famous interpreter. We’ll uncover how it works with the generated OPCODES, executing the program line by line and talk about an example of optimisations it does along the way. We’ll explore how Python manages variables, function calls, and exceptions. Additionally, we’ll touch upon object creation and destruction. The primary aim of this talk is to provide a concise yet comprehensive overview of the components involved in executing a simple program within CPython. Through precise references to the CPython code base, attendees will be equipped to explore further on their own."

uk: вхід без авторизації


Did you know Matplotlib could do that?

uk: Комп'ютерні науки

description: "In this talk, I am going to expand on my NormConf Lightning Talk “How to stop crying when using Matplotlib”. Matplotlib is my tool of choice for custom data visualizations and I have been teaching it for the last 2 years in a dedicated course at HSLU. It is an extremely popular data visualization library among the Python data science community and often the only one that can produce fully customized, complex visualizations. However, due to its long history, API changes and lack of good educational resources, many people struggle to harness all its capabilities, ending up frustrated, dissatisfied and with an ugly chart as an output. I’m going to explain why Matplotlib works the way it works and how to work with it instead of against it. I will also show some tips and tricks for writing sustainable code and and share a few recipes for making beautiful, complex data visualizations. Licensed as Creative Commons Attribution 4.0 International.

uk: вхід без авторизації


OER4SDI - Vektordatenanalyse (EGI 5.1)

keywords: Geoinformatik ArcGIS GIS Karten Maps Kartografie Layout

uk: Комп'ютерні науки

description: Dieses Video ist im Rahmen des Projektes OER4SDI gefördert von ORCA.NRW entstanden und beschäftigt sich damit eine umfassende Standortanalyse in ArcGIS Pro durchzuführen. Dabei werden verschiedene Werkzeuge der Vektordatenanalyse angewendet, um die Wohnqualität in einem Gebiet zu bewerten. Die Analyse umfasst die Bewertung der Lärmbelastung durch Autobahnen, die Nähe zu Naturschutzgebieten und die Größe der Gebäude.

@language: de

uk: вхід без авторизації

Hochschule Bochum

addressCountry: DE

addressLocality: Bochum

addressRegion: North Rhine-Westphalia


uk: Комп'ютерні науки

description: Dieses Video ist im Rahmen des Projektes OER4SDI gefördert von ORCA.NRW entstanden und beschäftigt sich mit: - dem erkunden und Interpretieren von SAR-Satellitenbildern - dem ändern der Symbolisierung von Bilddaten - dem hervorheben interessanter Features - dem anwenden von Werkzeugen zur Rasterdatenanalyse - dem erstellen von Farbkompositionsbildern

uk: вхід без авторизації

name: OER4SDI - Satellitenbildanalyse (EGI 6.1)


OER4SDI - Rasterwerkzeuge (EGI 6.2)

uk: Комп'ютерні науки

description: Dieses Video ist im Rahmen des Projektes OER4SDI gefördert von ORCA.NRW entstanden und beschäftigt sich mit: - dem erstellen und interpretieren eines digitalen Geländemodells (DGM) - dem anwenden von Werkzeugen zur Rasterdatenanalyse - dem erzeugen von 3D-Ansichten und bearbeiten von Rasterdaten - dem durchführen von Map Algebra zur Analyse spezifischer Bedingungen

uk: вхід без авторизації


Створення мудборду

uk: Комп'ютерні науки

@language: uk

uk: вхід без авторизації

Lviv Polytechnic National University

addressCountry: UA

addressLocality: Lviv

addressRegion: Lviv

name: Projekt 'Open Education Resources with Ukraine'


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Основні поняття інтерфейсів користувача та засоби їх проектування


Методи евристичного аналізу

uk: Комп'ютерні науки

uk: вхід без авторизації


Багатокритерійна оптимізація

cs: Matematika

de: Mathematik

fi: matematiikka

uk: Математика

en: Mathematics

fr: Mathématiques

da: Matematik

es: Matemáticas

nl: Wiskunde

de: Mathematik, Naturwissenschaften

uk: Математика, природничі науки

en: Mathematics, Natural Sciences

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Understanding Pathogen Spread Through Big Data

name: Eidgenössische Technische Hochschule Zürich (ETH Zürich)

addressCountry: CH

addressLocality: Zurich

addressRegion: Zurich


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Задачі близькості. Метод пошуку Найближчої Пари.

name: Taras Shevchenko National University of Kyiv


Метод деформованих зірок.

uk: Комп'ютерні науки

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Історія розвитку та напрямки обчислювального інтелекту


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Нечітке логічне виведення. Нечіткі нейромережі.


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Поняття множини. Операції над множинами. Закони алгебри множин


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Декартовий добуток. Відношення. Способи задання бінарних відношень.


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Властивості бінарних відношень. Відношення еквівалентності. Відношення часткового та строгого порядку.


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Функціональні відношення. Типи відображень. Добуток відображень. Обернене відображення


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Основні комбінаторні конфігурації без повторень та з повтореннями. Біном Ньютона. Поліноміальна теорема.


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Числення висловлювань. Закони логіки висловлювань. Нормальні форми логіки висловлювань. Логічне виведення в логіці висловлювань.


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Булеві функції. Алгебра Буля та Жегалкіна.


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Двоїсті функції. Принцип двоїстості. Спеціальні форми подання булевих функцій.


uk: Комп'ютерні науки

description: Photogrammetric processing and fruition of products in open-source environment applied to the case study of the Archaeological Park of Pompeii The geomatic strategy for the survey campaign, data processing and product fruition in an archaeological context is presented and discussed. The case study is the Domus V situated in the Archaeological Park of Pompeii (Regio VII, Insula 14), which was surveyed in September 2020 by the Geomatics Laboratory of Genoa University in collaboration with the archaeologist group of the same University, under the ministerial concession DG 553 Class 34.31.07/246.7 of 26 January 2016 and its renewal on 9 April 2019 (34.31.07/3.4.7/2018). The survey campaign involved the following integrated geomatic techniques: - UAV photogrammetry, performed with DJI Mavic 2 Pro. The shooting geometry was nadiral with two different altitudes of 40 m and 15 m. An additional survey with a tilting angle of 45° at a flight altitude of 15 m was performed along concentric paths around the site. The UAV dataset is composed of 1400 images. The photogrammetric surveys are framed thanks to temporary Ground Control Points (GCPs), surveyed with GNSS in Network Real Time Kinematic (NRTK) positioning strategy. - Terrestrial photogrammetry, 7000 images of the internal vertical walls were taken with a Canon Eos 40D camera at a shooting distance of about 2 m following a bottom-to-top trajectory. - Terrestrial laser scanning, using the Z+F 5006h phase difference instrument. The integrated survey allowed to move from a general view of the entire site to an increasingly detailed one, mainly aimed at the vertical walls, thanks to the global framing provided by the UAV survey. The UAV and terrestrial photogrammetry campaigns were processed through the open-source software MicMac [1] to create the dense point clouds, and CloudCompare [2] to align the different blocks. MicMac was chosen for its open-sourceness and its rigorousness in the photogrammetric processing, both related to the estimation of the external/internal orientation parameters and the dense matching to obtain the 3D point clouds from the images, that is based on a multi-scale, multi-resolution pyramidal approach that minimizes the outliers and the noise. Due to the not linear computational time in respect of the number of images, the MicMac processing was split in blocks of 500 images each (about 24 hours of processing time), with 100 overlapping images between two consecutive blocks, to align them through a point-to-point strategy. The obtained 3D point cloud was oriented and scaled using 15 natural points found on the terrestrial laser scanner point cloud, obtaining deviations on points positions ranging between 1 and 2 cm. The quality of the alignment was tested computing the distance between the laser scanner and the photogrammetric point clouds using CloudCompare M3C2 algorithm [3] on a representative area of 1.60 m × 2.25 m of the fresco on the central wall of the surveyed room, obtaining distances of ± 5 mm orthogonally to the wall. Moreover, the software MAGO [4], developed in C++ environment within the Geomatics Laboratory, was used to produce high-resolution orthophotos of vertical walls. MAGO exploits a step-by-step self-adaptive mesh that fits the dense point clouds considering a triangular plane area, where the image pixel is projected at its original resolution via the collinearity equations. The needed inputs are the image(s) to be orthorectified, the external and internal orientation parameters, the user-defined orthophoto plane and the output orthophoto resolution. MAGO was recently updated to generate orthophotos of non-coplanar adjacent walls, i.e., forming an edge between them, through a rotation so that the two walls are in a continuous common plane. The orthophotos were made accessible and viewable via a QGIS [5] project built so to manage two different reference frames, i.e, the traditional planimetric plane (X,Y) and the vertical plane of the walls (X-Y,Z), where the X-Y represent the planimetric coordinates along the wall direction. This allows to introduce the third dimension in the typical GIS representation, thus realizing a 3D GIS environment. The QGIS project is organized with a “master-slave” architecture, where the master project is dedicated to the (X,Y) plane and reports the vectorial geometries (lines) representing the perimeter of the walls, whereas a different slave project is dedicated to each specific wall with the corresponding orthophoto in a (X-Y,Z) plane. Each slave project is connected to the master thanks to a QGIS action that opens it when clicking on the corresponding wall in the master project. In each sub-project, the orthophoto of the wall is displayed together with three default shapefiles: point, line and polygon shapefile, respectively. The attribute tables of the three shapefiles are set to automatically be updated with the following information once the user introduces a new geometry: - point shapefile: the image coordinates (x, y) in pixel units and in the corresponding object coordinates (E, N, Z), where E and N represent the east and north coordinates in ETRF2000-2008.0/UTM33N reference frame and Z is the height of the point on the wall; - line shapefile: length of the drawn line in meters; - polygon shapefile: length of the perimeter and polygon surface, in meters and square meters, respectively.

uk: вхід без авторизації

name: Open Source Geospatial Foundation (OSGeo)


uk: Комп'ютерні науки

description: Landslide susceptibility assessment: soil moisture monitoring data processed by an automatic procedure in GIS for 3D description of the soil shear strength Slope stability is strongly influenced by soil hydraulic conditions, affected by the meteoric events to which the site is subject. With particular reference to shallow landslides triggered by rainfalls, the stability conditions can be influenced by the propagation of the saturation front inside the unsaturated zone. The soil shear strength varies in the vadose zone depending on the type of soil and the variations of soil moisture. In general, monitoring of the unsaturated zone can be done by measuring suction and/or water content. The measurement of the volumetric water content can be performed using low-cost instrumentation, such as the Waterscout SM100 capacitive sensors (Spectrum Tec.), distributed over the study areas. Such sensors provide data in near-real time and are relatively easy to install and replace. However, it is essential to perform a site-specific calibration of the instrumentation, since previous work (Bovolenta et al. 2020) has shown that the factory settings lead to a general overestimation of the actual volumetric soil water content. Therefore, following a sampling of the analyzed soil and a specific laboratory procedure, it is necessary to define the calibration curve that allows the transition from raw data, meant as the ratio between sensor output voltage and input voltage, to soil water content. Then, the knowledge of soil water content allows the estimation of the suction parameter, thanks to a Water Retention Curve (WRC), and consequently the definition of the soil shear strength in partly saturated conditions. Several methodologies for landslide susceptibility assessment, based on global Limit Equilibrium (LEM) or Finite Element (FEM) methods, need the soil shear strength description in order to evaluate the slope stability conditions. Both in the recent literature (Escobar-Wolf et al. 2020, Moresi et al. 2020) and in the GRASS GIS software (r.shalstab), models are already proposed for shallow landslide susceptibility estimation in GIS, based mainly on LEM. However, these models do not usually consider the unsaturated soil behaviour, but at most take into account the strength contribution provided by the vegetation root systems. The present contribution describes the implementation of an automatic procedure in GRASS GIS that, starting from monitoring data related to the soil volumetric water content, provides a 3D description of the soil shear strength in the vadose zone, that is essential for the subsequent landslide susceptibility assessment, especially in the case of shallow landslides. Soil moisture sensors data come from five monitoring networks that were set up between 2019 and 2021 in the framework of the Interreg Alcotra AD-VITAM project. Each network was organized into measurement nodes (from three to five) instrumented with four soil moisture sensors each and communicating via radio with a receiver. The receiver was then connected to a modem for remote data transmission. The four sensors in each node have been placed in the soil at four different depths (-15, -35, -55, -85 cm from the ground level). The monitoring systems allow to obtain data with a minimum frequency of 5 minutes, in .csv format so that can feed a geodatabase. Starting from a properly storing of data recorded by the monitoring network in a geodatabase, at the moment within GRASS GIS but in the near future in PostGIS, the equation of the site-specific sensor calibration, defined in laboratory, and the equation of the WRC are implemented in a procedure that allows to pass automatically from the raw sensor data to the soil water content, and then to the evaluate the suction parameter. Hence, the soil strength can be estimated for each depth at which a soil moisture sensor is installed. Moreover, since the study area is often in the order of few square kilometers, the information must be spatialized over the entire area of interest, through appropriate techniques of interpolation and extrapolation. This procedure could be integrated into a LEM or FEM, including the above cited, taking advantage of the soil moisture measurements to improve the evaluation of the stability conditions over time, by analysing the evolution of the saturation front according to the weather conditions. The authors, in particular, will integrate it into a system called LAMP (LAndslide Monitoring and Predicting), which has been under development for several years through the implementation in a GIS environment of an Integrated Hydrological-Geotechnical (IHG) 3D model for the assessment of landslide risk triggered by measured or forecasted precipitation. The integration of this procedure in LAMP will allow to obtain a simple but effective modelling for the assessment of susceptibility to shallow landslides, too.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Processing Earth observation data modeled in a time-series of raster format is critical to solving some of the most complex problems in geospatial science ranging from climate change to public health. Researchers are increasingly working with these large raster datasets that are often terabytes in size. At this scale, traditional GIS methods may fail to handle this processing and new approaches are needed to analyze these datasets. The objective of this work is to develop methods to interactively analyze big raster datasets with the goal of most efficiently extracting vector data over specific time periods from any set of raster data. In this paper, we describe RINX (Raster INformation eXtraction) which is an end-to-end solution for automatic extraction of information from large rasters datasets. RINX heavily utilizes open source geospatial techniques for information extraction. It also complements the traditional approaches with state-of-the-art high-performance computing techniques. This paper will discuss details of achieving this big temporal data extraction including methods used, code developed, processing time statistics, project conclusions, and next steps. The input for RINX is a set of rasters from which the information has to be extracted and a set of data point locations for which the information needs to be extracted. The output for RINX is a structured representation of extracted information from the raster datasets for each data point in CSV text format. The loading and pre-processing of the input datasets to RINX is accomplished using a combination of Bash and SQL scripting techniques for automation. This pre-processed input is then fed into the open source spatial database PostGIS to extract the required information by using multiple spatial techniques. Finally, the extracted output is post-processed for deduplication and standardization of extracted information for research use. RINX is designed in a way that makes it easy to deploy and scale on any local, cloud, or cluster computing platform. RINX was created to aid the study of environmental conditions and how they affect the health of people over their lifespans. This involves calculating exposures such as air pollution, humidity, precipitation, temperature, and other exposures at cohort member address locations over time. For initial work with one cohort, daily precipitation, temperature, and humidity estimates were needed for 4,796 cohort address locations for a 19 year time period, 1999 – 2017. The 800-meter resolution PRISM Spatial Climate Dataset for the Conterminous United States was used as the input for this data extraction. PRISM refers to Parameter-elevation Relationships on Independent Slopes Model, created by the PRISM Climate Group, Oregon State University. The PRISM dataset is published in .BIL raster format, with one raster representing one climate variable per day for the time period 1981 - 2020. The total size of the dataset is around 8 TB with over 100,000 rasters of size 85 MB each. For work on the initial cohort, RINX enabled the extraction of 7 key climate variables: precipitation, temperature (maximum, minimum, mean), dew point temperature (mean), and vapor pressure deficit (minimum, maximum) for 19 years of data from 48,500 800-meter resolution rasters for 4,796 data points. This resulted in a total of 10.3 Million “patient-day” calculations creating a total of 72.1M observations. Additionally, absolute and relative humidity were calculated using the existing mean temperature and dewpoint variables. RINX provided a unified solution of 9 climate variables for all persons/days for the entire dataset. It was deployed and scaled on multiple servers on a high-performance computing cluster. Our initial results reveal that it is extremely fast and efficient in processing large raster datasets. It took 1 day to load and 4 days to process and extract 7 climate variables from 48,500 rasters for the 72.1M observations at 4,796 locations. RINX enabled the researchers to analyze this big climate dataset at a fine-grained address level with high efficiency and speed. Once the scripts were written, tested, and fine tuned, processing time was reduced from months to days compared to traditional methods, resulting in substantial time savings. We are currently testing RINX on a much larger dataset of 100,000 input point locations for a time period of 1981 - 2020, spanning the full range of the PRISM 800m data. This climate data is only available for purchase, however the PRISM Climate Group has made a version of this data available for free at a resolution of 4 kilometers. To make our solution entirely repeatable with open source software, code, and data, we will use RINX to extract point location data from the freely available 4km PRISM data. Results from these analyses will be presented as part of this paper. Our solution is based on open source technology, using PostGIS that can be deployed on local or cluster computing environments. It provides an efficient way to solve geospatial big data problems, particularly those involving large temporal raster datasets where point location data extraction is desired. Big data is changing the ways data is managed and analyzed. The next generation GIS tools can help researchers process big data at scale. RINX is an end-to-end data extraction and processing solution for large raster datasets. RINX is open-source and will be shared on Github. It can be easily deployed and scaled on any local, cloud, or cluster computing environment. We used RINX for processing on a large number of PRISM climate datasets, however our solution could be applied to any temporal raster data such as NDVI, night lights, and more.

uk: вхід без авторизації

name: RINX: A Solution for Information Extraction from Big Raster Datasets


uk: Комп'ютерні науки

description: Exploring jittering and routing options for converting origin-destination data into route networks: towards accurate estimates of movement at the street level Introduction Origin-destination (OD) datasets provide information on aggregate travel patterns between zones and geographic entities. OD datasets are ‘implicitly geographic’, containing identification codes of the geographic objects from which trips start and end. A common approach to converting OD datasets to geographic entities, for example represented using the simple features standard (Open Geospatial Consortium Inc 2011) and saved in file formats such as GeoPackage and GeoJSON, is to represent each OD record as a straight line between zone centroids. This approach to representing OD datasets on the map has been since at least the 1950s (Boyce and Williams 2015) and is still in use today (e.g. Rae 2009). Beyond simply visualising aggregate travel patterns, centroid-based geographic desire lines are also used as the basis of many transport modelling processes. The following steps can be used to convert OD datasets into route networks, in a process that can generate nationally scalable results (Morgan and Lovelace 2020): ``` OD data converted into centroid-based geographic desire lines Calculation of routes for each desire line, with start and end points at zone centroids Aggregation of routes into route networks, with values on each segment representing the total amount of travel (‘flow’) on that part of the network, using functions such as overline() in the open source R package stplanr (Lovelace and Ellison 2018)``` This approach is tried and tested. The OD -＞ desire line -＞ route -＞ route network processing pipeline forms the basis of the route network results in the Propensity to Cycle Tool, an open source and publicly available map-based web application for informing strategic cycle network investment, ‘visioning’ and prioritisation (Lovelace et al. 2017; Goodman et al. 2019). However, the approach has some key limitations: ``` Flows are concentrated on transport network segments leading to zone centroids, creating distortions in the results and preventing the simulation of the diffuse networks that are particularly important for walking and cycling The results are highly dependent on the size and shape of geographic zones used to define OD data The approach is inflexible, providing few options to people who want to use valuable OD datasets in different ways``` To overcome these limitations we developed a ‘jittering’ approach to conversion of OD datasets to desire lines that randomly samples points within each zone (Lovelace, Félix, and Carlino Under Review). While that paper discussed the conceptual development of the approach, it omitted key details on its implementation in open source software. In this paper we outline the implementation of jittering and demonstrate how a single Rust crate can provide the basis of implementations in other languages. Furthermore, we demonstrate how jittering can be used to create more diffuse and accurate estimates of movement at the level of segments (‘flows’) on transport network, in reproducible code-driven workflows and with minimal computational overheads compared with the computationally intensive process of route calculation (‘routing’) or processing large GPS datasets. The overall aim is to describe the jittering approach in technical terms and its implementation in open source software. Before describing the approach, some definitions are in order: ``` Origins: locations of trip departure, typically stored as ID codes linking to zones Destinations: trip destinations, also stored as ID codes linking to zones Attributes: the number of trips made between each ‘OD pair’ and additional attributes such as route distance between each OD pair Jittering: The combined process of ‘splitting’ OD pairs representing many trips into multiple ‘sub OD’ pairs (disaggregation) and assigning origins and destinations to multiple unique points within each zone``` Approach Jittering represents a comparatively simple — compared with ‘connector’ based methods (Jafari et al. 2015) — approach is to OD data preprocessing. For each OD pair, the jittering approach consists of the following steps for each OD pair (provided it has required inputs of a disaggregation threshold, a single number greater than one, and sub-points from which origin and destination points are located): ``` Checks if the number of trips (for a given ‘disaggregation key’, e.g. ‘walking’) is greater than the disaggregation threshold. If so, the OD pair is disaggregated. This means being divided into as many pieces (‘sub-OD pairs’) as is needed, with trip counts divided by the number of sub-OD pairs, for the total to be below the disaggregation threshold. For each sub-OD pair (or each original OD pair if no disaggregation took place) origin and destination locations are randomly sampled from sub-points which optionally have weights representing relative probability of trips starting and ending there.

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description: Who speaks for the forest? Participatory mapping and contested land cover classification in Central Bali, Indonesia Overview: This presentation will discuss the ongoing effort to map, in unprecedented detail, a forested area in Central Bali, Indonesia, the use and ownership of which is currently a contested question. The presentation will outline the historical and political reasons for the contested nature of the land area under investigation, and then discuss participatory field mapping methods and a collaborative analysis pipeline developed to represent via formal GIS methodologies the land and its use with the needs of different and differing stakeholders in mind. Background: Our research approach is informed by current approaches to community mapping in general [Cochrane2020] and specific to emerging economies, with a particular focus on the conditions in Indonesia [Sulistyawan2018]. In particular, we are studying an area is Central Bali in the vicinity of the Taman Wisata Alam (TWA) Buyan -Tamblingan comprising 1,491 hectare of forest area including Alas Merta Jati [Suryawan2021], part of the Batukaru nature reserve which is estimated to contain sufficient springs to meet Bali’s water needs [Zen, 2019] (Fig. 1). The Alas Merta Jati is contested as it is currently claimed as ancestral lands (or “customary forest”) by the Tamblingan people and at the same time claimed as a state forest by the Indonesian government. While both entities claim to want to protect the forest along fashionable “sustainable” principles [Strauss2015], each entity interprets the responsibilities and benefits of sustainable actions in different ways. Subjecting the area to GIS compliant analysis approaches is one way by which differences and commonalities across stakeholders can become tractable. Collaboration framework : Our work is coordinated and overseen by a local NGO, the WISNU foundation (https://www.wisnu.or.id/) with which we have a memorandum of understanding outlining work methods, data collection and data ownership as well as ownership of intellectual property, creating formal boundary conditions for an equitable long-term outcome of the project. Moreover, our research team includes GIS professionals from the Indonesia National Research and Innovation Agency with expertise in remote sensing of tropical forests. Data sources and field work: Our data collection relies on a combination of high-resolution satellite imagery from PlanetScope (PS) provided by Planet Labs (integration of Sentinel-2 data is in progress as well) and field level data collection through inhabitants of the area. PS with a resolution of 3.7 m/pixel containing four channels: Blue (455 - 515 nm), Green (500 - 590 nm), Red (590 - 670 nm), and Near-Infrared (780 - 860 nm) [Raza et al. 2020]. Our first step follows standard practices. We study the composite’s PS satellite data in comparison with Google Earth (GE) images to identify a first round of land cover features. However, we then also check questionable areas with local informants who collect short video recordings of the actual situation on the ground (Fig. 2) and upload these verification datasets to a shared server. Moreover, our system is set up to support low-tech input data collected with old-fashioned paper and pencil. A handwritten set of longitude, latitude and identified land cover class is sent (via email) to the evaluation team where custom python scripts convert the information to an entry into a vector data set suitable for classification purposes. Complex land cover classes: The single most significant issue we encounter in this project is the fact that local knowledge and local interests are not represented in GIS maps nor in the land cover categories that routinely constitute formal categories in GIS representation. The existing GIS knowledge production pipeline, with its reliance on visual evidence, is not sufficient to address these needs. For example, how might one monitor and detect the outcome of efforts of the "jaga teleng" (traditional forest guards) as opposed to modern forest regrowth approaches? Even some quotidian and concrete “use” classes in the study area are resistant to visual-only inspection. Coffee plant farms typically grow together with and often under clove tree gardens and cannot be distinguished even with high-resolution (3.0m/pixel) satellite imagery without additional field level data collection. In general, the land use conditions in Bali are characterized by a variety of mixed uses and mixed conditions, with untouched areas mingling with secondary forests and overgrown light use agricultural areas creating a complex assemblage of “quasi-natural” conditions. And the tropical conditions on the island ensure that an agricultural area that has been harvested or abandoned, regrows to a semi-wild area in months. While this project contains many elements, the image interpretation and metadata creation that can be ingested into a GIS framework to represent some of the convention challenging categories listed above, is by far the most challenging aspect of the effort.

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description: Currently, various kinds of geospatial data are provided as open data/or map tile data. This implies that geospatial data have become easier to obtain and use than older data with traditional licenses and formats. By combining map tile data with Web Mapping clients, such as Open Layers and Leaflet, we can browse maps of any location without complicated procedures, i.e., downloading data, transforming coordinate system, extracting area of interest, and installing software. These web mapping technologies have been developed mainly in the field of human interpretation of map images. In addition, there has been the development of technologies for usage of map tiles not only background image of Web Mapping, but also processing and visualizing data in a client-side Web browser. The Geological Survey of Japan (GSJ) has proposed Data PNG and related format (1) for distributing data as map tiles. This format provides data in the PNG format which allows retaining numerical attributes, such as temperature, elevation, and geological classification. It is also possible to develop web applications with good responsiveness to user requests and promote diverse data use (Nishioka, 2019). The GSI published a demonstration site for the Data PNG tile (2) (3). Kitao (2020) developed a Web application for visualizing mapping point cloud data provided as Data PNG. Mapbox Terrain-RGB provides elevation data in PNG format and Mapbox GL JS visualizes these data as a 3D map. These applications were implemented with the capabilities of WebGL. WebGL is a cross-platform, open web standard JavaScript API for 2D and 3D graphics in modern Web browsers that allows the GPU-accelerated usage of image processing without the use of plug-ins. As described above, there are many applications for client-side data visualization using WebGL. However, an implementation of data analysis using WebGL, especially the map algebra function, has not been progressively developed. This paper aims to develop map algebra functions for Data PNG tiles with WebGL in a client-side Web mapping system. In this study, we attempted to develop map algebra functions for vineyard suitability assessment in Nagano Prefecture, Japan. In recent years, “Japan Wine,” made exclusively from grapes grown in Japan, has been gaining international recognition, and new wineries in Japan are also increasing. Thus, there is an urgent need to provide information to support the selection of appropriate vineyard sites and grape varieties. There have been attempts to assess the suitability of agricultural fields for crop production applying GIS. Despite these efforts, suitable site evaluation of vineyards has not been fully disseminated due to the lack of the following components: (1) sufficient quality, quantity, and accurate information necessary to determine the suitable site; (2) appropriate criteria for evaluating suitable sites based on the information, and (3) methods for providing evaluation results to consumers, such as new farmers. Therefore, we attempt to develop the client-side Web mapping system, using only a Web browser without any special skills and specific software, which enables new farmers to evaluate suitable sites for vineyards. A variety of environmental information is required for assessing vineyard suitability. In this report, we converted spatial information about geology, soils, topography, and meteorology, which is available as open data, to Data PNG tiles with FOSS4G tools, such as QGIS, TileMill, and MBUtil for suitability assessment. The vineyard suitability assessment system consists of the following map algebra functions: 1. Generate assessment values from a Data PNG tile layer by performing quadrature calculations, specifying the order of operations using parentheses, and classification based on logical operation formulae. 2. Comprehensive assessment function that performs a quadratic calculation, specifies the order of operations using parentheses, and classification based on logical operation formulae between the layers generated by the above procedure. 3. Vineyard suitability visualize function based on the comprehensive assessment The Web Mapping interface was developed with Leaflet, which has a graphical interface to input map algebra formulae and a function to display and export the image of vineyard suitability based on the comprehensive assessment result described above. A prototype of the vineyard suitability assessment system is available in the following: wata909.github.io/web-map-algebra. In this system, data used for assessment are provided as Data PNG tiles, and a map algebra function is performed by WebGL on a client-side. In other words, unlike many other Web Mapping systems, our system does not require server-side systems and/or middleware and can be operated using only a web browser. This means that various entities can be operated on the same system at a low cost or on a free Web service, such as GitHub pages. Additionally, the functions implemented in this system can be applied to various evaluations using the map algebra functions. However, our system contains only seven items for assessing suitable locations, which is not sufficient. The arithmetic functions of the system are limited to four arithmetic and logical operations, and it is not capable of implementing the complex model calculations required for highly realistic assessment. We are currently constructing a suitability assessment model using machine learning, information on the distribution of vineyards obtained from field surveys, and various environmental factors derived from field monitoring and published open data. In the future, we will use these data to improve the system and make it more practical.

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name: Client-side Web Mapping system for vineyard suitability assessment


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description: Developing a privacy-aware map-based cross-platform social media dashboard for municipal decision-making # Developing a privacy-aware map-based cross-platform social media dashboard for municipal decision-making ## Introduction Users of location-based social media networks (LBSN), such as Instagram, Flickr, or Twitter, have produced an unprecedented base of data over the past decade. According to ILIEVA & MCPHEARSON (2018: 553), "the enormous scale and timely observation are unique advantages of [social media data]" and therefore hold enormous potential for various application purposes such as urban planning, among others. Most notably for Instagram, as one of the largest LBSN, encouraging the sharing of locations when creating content, offers completely new and promising application purposes, through the combination of the spatial component with timestamps and the actual content (image & text). Public social media (SM) data have shown their potential examining the increasingly relevant social problems of Spatial (In-) Justice, spatial (in-) equality and spatial (in-) equity (Cf. SOJA 2013: 47). However, few research attempts were made to make these results available broader in practice and accessible to laypersons in an understandable way. LBSN data could contribute significantly to creating a better information base for municipal decision-making processes, reaching especially younger target groups. Until now, specifically these groups were difficult to reach in common participation processes (Cf. SELLE 2004), while bearing consequences of municipal policies for the longest period of time. Our stated research goal is therefore to provide citizens, laypersons and municipal decision-makers with an unprecedented LBSN Dashboard, as a simple open-source platform for spatial multi-purpose LBSN analysis. ## Problem Statement Such an undertaking raises certain ethical and legal questions, since the user data belong to the users themselves, including the right to self-determination over their data, on the one hand, and the right to privacy on the other. The far too short-sighted (but frequently used) argument that posts have been deliberately published, with all the consequences of their public nature in mind (e.g., BURTON et al. 2012: 2), is simply not sufficient for an in-depth discussion of privacy. This further violates the most important aspects of privacy (Cf. BOYD & CRAWFORD 2012: 672). In fact, most users are not or only partially aware of what can actually be inferred from what they share or disclose about themselves (KESSLER & MCKENZIE 2018: 6f). Yet, privacy is rarely addressed in LBSN research and, worse, often negligently ignored. In this context, many negative examples can be found where data was analyzed and high-resolution results were published, clearly violating users' privacy, for example, in scientific publications (Cf. KOUNADI & LEITNER 2014: 140). ## Research Interest Given the increasing socio-spatial inequality, the rapid growth of SM, and the growing interest of municipalities in SM knowledge, we see a significant need for such a privacy-aware LBSN dashboard, which is entirely new to the geospatial community. We develop a privacy-aware LBSN dashboard prototype and propose a data processing pipeline based on the HyperLogLog (HLL) algorithm by FLAJOLET et al. (2007). The dashboard is geared towards easy information retrieval and making use of the data richness of LBSN -- without compromising user privacy and the need for extensive data retention. Instead, we provide a unique, customizable, GDPR-compliant privacy approach. The combination of different open-source tools for structuring multi-platform LBSN data, leveraging the capabilities of HyperLogLog and simple Python integration ensure easy reproducibility and active community development (Cf. DUNKEL et al. 2021; DUNKEL & LÖCHNER 2021a & b). The dashboard prototype is tailored for use in municipalities and its citizens, but offers high scalability for other purposes or other spatial levels. A limited interactive demo and its GitHub repository are permanently publicly available as a result of a Master's thesis and an IoT Design Thinking Workshop (Cf. WECKMÜLLER 2021; BUNDESSTADT BONN 2022). We plan on finishing and automatizing the data processing pipeline, enabling more sophisticated queries and adding further visualization methods. In the long run, the dashboard is thought to serve as a participation and open data hub for all citizens and for any city in the world. So far, the city of Bonn and Chemnitz (Germany) are pilot partners of this research project.

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description: Data-driven innovation, as outlined by Granell et al. (2022), has seen recent advances in technology driven by the continuous influx of data, miniaturization and massive deployment of sensing technology, data-driven algorithms, and the Internet of Things (IoT). Data-driven innovation is considered key in several policy efforts, including the recently published European strategy for data, where the European Commission acknowledged Europe’s huge potential in the data economy by leveraging on available data produced by all actors (including public sector, private sector, academia and citizens). Technologies currently used for the management, exchange and transmission of data, including geospatial data, must be evaluated in terms of their suitability to efficiently adapt to streams of larger data and datasets. As more users access data services through mobile devices and service providers are faced with the challenges of making larger volumes of data available, we must consider how to optimise the exchange of data between these clients and servers (services). For many years JSON, GeoJSON, CSV and XML have been considered as the 'de facto' standard for data serialisation formats. These formats, which enjoy near ubiquitous software tool support, are commonly used for the storage and sharing of large amounts of data in an interoperable way. Most Application Programming Interfaces (APIs) available today facilitate data sharing and exchange, for a myriad of different types of applications and services, using these exchange formats (Vaccari et al., 2020). However, there are many limitations to approaches based on JSON and XML when the volume of data is likely to be large. Potentially the most serious of these limitations is related to reduced computational performance, when exchanging or managing large volumes of data where there are high computational costs associated with (de)serializing and processing these data. Against this background, binary data serialization approaches allowing for the interoperable exchange of large volumes of data have been used extensively within scientific communities such as meteorology and astronomy for decades. In recent years, popular distributors of geospatial data have also begun making use of binary data formats. Examples are OpenStreetMap (OSM) data (e.g. the OSM Planet and OSM Full History Planet files, providing access to the whole OSM database and its history) as well as the popular ESRI Shapefile format's main file (.shp), which also contains geometry data and is stored as a binary data file. In this paper we describe the methodology, implementation and analysis of a set of experiments to analyse the use of binary data serialization as an alternative to data exchange in XML or JSON data formats for several commonly encountered GIS workflows. Binary data serialization allows for the storage and exchange of large amounts of data in an interoperable fashion (Vanura and Kriz, 2018). While anecdotal evidence indicates binary serialization approaches are more efficient in terms of computation costs, processing times, etc., there are additional overheads to consider with these approaches including special software tools, additional configuration, schema definitions, etc. (Viotti and Kinderkhedia, 2022). Additionally, there have been few, if any, investigations of binary data serialization approaches specifically for geographical data. Our set of experiments investigates the advantages and disadvantages of binary data serialization for three common GIS workflow scenarios: (1) geolocation point data from an OGC SensorThings API; (2) geolocation point data from a very large static GeoPackage dataset representing the conflation of address data from the National Land Survey of Finland and OpenStreetMap; and (3) geographic polygon datasets containing land cover polygons (currently ongoing work). We consider comparisons of JSON and GeoJSON with two very popular binary data formats (Proos and Carlsson, 2020), namely Google Protocol Buffers and Apache Avro. Protocol Buffers (Protobuf) is an open source project developed by Google providing a platform neutral mechanism for serializing structured data. Apache Avro, another very popular schema-based binary data serialization technique, is also a language-neutral approach which was originally developed for serializing data within Apache Hadoop. Both Protobuf and Avro have wide support in many popular languages such as C++, C#, Java and Python. The full paper will provide detailed descriptions of the implementations of our experiments. However, here we provide a summary of some of the key results and highlights of our analysis. As binary data formats such as Protobuf and Avro are not self-describing schemata and schema definitions are required for each dataset or data stream, these definitions are required for the serialization and…

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name: Geospatial data exchange using binary data serialization approaches


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description: Serving Geospatial Data using Modern and Legacy Standards: a Case Study from the Urban Health Domain Urban planning and design play an important role in amplifying or diminishing built environmental threats to health promotion and disease prevention (Keedwell 2017; Hackman, et al. 2019). However, there is still a lack of good evidence and objective measures on how environmental aspects impact individual behavior. The eMOTIONAL Cities project (eMOTIONAL Cities - Mapping the cities through the senses of those who make them 2021) sets out to understand how the natural and built environment can shape the feelings and emotions of those who experience it. It does so with a cross-disciplinary approach which includes urban planners, doctors, psychologists, neuroscientists and engineers. At the core of this research project, lies a Spatial Data Infrastructure (SDI) which assembles disparate datasets that characterise the emotional landscape and built environment, in different cities across Europe and the US. The SDI is a key tool, not only to make the research data available within the project consortium, but also to allow cross-fertilisation with other ongoing projects from the Urban Health Cluster and later on, to reach a wider public audience. The notion of SDIs emerged more than 20 years ago and has been constantly evolving, in response to both technological and organisational developments. Traditionally, SDIs adopt the OGC W_s service interfaces (e.g.: WMS, WFS, WCS), which are based on SOAP, the Simple Object Access Protocol. However, in recent times, we have seen the rise of new architectural approaches, which can be characterised by their data-centrism (Simoes and Cerciello 2021). Web-based APIs have numerous advantages, which speak for their efficiency and simplicity. They provide a simple approach to data processing and management functionalities, offer different encodings of the payload (e.g.: JSON, HTML, JSON-LD), can easily be integrated into different tools, and can facilitate the discovery of data through mainstream search engines such as Google and Bing (Kotsev et al. 2020). These APIs often follow a RESTful architecture, which simplifies its usage, while minimising the bandwidth usage. Moreover, the OpenAPI specification (OpenAPI Initiative 2011) allows to document APIs in a vendor-independent, portable and open manner, which provides an interactive testing client within the API documentation. OGC has embraced this new approach in its new family of standards called OGC APIs (OGC 2020a). Although still under active development, it already produced several approved standards: the ‘OGC API - Features’’ (OGC 2022b, the ‘OGC API - EDR’ (OGC 2022c), the ‘OGC API Common’ (OGC 2022d) and the ‘OGC API - Processes’ (OGC 2022e) which provide standardised APIs for ensuring modern access to spatial data and processes using those data. There are many similarities in the process of designing and implementing open source and open standards. OSGeo encourages the use of open standards, like those from OGC and there is even a Memorandum of Understanding between the two organisations (OSGeo 2012). In practice, many long-standing OSGeo projects implement OGC standards and they often contribute to the standards development (e.g.: GDAL, Geoserver, QGIS, OpenLayers, Leaflet). However, in the majority of cases they still implement the legacy W_s standards, rather than the new OGC APIs. In the eMOTIONAL Cities project we have set out to create an SDI based on OGC APIs, but realised that we needed to support some legacy standards, because an OGC API equivalent was not widely supported yet. This has led us to create two stacks: one OGC APIs (e.g.: modern) and another one using W*s services (e.g.: legacy). Both stacks rely on FOSS/OSGeo software, and whenever relevant we have contributed to some of those projects. The modern stack includes Elasticsearch and Kibana (Elastic), which add extra capabilities in terms of searching, analytics and visualisation. For the sake of reproducibility, all software components were virtualized into docker (Wikipedia 2022) containers and they are orchestrated using docker-compose. The results are published in the eMOTIONAL Cities public github repository (eMOTIONAL Cities H2020 Project 2021). Despite its numerous advantages, we still see a lack of adoption of the OGC APIs within most SDIs. In part this could be due to the standards not being well known, but it could also be due to a lack of knowledge about which implementations are available out there, specially as FOSS. In this paper we would like to share our modern SDI architecture, and the reasons for choosing pygeoapi (Kralidis 2019) for publishing data as OGC API Features, Vector Tiles and Records. Although the standards we selected target the Urban Health use case, we believe they are generic enough to be useful for sharing data in other contexts (e.g.: climate change, cross-border datasets). We are confident about a transition to OGC APIs, but we are also conscious that this may take time, and for a period of time many solutions will have to offer both modern and legacy standards.

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description: Development of a collaborative platform for intelligent territorial mapping of the city of Oran In developing countries, sustainable development and territorial intelligence are of greater interest to public authorities and citizens. In Algeria, the combination of resources with technological innovation goes in the direction of building a productive territorial intelligence. This translates into a process aiming at developing a systemic approach of the territory in order to analyse its physical, social and economic dimensions in order to exchange the different points of view of the territorial, social and economic actors and to make the policies more coherent. In this contribution, we have focused the research on studies related to decisional computing used by governmental entities, especially in the field of public services. It turned out that the use of collaborative web platforms involving several actors belonging to different spheres (government, economy, social, etc.), constitutes a tool for the development of territorial intelligence thanks to the availability of data which allows a considerable saving of time and cost. Indeed, the construction of a territorial information system makes possible the networking of these actors, to elaborate clear and reliable schemes of urban planning for a liveable environment, which led us to think about the implementation of a web platform for exchanges, collections, production and dissemination of data and social animation to reach equitable consensus. This will allow, among other things, the development of project management through the formalisation of objectives and collaborative work for the planning and optimisation of tasks. Geographical information is a crucial element in most of the daily uses thanks to the intelligent applications put online and exploited by different categories of connected people. Therefore, the interest and necessity of sharing geo-located information for decision support systems is well proven nowadays. In the same context, participatory mapping initiatives through voluntary geographic information (VGI), citizen-generated content or crowdsourcing are now being used as a new instrument for information gathering and two-way exchange between the various entities in the urban environment ranging from ordinary citizens to leading actors. This direct data is a key element in all the decision-making processes leading to the achievement of urban governance modalities. The objective of our work is to provide an interactive solution ensuring the collaboration of actors (decision-makers and citizens) on a webmapping platform for the reporting of needs by citizens in terms of public services such as road defects, public lighting failures and any other existing problems in an urban area. This application could also be used for emergency alerts (road accidents, natural disasters, etc.). As a study area, we chose the city of Oran, located in the west of Algeria, which is the second largest urban metropolis in the country. The realization of the collaborative web mapping platform is based on Free and Open-Source Software for Geospatial (FOSS4G). As a spatial database management system, we used PostgreSQL with its spatial extension PostGIS, which is classified as one of the most powerful open source DBMS. The GIS server used in our application is GeoServer, which guarantees to satisfy a maximum of required webmapping services (WMS, WFS, WMTS, WCS, etc.). The webmapping interface must offer two main components: an interactive citizen space with the web map and a space for decision makers who will be able to consult, verify and validate the data sent in order to proceed with the action. Among the development options for this type of webmapping interface, we are interested in GeoNode, an open source framework based on mature and robust frameworks and software like Django, OpenLayers, PostGIS, GeoServer and pycsw. In our case, GeoNode will allow the integration of a multitude of geospatial functions for manipulating data and responding to any type of request on the web map. The platform, which we have named "Wilayati", will offer new participatory methods for monitoring activities in the urban environment. Its functionalities will ensure, on the one hand, the sharing of data on a map based on voluntary contributions from the citizens of Oran and, on the other hand, the visualisation and manipulation of the data by decision-makers in order to give them a support for the management of localised interventions. Different types of data on the urban fabric of the city of Oran were collected from the processing of satellite images as well as datasets on the road network of Oran obtained from OpenStreetMap after improving the intrinsic quality. In parallel, a campaign on social networks will soon be launched, with the aim of better analysing the orientations of the public services most requested by citizens. The application, under development, will provide a new source of data that can be easily exploited in urban governance and will provide a way for citizens to participate in improving their environment through regular updates of the geographical database. Finally, as a perspective, the results, after deployment of the platform, will give an overview of the impact of citizens in participatory mapping highlighting points of interest and urban infrastructures of cities in Algeria.

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description: Mainstreaming metadata into research workflows to advance reproducibility and open geographic information science Free and open source software for geospatial analysis (FOSS4G) supports burgeoning possibilities for practicing open and computationally reproducible human-environment and geographical research (Singleton et al 2016). Open and reproducible research practices may accelerate the pace of scientific discovery and enhance the scientific community's functions of knowledge verification, correction, and diffusion (Rey 2009, Kedron and Holler 2022). Geospatial metadata provides the foundation for reproducibility and open science and accordingly, requires more support in open source geospatial software. Following Wilson and others' (2021) five star guide for reproducibility, researchers can achieve four stars by conducting research with open data and software and documenting metadata according to the standards of the International Organization for Standardization (ISO) and OGC (Open Geospatial Consortium). For Tullis and Kar (2021), metadata is the key to documenting the provenance of research data artifacts, preserving information about every detail of data creation and transformation. Wilkinson and others' (2016) FAIR Guiding Principles for scientific data management enumerate functions for metadata in each of the principles for research: findable, accessible, interoperable, and reusable. However, open source geospatial software platforms generally lack the tools necessary for mainstreaming geospatial metadata into the full research workflow in support of more efficient research work and enhanced reproducibility and open science. This research on metadata is part of a larger human-environment and geographical sciences reproducibility and replicability (github.com/HEGSRR) project aimed at conducting formal reproduction and replication studies in the geographical sciences and integrating reproducibility into undergraduate and graduate level curricula in research methods. Following the National Academies of Science, Engineering and Medicine (NASEM, 2019), a reproduction study aims find the same results using the same data and methodology as a published study. A replication study aims to test the findings of a published study by collecting new data and following a similar methodology, which may intentionally modify one or more research parameters. Together, reproduction and replication studies offer a deep understanding of the original research, test its credibility and generalizability, and enhance the self-corrective mechanisms of the scientific community. Metadata is information about data, including essential contextual information about the data's spatial structure, attributes, creation, maintenance, access, licensing, and provenance. A key component of reproducible research is an executable research compendium containing all of the data, code, and narrative required to compile a research publication from raw data (Nust and Pebesma 2021 and the Opening Reproducible Research Project). Computational notebooks like Jupyter notebooks or R Markdown are commonly used to interweave narrative with code in executable compendia. In order to maximize replicability and inferential power, the research compendium should begin with a pre-registered research plan prior to data collection, requiring researchers to fully specify metadata for all of the research data and analyzes that they intend to create (Nosek et al. 2018). It is recommended to store compendia in version tracking systems like Git in order to preserve a full history of changes to the research project. Finally, the compendium should be published parallel to academic publications so that other researchers can independently re-run, check and verify the analysis, or incorporate the research in future projects. In order to maximize the findability and legibility of the research compendium for both humans and machines, the overall repository and each of its components must be meticulously documented with metadata according to international standards (Wilkinson et al. 2016, Wilson et al. 2021). In this three-part research paper, we focus on metadata in research compendia and related research products through all phases of the research workflow. First, we specify ideal requirements of geospatial metadata in support of reproducible research workflows and open science. We consider metadata needs at each step of the research process, including proposal writing, pre-analysis registration, ethics review board approval, data collection and analysis, publishing, and reproducing published research. The metadata software needs assessment is based on literature review of reproducibility and open science, and on teaching and practicing reproducibility with geographic methods. Second, we review the Dublin Core and International Organization for Standardization (ISO) geospatial metadata standards and popular open source platforms for geospatial research and their support for the requirements of geospatial metadata articulated in the first part. The scope of the review includes metadata functionality available through spatial analysis software platforms, including R, Python, QGIS, GRASS and SAGA; and it also includes metadata or cataloging tools, including GeoNetwork, GeoNode, the USGS Metadata Wizard, and mdeditor. Finally, we articulate a vision for open source geospatial metadata software development in support of open and reproducible human-environment and geographical research. In this vision, metadata software tools shall integrate with executable research compendium to assist researchers with their workflow from inception to publishing and archiving. The vision builds off our HEGSRR project, in which we independently reproduce and replicate published studies with open source geospatial software, integrate reproduction and replication studies into project-based geographic information science courses, and develop curricula and infrastructure for reproducible research. Each section of the paper is thus supplemented with experiences and examples drawn from the HEGSRR project. Of particular relevance, we have already completed seven reproduction or replication studies with graduate and undergraduate students using open source geospatial software, encountering numerous barriers caused by inadequate use or documentation of metadata in research planning, execution, and archiving. We have also developed a template Git repository compendium for reproducible research and prototyped its use in our studies and teaching, discovering software barriers to documenting metadata and opportunities to integrate metadata into more efficient and reproducible research practices. We have selected FOSS4G for this research paper in hopes of reaching both the academic audience and developer audience at the conference. We hope to raise awareness in the academic audience of the critical importance of geospatial metadata in each stage of the research workflow. We hope to raise interest in the open source geospatial software community for collaboration on improved support for geospatial metadata in research workflows.

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description: Laying the foundation for an artificial neural network for photogrammetric riverine bathymetry The submerged topography of rivers is a crucial variable in fluvial processes and hydrodynamics models. Fluvial bathymetry is traditionally realised through echo sounders embedded on vessels or total stations and GNSS receivers whether the surveyed riverbeds are small streams or dry. Besides being time-consuming and often spatially limited, traditional riverine bathymetry is strongly constrained by currents and deep waters. In such a scenario, remote sensing techniques have progressively complemented traditional bathymetry providing high-resolution information. To date, the peak of innovation for bathymetry has been reached with the use of optical sensors on uncrewed aerial vehicles (UAV) systems, along with green lidars (Vélez-Nicolás et al., 2021). The main obstacle in optical-derived bathymetry is the refraction of the light passing the atmosphere-water interface. The refraction distorts the photogrammetric scene reconstruction, causing in-water measures to be underestimated (i.e., shallower than reality). To correct these distortions, radiometric-based methods are frequently applied. They are focused on the spectral response of the means crossed by the light and are typically built on the theory that the total radiative energy reflected by the water column is function of the water depth (Makboul et al., 2017). The primary goal of the research on submerged topography is to understand the relationship between the water column reflectance and the water depth using statistical and trigonometrical models. The spread of artificial intelligence has given a new light of interest on spectral-based bathymetry by investigating the non-linear and very complex relationship between variables (Mandlburger et al., 2021). To train artificial intelligence models, large amounts of data are usually necessary; therefore, participatory approach and data sharing are required to build statistically-relevant datasets. In this scenario, FOSS tools and distributed resources are mandatory to manage the dataset and allow the replicability of the methodology. This work aims to test the effectiveness of artificial intelligence to correct water refraction in shallow inland water using very high-resolution images collected by Unmanned Aerial Vehicles (UAV) and processed through a total FOSS workflow. The tests focus on using synthetic information extracted from the visible component of the electromagnetic spectrum. An artificial neural network is created with the data from three different case studies placed in west-north Italy, and geologically and morphologically similar. The data for the analysis were collected in 2020. Each data collection was realised using a UAV commercial solution (DJI Phantom 4 Pro), and the following datasets were generated: i) RGB georeferenced orthomosaic of the riverbed and banks obtained from photogrammetric process, ii) georeferenced Digital Elevation Model (DEM) of the riverbed obtained from photogrammetric process, iii) GNSS measures of the riverbed and the riverbanks. The UAV-collected frames were elaborated through a standard structure from motion (SfM) procedure. Visual SfM was employed to align images and the 3D point cloud computation. The digital surface model (DSM) and the orthomosaic production were generated starting from the point cloud in Cloud Compare software. By applying the so-called direct-photogrammetry, the point clouds were directly georeferenced in the WGS84-UTM32 coordinate system thanks to the positioning information retrieved from the embedded GNSS dual-frequency receiver (Chiabrando, Lingua and Piras, 2013). Using the information regarding the camera position and the local height model provided by the national military Geographic Institute (IGM), the ellipsoidal heights were translated into orthometric heights. The GNSS measures had 3 cm accuracy on the vertical component and 1.5cm on the horizontal components. The RGB information, DSM and seven radiometric indices (i.e., Normalised Difference Turbidity Index; Red and Green Ratio; Red and Blue Ratio; Green and Red Ratio; Green and Blue Ratio; Blue and Red Ratio; Blue and Green Ratio) were calculated and stacked in an 11-bands raster (input raster). The Up component of the bathymetry cross-sections constituted the so-called "Z_GNSS" dataset and is the dependent variable of the regression. The position (Easting, Northing, Up) of each Z-GNSS observation was used to extract the pixel values of each band of the input photogrammetric dataset, including the photogrammetric DEM. The dataset was then normalised and divided into test (20% observations) and training (80% observations) datasets. In this work, a 5-layer multilayer perceptron (MLP) networks model with three hidden layers was built in Python using the deep learning library Keras with TensorFlow backend (Abadi et al., 2016). The ReLu activation function was added to the ANN layers to bring non-linear properties in the network. The dimension of the input layer is 11, and the weights are initialised to small Gaussian random values (kernel initialiser 'Normal') despite usually skewed or bimodal. A kernel regulizer, L1, was added to reduce the overfitting. The applied optimiser to update weights in the network is the Adaptive Moment Estimation (Adam) search technique, and the loss function, which evaluates the model used by the optimiser to navigate the weights, is the mean absolute error between the predicted output and the target output. The network was trained on the normalised dataset. The r-squared score, the Mean squared error and the Mean absolute error were computed. Finally, the permutation importance was measured using the eli5 python library. The neural network regressor performed over 0.80 of r-squared score on the test dataset. As expected, the permutation importance analysis reveals the high impact of the DEM and visible bands, and low importance scores are reported for ratios bands. The results are satisfying and quite relevant, although the model is the first step through a more complex and deeper neural network to correct water distortions in rivers. It has been trained on a relatively small dataset, but we intend to follow up with the research, add more data, and develop a free and open tool for the scientific community. The present work, provide a good insight about the high reliability and accuracy of artificial intelligence approaches in optical-derived bathymetry.

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description: Development of a graphical user interface to support the semi-automatic semantic segmentation of UAS-images Image semantic segmentation focuses on the problem of properly separating and classifying different regions in an image depending on their specific meaning or use, e.g. belonging to the same object. It is worth to notice that in general segmentation is a ill posed problem: it is not possible to provide a unique solution to such problem, different solutions can typically be acceptable, depending on the segmentation criterion which is applied. Nevertheless, regularization techniques are typically used to reduce the issues related to ill posedness, hence ensuring the computability of a unique solution. In the case of semantic segmentation, ill posedness is also reduced by the specific data and object interpretation that shall be included in the semantic part of the data. It is also worth to notice that image semantic segmentation tools can be useful in many several applications, related both to the interpretation of images themselves, but also of other entities related to such images. The latter is for instance the case of a point cloud, whose objects and areas are also described by some images. In this case, a proper image semantic segmentation could be back projected from the images to the point cloud, in such a way to exploit such process to properly segment the point cloud itself. Automatic image semantic segmentation is a quite challenging problem that nowadays is usually handled by taking advantage of the use of artificial intelligence tools, such as deep learning based neural networks. The availability of reliable image segmentation datasets plays a key role in the training phase of any artificial intelligence and machine learning tool based on the image analysis: indeed, despite artificial intelligence tools can currently be considered as the state of the art method in terms of recognition and segmentation ability, they do require a huge size learning dataset in order to ensure reliable segmentation results. The developed graphical user interface aims at supporting the semi-automatic semantic segmentation of images, hence easing and speeding up the generation of a ground truth segmentation database. Then, such database can be of remarkable importance for properly training any machine or deep learning based classification and segmentation method. Despite the development of the proposed graphical user interface has been originally motivated by the need of easing the process of producing a ground truth segmentation and classification of plastic objects in maritime and fluvial environments, within a project aiming at reducing plastic pollution in rivers, the developed tool can actually be used in contexts that are more general. Indeed, the interface supports in particular two types of quite specific operations: 1) segmenting and identifying objects in a single image, 2) exporting previously obtained results in new images, while also enabling the computation of certain related parameters (e.g. navigation related, such as tracking the same object over different data frames). Different types of images are supported: standard RGB, multispectral images (already available as TIFF (Tagged Image File Format) images) and thermal ones. For what concerns the semantic segmentation of a single image, several alternative segmentation options are supported, starting from manual and going to semi-automatic segmentation methods. First, the manual segmentation of the objects is ensured by means of properly inserted polylines. Then, intensity based and graph based methods are implemented as well. On the semi-automatic side, two tools are provided: a) a machine learning based method, exploiting few click choices by the user (implementing a rationale similar to that in (Majumder et al., “Multi-Stage Fusion for One-Click Segmentation”, 2020), i.e. aiming at minimize the user input), b) when images are periodically acquired by a UAS, at quite high frequencies, two successive frames are expected to be not that different from each other. Consequently, the system aims at determining the camera motion between different frames, and using machine learning tools to properly extend and generalize the results in the previous image to those of the new one. The latter method opens to a wider scenario, where some more information may come by the availability of consecutive frames. In particular, such additional information that could be determined by properly analyzing consecutive frames could be used to: assess and track the UAS movements while acquiring the video frames, increase the automation in the segmentation and classification process of an object. Overall, the developed graphical user interface is expected to be useful to support the semi-automatic identification of objects, and to help determining the UAS and the object movements as well. Despite full autonomous image semantic segmentation would clearly be of interest, its development seems to be quite challenging. Nevertheless, future investigations will be dedicated to these aspects, in order to increase the procedure automation level. The simulator will be freely available for download from the website of the GeCo (Geomatics and Conservation) laboratory of the University of Florence (Italy).

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description: The Role of Open Standards in Digital Building Permitting, 3D Registration of Condominium, and Update of 3D City Models Digitalization is being adopted in many public services to increase the efficiencies of the required operations. Regarding this, there is an important interest in digitalizing the current building permit procedures since most of the buildings are designed digitally and as three-dimensional (3D). In addition, several countries are making an effort to realize the transition from two-dimensional (2D) cadastre to 3D cadastre. This is because 2D delineation of the legal rights may remain incapable to reflect the reality with respect to property ownership in multipartite buildings. The 3D city models should also be kept updated to effectively manage the occasions (e.g., natural disasters) and services (e.g., waterworks) in the living areas. In this sense, the open data standards have a vital role to enable interoperability between different domains such as AEC and Land Administration. In this sense, this paper first aims to show the current situation and opportunities on how to efficaciously benefit from open data standards for three significant issues. The issues can be listed as, 1) digitalizing the building permit procedures, 2) registering the condominium as 3D, and 3) updating the 3D city models. It then presents an approach for integrating open standards for 3D registration of condominium rights in Turkey context. The integration of GIS and BIM, GeoBIM, has gained importance in terms of digital building permitting since there are rules to be checked with respect to the built environment; for example, the availability of bicycle parks. Besides, zoning plans that are essential for building permitting are generally formatted with GIS-based data. There are studies in the literature that aim to carry out the building permitting by benefiting from the integrated GIS and BIM approach. This approach is also connected with the update of the 3D city model database because the as-built models of the buildings can be integrated into this database after the necessary conversions (Guler & Yomralioglu, 2021). 3D registration of condominium rights, which is part of the 3D cadastre, is often researched in the literature since 2D-based delineation of ownership rights might be insufficient in detecting who owns or responsible for which parts of the multipartite buildings. The availability of 3D representation of ownership rights will be efficient for various land administration applications, for example, property valuation. Open standards are, of course, pivotal for realizing the 3D registration of condominium rights as they not only provide the integration between different organizations but also enable the interoperability for other processes that are needed the same data. In this connection, Land Administration Domain Model (LADM) is the first standard that comes to mind because it aims to provide a common language for land administration systems and supports 3D representation through boundary face and boundary face strings. Since standards like CityGML focus on 3D modeling of buildings more deeply, there are attempts that integrate the CityGML and LADM by exploiting advantageous features of each of the standards for better depiction of ownership rights as 3D (Li et al., 2016). The “Building” and “Cadastre” themes that are produced within the context of Turkey National GIS (TNGIS) describe the relationship between related features, namely parcel, building, building blocks, and condominiums. These features are modeled such that they allow for integration with other standards such as CityGML and IndoorGML so as to enable the efficient reuse of spatial data in different applications. To enable better interoperability and 3D depiction of condominium rights, an integrated model is developed. The proposed features that are adapted from LADM permits the 3D representation of ownership rights. The proposed features are linked with the IFC entities, namely “IfcZone”, “IfcRelAssignsToGroup”, and “IfcSpace” to provide integration with IFC. “BuildingUnit” is linked with the “BuildingCondominium3D” feature, and hence the integration with CityGML data is provided. It can be mentioned that “BuildingCondominium3D” corresponds to the “LegalSpaceBuildingUnit” feature of LADM. The proposed model incorporates the integration with IndoorGML by means of the link between “BuildingCondominium3D” and “CellSpace”. Due to fact of the inevitable proliferation of digitalization, the processes related to land and city management need to be accomplished more digitally and fast. There is an important potential to be practiced building permit issuing, as one of the important public services, in the sense of improvement and automation of the process (Noardo et al., 2022). Open data standards have a quite crucial role in realizing this potential. This is because these standards enable the standardization of information flow between designers and organizations that are responsible for compliance checking. In other words, applicants can prepare their submissions according to required information for building permit issuing. There is a strong interrelation between digital building permitting and the update of 3D city models because if the as-built IFC data of buildings are available, these data can be converted to CityGML, and thus the 3D city model database can be kept up-to-date. In addition to this, an up-to-date 3D city model database can be used for digital building permitting as there is a need for built environment data for integrated and comprehensive compliance checking. For example, rules with respect to infrastructure facilities can be checked using 3D city models. Open standards are effective to be successful in practicing the 3D cadastre. With the increasing trend in BIM, there are proposed approaches that use the IFC schema for 3D delineation of apartment rights in multipartite buildings. In parallel, this paper concentrates on a model that provides the integration with IFC data in the 3D representation of condominium rights in Turkey. The misinterpretations regarding who is responsible or owns of which parts of the buildings can be prevented using the IFC-based depiction of ownership rights (Shin et al., 2020). The semantic information pertaining to independent sections can be queried using the produced IFC-based models. These models will also be quite helpful for property valuation applications in Turkey that exploit 3D variables such as size, volume, position, and material quantities since they provide detailed information on indoor parts of the buildings (El Yamani et al., 2021).

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description: Creating a land use/land cover dictionary based on multiple pairs of OSM and reference datasets 1. Background OpenStreetMap (OSM) can supply useful information to improve land use/land cover (LULC) mapping (Arsanjani, 2013; Schultz, 2017; Zhou, 2019). A dictionary is needed to convert each OSM tag into an LULC class. However, such a dictionary was mostly created subjectively or with only one pair of OSM and reference datasets. As a result, the existing dictionaries may not be applicable to other study areas. This study designed four measures: sample count, average area percentage, sample ratio and average maximum percentage; and used multiple pair of OSM and reference datasets to create a dictionary. 50 pan-European metropolitans were involved for testing and 1409 different OSM tags were found. We further found that: 1) Only a small proportion of OSM tags play a decisive role for LULC mapping. 2) An OSM tag may correspond to multiple different LULC classes, but the issue that which and how different LULC classes correspond to each OSM tag can be determined. Moreover, not only the proposed dictionary is useful for various applications, e.g., producing LULC maps, obtaining training and/or validation samples, assessing the quality of an OSM dataset, but also the approach to creating this dictionary can be applicable to different study areas and/or LULC datasets. 2. Data OSM datasets of the 50 metropolitans were acquired for free from http://download.geofabrik.de/index.html in June 2020. Corresponding reference datasets (called urban atlas or UA) were available from https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012/# in June 2020 freely. 3. Methodology The tenet of our approach is to use multiple pairs of OSM and reference datasets for creating an OSM-LULC dictionary. In each pair of datasets, an OSM tag may correspond to different LULC classes, it is therefore necessary to determine which is the most appropriate LULC class for each OSM tag. we assumed that most OSM tags have been tagged by volunteers correctly (Zhou et al. 2019). Following this assumption, the way to determine the most appropriate LULC class for each OSM tag includes two steps. Firstly, all objects of an OSM tag are intersected with those of different LULC classes, respectively. After that, the LULC class with the maximum intersecting area is viewed as the most appropriate one for this OSM tag. Four attributes and four measures are designed to describe an OSM- LULC dictionary. They are: Tag ID, Tag Name, Class ID and Class Name in terms of attributes; and Sample Count, Average Area Percentage, Sample Ratio and Average Maximum Percentage in terms of measures. They are introduced as follows: 1. Tag ID denotes the ID of an OSM tag, 2. Tag Name denotes the name of an OSM tag. 3. Class ID denotes the ID of an LULC class. 4. Class Name denotes the ID of an LULC class.5. Sample Count (SC) denotes how frequent an OSM tag is appeared in different study areas or datasets. 6. Average Area Percentage (AAP) denotes the average of the area percentages of an OSM tag in multiple different OSM datasets. 7. Sample Ratio (SR) denotes the percentage of study areas or datasets that an OSM tag corresponds to an LULC class. 8. Average Maximum Percentage (AMP) denotes the average of all the maximum percentage in different study areas or datasets. 4. Conclusion and application This study proposed an approach to creating an OSM-LULC dictionary. The tenet of this approach was to involve multiple pairs of OSM and reference datasets for the analysis. First of all, each pair of OSM and reference datasets were intersected and the most appropriate LULC class for each OSM tag was determined. Then, the four measures, i.e., sample count (SC), average area percentage (AAP), sample ratio (SR) and average maximum percentage (AMP), were designed and calculated based on multiple pairs of OSM and reference datasets. More precisely, a total of 50 pairs of OSM and reference datasets in pan-European metropolitans were chosen as study areas for creating an OSM-LULC dictionary. Finally, a number of 1409 different OSM tags were found and they were reclassified into five and 14 different LULC classes, respectively. Moreover, this dictionary was also analyzed with the four proposed measures. Results showed that: Firstly, most OSM tags (＞ 1,000) were only found in less than five study areas (SC ＜ 5). Moreover, only 37 of the 1409 OSM tags had a percentage of average area (AAP) larger than 0.1%. This indicates that a small proportion of OSM tags can play a decisive role. Secondly, an OSM tag may correspond to multiple different LULC classes within a pair of OSM and reference datasets; The most appropriate LULC class for each OSM tag may also vary among different pairs of datasets. Thus Both the SR and AMP may also vary in different pairs of OSM tag and LULC class. With the proposed dictionary, it is possible to understand the differences of different OSM tags and different pairs of OSM tag and LULC class. This is essential not only for producing LULC maps, but also for picking up training and/or validation data from an OSM dataset and also for detecting incorrect tags in an OSM dataset. Therefore, we concluded that it has benefits for creating an OSM-LULC dictionary based on multiple pairs of OSM and reference datasets.

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