uk: Комп'ютерні науки

description: 1. Introduction: For most end-users, the term ‘software’ is equivalent with executing a given application to obtain a desired result. Moreover, the highest importance is usually attributed to the software being free to use. Besides intuitive use, a key requirement for success and wider acceptance of a software application is easy access, which is often facilitated though open-source projects. While users naturally only care about stability and functionality of the software, software developers often see their task completed once the application reaches a certain degree of maturity and its source code is made available. However, in addition to ease of use and targeted software development, a third component in the life cycle of software design [Vogt 2019] is the software provision. The importance of adequate software dissemination entails a wide range of aspects, which are often undervalued but are crucial to best meet end-user expectations and to achieve the highest application acceptance. In this manuscript, we outline a perspective on approaches to appropriately address issues of software provision aimed at promoting software in an efficient way. We illustrate the motivation and features of various aspects of software provision on the recently published software GWB [Vogt et al., 2022] [1] and its implementation on the FAO cloud computing platform SEPAL [2]. 2. Software provision This section summarises reflections on various aspects when disseminating a software application. - Source code: The provision of the source code is often perceived as a final product delivery. However, most end-users cannot make any use of the source code because they do not understand the programming language, do not know how to compile it or how to properly link required dependencies. The large number of Linux distributions provides an additional challenge due to varying inter-dependencies of distribution-specific compiled libraries and packaging policies. Packaging - the conversion of source code into a functional executable binary - is a science on its own and is beyond the skills of a typical end-user. - Target platform: Maximum outreach is achieved through a software setup that will work on as many platforms and operating systems (OS) as possible. - Software packaging: The scope of packaging is to bundle the entire application into a single archive, including or linking OS-specific dependencies, pre- and post-installation instructions, and the integration into the OS via menu entries. Examples are rpm and deb-packages (Linux), dmg-packages (macOS), and exe/msi-packages (MS-Windows). Packaging allows for efficient system-wide software management: installation, upgrades, and removal of the application and provides application access to all OS users. However, it also requires administrator rights, which are not available on many secured or closed IT environments, such as in government agencies, where users may fully access a limited OS-space only, i.e., their $HOME directory. Yet, this situation can be addressed by setting up the software and all required components in a self-contained single directory, which is then compressed into a self-extracting installer. Any user can then download such a standalone installer, extract it and have full access to the application without administrator rights. A similar result can be achieved with a Docker container [3]. - Documentation: Documentation is crucial for software adoption, including a user manual, product sheets with application examples, and guided instructions in workshop material. The manual should also be completed by a user community to help end-users answer the questions they will raise while using the software. Developers of the tools should be actively involved in tackling these questions [Srba et al., 2016]. 3. Application The GuidosToolbox Workbench (GWB) [Vogt et al., 2022] provides various generic image analysis modules as command line scripts on 64-bit Linux systems. In this section we use GWB to exemplify how we addressed the software provision points mentioned before. - Source code: in addition to the distribution-independent compiled executables we provide the plain text source code for all modules in a dedicated subdirectory of the application. - Software packaging: all modules are launched via customised Bash scripts and setup in the IDL programming language [4]. Because IDL provides its own set of highly efficient processing libraries, all scripts and required libraries can be stored in a single, distribution-independent application directory. Combined with customised packaging setup-files, this archive is then converted into distribution-specific packages for common Linux distributions. In addition, we provide a generic standalone installer using the makeself [5] archiving tool. The standalone installer can be used on any Linux distribution for either, system-wide installation, or installation in user space on restricted systems, i.e., under $HOME. All installer packages include two sample images and module-specific usage descriptions, aimed at generating sample output illustrating the features of each module. * Target platform: With its focus as a server-application, GWB is setup for the Linux OS, which can also be used on a regular desktop PC. The installation on cloud computing platforms, including an interface to upload/download personal data, greatly enlarges the outreach into the user community and allows usage of the software from any device having a Web browser and Internet access. A Jupyter [Kluyver, T., 2016] based application was developed within the SEPAL platform [2]. This application uses widgets and interactive displays to help the end-user provide personal data to the software. This application is developed in Python using the sepal-ui [8] framework, embedding a fully independent set of requirements. As the application is run using the voila dashboarding tool [QuantStack, 2019], the end-user is never confronted with the CLI, vastly improving the scope of potential end-user to non-IT experts. * Documentation: the project homepage [1] provides a brief overview and installation instructions. Highly detailed usage instructions are available on SEPAL for the command-line use [6] and the interactive Jupyter dashboard [7]. Conclusion: Software provision is an often overlooked yet critical component in software design. It comprises various aspects, which when addressed appropriately, can make a great impact in the promotion, outreach and acceptance of a software application.

uk: вхід без авторизації

name: Tackling the challenges of software provision


Lorenzo Amici

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: Monitoring landslide displacements through maximum cross-correlation of satellite images In the last years we have witnessed a huge increase in the availability of free and open multispectral, multitemporal and global coverage satellite imagery. At the same time, new open software tools for exploiting these images have arisen. Given the availability of short-revisiting time open satellite images, this study focuses on the analysis of satellite imagery using free and open source GIS software to identify displacements of single landslides. In particular, the Ruinon landslide was selected as the subject for this analysis. It is situated in Northern Lombardy, Italy, and it is one of the most active landslides of the Alps. The landslide is situated at the base of a Deep-seated Gravitational Slope Deformation, that affects the entire slope up to the summit at 3000 m a.s.l. Two major scarps can be identified: the upper one is a sub-vertical rock cliff of about 30 m in height, while the lower one is characterized by a more widespread debris cover. The general strategy employed in this work for obtaining landslide displacements in terms of direction and magnitude is to apply a local maximum cross-correlation on a multitemporal images stack. This was achieved using GRASS GIS and custom Python scripts. The images were selected from both the Sentinel-2 catalogue, which is free, and the Planet catalogue, available for free for research purposes. The main preprocessing steps are: creation of a suitable multi-temporal stack, clipping the satellite images to the selected AOI and applying cloud masking and an atmospheric correction; image co-registration to ensure that the images become spatially aligned so that any feature in one image overlaps as well as possible its footprint in all other images in the stack; histogram matching to transform one image so that the cumulative distribution function (CDF) of values in each band matches the CDF of bands in another image. The main processing is based on the Maximum Cross-Correlation method implemented on couples of images. The first image of the couple will be refererred to as reference image, and the second one as secondary image. This algorithm was previously applied to land cover changes (You et al., 2017) and to the movement of desert sand dunes (Oxoli et al., 2020). In the developed procedure, the processing phase starts by placing a window in the same position of both images. The window on the secondary image is then shifted in all directions, and a cross-correlation coefficient is computed for each of the shifts. The shifted window with the highest cross-correlation coefficient is selected, and a displacement vector is computed between the center pixel of the reference image window and the center pixel of the new shifted window of the secondary image. The outputs are shifts (in pixels) in X and Y directions which are actually the distances required to register the window of the secondary image with the one of the reference image. It is important to note that the smallest displacement that can be identified by this procedure is a displacement of 1 pixel, i.e. a displacement of 10 m if considering Sentinel-2 data. Therefore, smaller movements cannot be sensed by because of the native resolution of input satellite data. Secondly, errors can arise from the images having differences in terms of co-registration and histogram distribution, since this process highly relies on the images being as aligned and similar as possible. For monitoring the activity of the Ruinon landslide, two different sets of images were considered. The first one consists of one image per year in the period 2015-2020, with the idea to track the evolution of the landslide throughout the last few years. Since the landslide is situated in a mountainous region, it is often covered by clouds, and in the winter months by snow. Because of this, only the best image for each year was selected for the analysis. The other set is composed of three images, one per month, in the period July 2019 - September 2019, aiming at highlighting a large movement that took place in the summer of 2019. To compare and evaluate the performances of the cross-correlation approach, data coming from UAV surveys (provided by the local environmental agency ARPA Lombardia) of the landslide were used. At first, the results obtained with the procedure were compared with the output given by the procedure when applied to RGB images obtained from the surveys, which have a resolution of 1m. The two outputs were found to be very similar, both for the displacement magnitudes and directions. Secondly, photogrammetric point cloud comparisons created from the UAV observations in periods close to the considered ones for satellite monitoring were investigated. In particular, the displacement along the vertical axis was inspected, and accumulation zones were found in correspondence to the largest movements of the landslide detected from the algorithm. Because of this, the results were considered consistent with the data of the surveys. The increased availability of high-resolution multitemporal satellite imagery promotes the use of these images for monitoring purposes. While on the field monitoring can produce very accurate results, a procedure like the one applied in this work has the advantage to be more flexible, scalable and cost-effective than an analysis on the field. The experimental procedure developed in this work led to promising results, despite being a first stage approach to landslide monitoring applying the maximum cross-correlation method. Many approaches were considered, varying the main parameters of the procedure (adding or removing a classification phase, considering different intervals between satellite images, modifying the size of the moving window and others), and the whole process was progressively improved and refined until satisfactory results were achieved.

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

type: Organization

name: Open Source Geospatial Foundation (OSGeo)

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


uk: Комп'ютерні науки

description: In this section, we describe the main routines of the MSPA code with reference to the morphological image analysis operations they rely on with links to their implementation in the open source Morphological Image Analysis Library (MIAL) recently released on GitHub at github.com/ec-jrc/jeolib-miallib by the first author. All morphological image analysis operators at the basis of MSAP are described in [Soille, 2004]. We briefly present the main MSPA foreground classes with reference to source code of the main morphological function used to compute them: core, boundaries, islets, connectors, and branches. The actual pseudo-code will be added in the final version of this paper and will include details on the computation of all MSPA feature classes including those of connected components of background pixels. The underlying code in the C programming language is available on GitHub at github.com/ec-jrc/jeolib-miallib/blob/master/core/c/mspa.c # Performance The performance of the algorithm is evaluated on images of increasing size as well as for on-the-fly computation for interactive analysis and visualisation. We demonstrate experimentally that the complexity of the proposed implementation is linear. That is, the computational time increases linearly with the number of pixels. We also show that the algorithm can handle images up to 2^64 pixels. For example, a Global MSPA map of forest cover in equal area projection and with a pixel resolution of 100 meter (400,748 x 147,306 pixels) was processed on the JRC Big Data Analytics Platform [Soille et al. 2018] in 12 hours. Processing large images is very much needed to mitigate dependencies with regards to the image definition domain because pixel classes may depend on the observation domain. As for the on-the-fly computation for interactive analysis and exploratory visualisation based on Jupyter notebooks [De Marchi and Soille, 2019], we show that the proposed implementation is fast enough for integration in JupyterLab with on-the-fly computation in an area corresponding to the mapview area and at resolution matching its zoom level. A Voila dashboard is in preparation and will be available for demonstration at the conference. # Conclusion Morphological spatial pattern analysis has gained traction since its inception in 2008. For many years, we maintain a dedicated MSPA website with extensive documentation, various GIS extensions and a user-friendly provision of MSPA within the desktop application GTB and the server application GWB. The present open release of MSPA will further expand the potential user-community. We are in the process of making MSPA directly available in the pyjeo python package [Kempeneers et al., 2019], so that data scientists using python for their analysis will directly benefit from the MSPA open source release. Since MSPA is available through a library compiled in C, it can be easily integrated in other data science environments. We therefore expect the release of the MSPA code under an open source license to further boost its use for the analysis of geospatial patterns and indeed any other types of spatial patterns occurring in other scientific domains. # References - Soille, P., Vogt, P. "Morphological segmentation of binary patterns" (2009) Pattern Recognition Letters, doi: 10.1016/j.patrec.2008.10.015 - Soille, P. et al. 2018. "A versatile data-intensive computing platform for information retrieval from big geospatial data" Future Generat. Comput. Syst. doi: 10.1016/j.future.2017.11.007 - Ossola, A. et al. "Yards increase forest connectivity in urban landscapes" Landscape Ecol 10.1007/s10980-019-00923-7 - Julien Carlier et al. "Using open-source software and digital imagery to efficiently and objectively quantify cover density of an invasive alien plant species" Journal of Environmental Management doi: 10.1016/j.jenvman.2020.110519 - Victor Rincon et al. "Proposal of new Natura 2000 network boundaries in Spain based on the value of importance for biodiversity and connectivity analysis for its improvement" Ecological Indicators doi: 10.1016/j.ecolind.2021.108024 - Giuseppe Modica et al. "Implementation of multispecies ecological networks at the regional scale: analysis and multi-temporal assessment" Journal of Environmental Management, Volume 289, 2021, doi: 10.1016/j.jenvman.2021.112494 - Vogt, P. and Riitters, K. "GuidosToolbox: Universal digital image object analysis (2017) European Journal of Remote Sensing" doi: 10.1080/22797254.2017.1330650 - Peter Vogt et al. "GuidosToolbox Workbench: spatial analysis of raster maps for ecological applications" (2022) Ecography. doi: 10.1111/ecog.05864 - Soille, P. "Morphological Image Analysis: Principles and Applications" (2004). Springer, doi: 10.1007/978-3-662-05088-0

uk: вхід без авторизації

name: Morphological Spatial Pattern Analysis: Open Source Release


uk: Комп'ютерні науки

description: Development of Multi-dimensional Array Database Based Massive Satellite Information Processing and Analysis System: KIWI-Sat Information and communication technology (ICT) is mainly applied to finance, telecommunications, and public sectors. However, since the early 2010s, there have been efforts to apply ICT to various fields such as aerospace, life science, energy, and automobiles. Recently, artificial intelligence and big data technologies have also been applied in the aerospace field, among others. In the field of aerospace, earth observation attracts the most interest. Recently, the number of satellites for earth observation is increasing every year. As small satellites can be manufactured at low cost, the number of small satellite constellation for temporal and spatial resolution is increasing. urban change detection, disaster monitoring, and traffic analysis are typical applications. These applications will be increasing more. When performing earth observation using satellite images, it must process a large amount of satellite information in real time and analyze satellite images with artificial intelligence. These studies are being conducted in a variety of ways, and especially, the area of interest in this study is processing technology for storing and retrieving massive satellite images. As a technology for handling massive satellite information, a multi-dimensional array database plays an important role. Representative examples based on open source software are Rasdaman and SciDB. In 2018, we started developing KIWI-Sat, a system for processing and analyzing massive satellite information for especially supporting Korean satellite images such as KOMPSAT-2, KOMPSAT-3, KOMPSAT-5 etc. KIWI-Sat supports GeoTiff, HDF 5, and JP2 which are main data types of representative satellite images. It is mainly being developed to support Korean satellite images KOMPSAT-2, KOMPSAT-3, KOMPSAT-3A, KOMPSAT-5, GOCI, etc. also, overseas satellite images such as Sentinel 1A, Sentinel 1B, Sentinel 2B, SPOT, and PlanetScope also be supported. Because KIWI-Sat supports main data type’s satellite images, Other satellite images can be easily supported. KIWI-Sat is being developed using a number of open source software such as Rasdaman, Pytorch, Django, Mapbox etc. First, it uses Rasdaman to process massive Raster-based satellite information, Pytorch to process AI inference module, and Django and Mapbox for visualizing Satellite images and overlay etc. KIWI-Sat is mainly composed of five subsystems: 1) 'K-SDA(KIWI-Sat Data Access)', which processes satellite information based on Rasdaman, an array database, 2) 'K-SAA(KIWI-Sat AI Analysis)', which is in charge of AI-based satellite image analysis, 3) 'K-SVI(KIWI-Sat Visualization)' that visualizes satellite image based on map, 4) 'K-SUT(KIWI-Sat Utility)' that provides utility functions such as system resource monitoring and upload/download of original satellite image, 5) OpenAPI for satellite information access There is a 'K-SOA(KIWI-Sat OpenApi)' in charge. In this paper, KIWI-Sat will be mainly explained focusing on 'K-SDA', which processes massive satellite images, and 'K-SAA', which analyzes satellite images with artificial intelligence. First, K-SDA was developed using Rasdaman. In the early of development K-SDA, SciDB was used as database system, but as SciDB finished its open source policy. We adopted Rasdaman, a representative open source, as alternative database for processing satellite images. For this, function and performance comparison was performed, and Rasdaman has a good performance in the uploading original satellite to storing in Database. The search performance for the selected area was excellent in SciDB. Considering the real-time performance of recent satellite image analysis, it was confirmed that Rasdaman has an advantage. In processing satellite images, the multidimensional array database can handle storage and retrieval of array units with theSQL standard, and has many advantages as it supports massive raster data. Second, it is 'K-SAA' that is linked to the satellite image-based AI module. KIWI-Sat has a structure that can be easily linked with the inference code and parameters of the previously developed artificial intelligence module. It works with the 'K-SVI' stage by receiving the satellite image obtained from 'K-SDA' and transmitting the obtained result to the DJango web framework by executing the inference code. We installed a demo system at KARI to test and improve Kiwi-sat. KIWI-Sat was installed in Ubunt 20.04 LTS, and it stores KOMPSAT-2, KOMPSAT-3, KOMPSAT-3A, KOMPSAT-5, Planetscope satellite images to search for ROI for optical images and SAR images, AI interlocking, etc. Several tests are being carried out.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Lake Maggiore and the Ticino River are water bodies shared by Italy and Switzerland: they are important resources for drinking water, irrigation and hydroelectricity generation as well as for tourism and biodiversity. The cross-border character and the often conflicting needs of the different users make the shared management of this resource very complex, but of great importance. The `‘Parchi Verbano Ticino´’ project, funded by Regione Lombardia / EU – INTERREG Italia Svizzera 2014/2020, aims to study the effects of water levels of the lake on various environmental components with a particular focus on protected natural areas. The level of the lake is regulated by a dam located at the southern shore of the lake. In this framework, this study aims to analyse the effect of water level on bird migration by: 1) Calculate the inundated bird habitat using a simulation based on measured water level; 2) calculate the inundated habitat from Sentinel-1 remote sensing imagery 3) Use the flooded area derived from S1 as ground truth to validate the previous simulation 1). The study area is centerend around Bolle di Magadino (Switzerland, 8°51’56.90”E, 46°9’42.17”N, a protected wetland located on the north shore of lake Maggiore at the confluence with the Ticino river. The area is a recognized nesting and stopover site for birds, listed as a Ramsar Wetland of International Importance and as Important Bird and Biodiversity Area (IBA). We defined the habitats of interest using a vegetation map provided by Fondazione Bolle di Magadino. The vegetation types collected from a phytosociological field study were aggregated into ten land cover classes that described the habitat types and land use. The final habitat map covers an extent of 6.7 km², including the 1500 ha of wetland called Bolle di Magadino. Daily passage of migrant birds have been recorded at Magadino ringing station and since 2019, traditional net captures were coupled with an Avian Vertical-looking Radar In this study we focus on the following periods, during which bird monitoring systems were both deployed: P1: 2019-05-01– 2019-06-20; P2 2019-10-01–2020-02-20 and P3 2021-02-01– 2021-07-20. The lake level measured at the hydrological station of Locarno (CH) was used to determine the inundated area in GRASS GIS using ther.fill module (GRASS Development Team, 2022) and a DTM that included the lake bathymetry (cell size 0.5 m). The lake level fluctuated between 192.3 and 194.9 m.a.s.l. over the study period, with a minimum in April and May, when the waters are used to irrigate the rice fields downstream, and a maximum in late autumn. We used the Google Earth Engine Platform (GEE) (Gorelick et al., 2017) to extract Sentinel-1 Synthetic Aperture Radar (SAR) images (ESA, 2021) as they are suited for surface water mapping and not affected by cloud coverage (Ovakoglou et al., 2021). We used Edge Otsu Algorithm with terrain correction (Markert et al., 2020) to estimate the inundated areas of the collection of a total of 236 images for the three time periods when bird migration was also monitored. The calculation was implemented GEE with the approach described by Gorelick et al. (2017) calibrating the threshold for our study area, adapting the code provided by Open Geo Blog (2021). The inundated areas were then overlapped with the land use map in order to estimate the extent of the submerged vegetation over the three time period defined. The resolution of all maps was 10 m, except the DTM that has 0.5 m, the CRS used in this work were WGS84 in GEE and the local CRS GCS CH1903 for all the other analysis. The area covered with water, according to S1, varied between 103.9 and 471.7 ha (210+- 83.6), some of the surface is a permanent wetland so it is never completely dry. Each habitat was affected differently by the flooding: when the water was at its highest, croplands were completely inundated, grasslands and reeds were submerged for 80% of their extent whereas urban areas and infrastructures were not affected (less than 1% underwater). The flooded area calculated by filling the terrain model at the level of the ranged from 130.4 to 248.90 ha (208+- 48.6). The correlation between inundated areas obtained using r.fill in GRASS and S1 on the same dates was fair for P1 and P2, but not for P3, when the water of the lake were taken for irrigation, but the habitats were flooded by rainfall. An interpolation of the flooded from S1 is a more efficient way to obtain an estimate of the flooded habitat on a daily basis, that is necessary to study its effect on migratory birds. The results presented here will contribute to the definition sustainable management tools of water management of lake Maggiore taking into account the effect of lake level on biodiversity in general and on bird habitat in particular.

uk: вхід без авторизації

name: Effect of water level on bird habitat at lake Maggiore


uk: Комп'ютерні науки

description: Design and implementation of an Open-Source Web-GIS to manage the public works of Abruzzo Region: an example towards the digitalization of the management process of Public Administrations According to the goals of the European Communications "2030 Digital Compass: the European way for the Digital Decade" and “Open Source Software Strategy 2020 – 2023” regarding the digitalization and the use of the Open-Source solution inside the Public Administrations, this paper presents the approach followed for the realization of an Open-Source Web-GIS to transfer all the information assets related to the public works, that must be judged by the Regional Technical Administrative Committee (C.R.T.A). The developed Web-GIS consists of a platform to support the “Civil Engineering” authority of the Abruzzo Region in the management of the public works during their whole administrative process. In particular, the main aims of the Web-GIS are: - to manage in a unique shared geospatial database the public works, that must be judged by the C.R.T.A. of the Abruzzo Region; - to monitor the activities and the life-cycle of the public works; - to share information related to the public works both with other regional authority offices and with citizens. In general, the creation of a WebGIS starts from a project created on the client side which, in a subsequent phase, will be loaded on a server to allow the visualization, interaction and distribution of the information among multiple users at the same time. In this case, the creation of GIS project for the management of geo-referenced territorial and alphanumeric information for their description required a careful study of the needs of the “Civil Engineering” authority of the Abruzzo Region and a definition of the contents of the GIS platform, passing through the documentation and the archives to consult and implement in the GIS. Finally, the choice of the output to be presented was made, also in relation to the type of end-users that will have to manage (regional authority employees) and view (citizens) the published information. In order to properly design the requested Web-GIS application, as a first step the structure of the geodatabase has been designed locally into the Qgis software, one of the most famous open-source GIS software. Among the main geodatabase formats, the geopackage, an open, OGC (Open Geospatial Consortium) standards-based, platform-independent, portable, self-describing, compact format for transferring geospatial information, has been selected. The GeoPackage standard describes a set of conventions for storing it within a SQLite database. The geopackage format has been selected considering the geometric entities of the public works that must be stored within the database, together with their attributes, that consist of points, multi-lines, and multi-polygons elements. In fact, the public works that must be judged by the C.R.T.A. of the Abruzzo Region can be buildings (strategic or scholar buildings or healthcare constructions), road works, hydraulic works, or land defense. These public works, as required by the Abruzzo Region, do not have an exact type of geometry but, depending on its type and the type of project can be represented in the most appropriate way to understand the intervention itself. The geopackage format allows storing all the information related to the public work in a single file, simplifying their management. The use of QGIS solution was made keeping in mind the idea of using LizMap software to publish directly the contents of the geodatabase designed locally in a simple way. Lizmap is an open source software designed by 3Liz, a service company revolving around QGIS software, which facilitates the publishing of web mapping applications from QGIS Desktop using QGIS Server as Map Server. Another important aspect for this choice consists in the fact that QGIS environment is well-known among the public authorities employees and this simplified the interaction during the design phase of the database. This allows verifying if the structure of the designed database satisfies all the requirements of the “Civil Engineering” authority of the Abruzzo Region. In addition, in the future, the “Civil Engineering” authority of the Abruzzo Region will be able to modify or update autonomously the public works that will be subject to the judgment of the Regional Technical Administrative Committee (C.R.T.A), directly in QGIS Desktop. After the realization of the project in Qgis Desktop, in order to share the map online, the Lizmap plugin inside Qgis Desktop was used to configure the publishing options, i.e. scales, base layers, metadata, etc. . Once the file configuration is compiled, it is possible to synchronize the working folder with the Qgis Server. When synchronisation is complete, the QGIS project can also be accessed on the Internet, through the Lizmap Web Client application using a web browser. Lizmap Web Client is installed on QGIS Server in order to insert projects and it allows to configure the project and the displayed web page. All this step can be performed locally, (intranet network) and finally, the project and the created settings files have to be transferred to the region geoportal of Abruzzo Region. In conclusion, the use of Lizmap to transfer Qgis Desktop projects on the web represents a good solution to move Public Administration towards the use of Open-source solutions and towards the digitalization procedures required by the European Commission. In addition, this tool had the purpose of ensuring maximum transparency to citizens who, although not insiders, can access the geoportal to see how the funds allocated by the Region, the Italian Nation, and the European Community are distributed and spent.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Using Sentinel 2 images to quantify agricultural encroachment in Burkina Faso’s protected livestock reserves In many parts of Burkina Faso, competition over land use has increased tensions and often conflicts between farming and herding communities. Allocating land for farming or grazing is increasingly perceived as a zero-sum calculation among these communities. As a response, the government of Burkina Faso created “Pastoral Zones” across the country as reserves for livestock herders where animals could graze without the risk of entering cropland. Farming in these areas is typically prohibited unless done by herders residing within the reserve. However, farms have appeared in pastoral zones over the years, reducing resources available to herders and exacerbating already fraught tensions between herding and farming communities (Nébie et al 2019). This study uses Sentinel 2 imagery to quantify to what extent agricultural growth is encroaching on two such pastoral zones in Southern Burkina Faso, Niassa and Sondré-Est. This study found a significant growth of agricultural cultivation in both zones between the period of 2016 and 2021. To map agricultural growth, Sentinel 2 imagery was used in Google Earth Engine (GEE). Reproducibility and accessibility were prioritized, hence the use of a free platform and open EO data was prioritised. Google Earth Engine stood out as an accessible cloud platform to easily access the imagery and run the analysis (Gorelick et al, 2017). To visualise agricultural areas, the “3 Period Timescan” (3PTS) Method was employed. This method uses a series of NDVI Images from the Sentinel 2 satellite throughout a growing season to isolate areas of active cultivation. This product consists of a Red-Green-Blue composite of Sentinel-2 Images where the red band represents the maximum NDVI value during the first period of the growing season, the green the maximum NDVI in the middle, and the blue the maximum NDVI at the end. As a result, the method is able to create a seasonal time-series profile of NDVI. A single NDVI product provides an indication of vegetation presence on a given date, but it is not sufficient to distinguish croplands from other types of vegetation. Croplands are thus identified by their temporal evolution of NDVI values throughout the different phases of the agricultural season: photosynthetic activity of crops is low during the planting period (“beginning of the season”, approximated by 15th June to 1st August), increases during the growing phase (“middle”, 2nd August to 1st September) until reaching a maximum value right before the harvest; once harvested, NDVI values decrease drastically (“end of season”, 2nd September to 15th October). Thus, the approach employed for investigating cropland change considers maximum NDVI values for those three separate subperiods of the agricultural season and aggregates this information into a higher-level product, a RGB color composite so-called 3-Period TimeScan, reflecting the vegetation temporal evolution during the agricultural period, at 10m resolution (Boudinaud and Orenstein, 2021). 3PTS images allow for a user-friendly method to visually identify cropland. Cropland pixels from 3PTS images, when visualized in GEE appear in a dark blue due to the sharp changes from the 2nd and 3rd periods of the time series. This contrasts well with natural vegetation, which has a smoother temporal profile with a noticeable peak in the 2nd period and thus appears greener or a much lighter blue. Forests, due to their high NDVI values throughout the entire growing season appear in white, due to the saturation of all 3 bands. Bare soil, with it’s low NDVI values throughout all 3 periods appears as nearly black pixels. Rather than machine learning, visual identification was the preferred method of identification due to the relatively small size of each pastoral zone. The time needed to prepare training data and clean the results of a supervised classification would have exceeded the time to manually identify each area of cropland. As a result, once the images were treated by GEE, they were manually traced within QGIS. The 3PTS script, originally made for GEE was then translated to run in PyQGIS. Once run, the script created a raster image for each year’s growing season in the archive (2016-2021) and polygons were traced over each visualised cluster of cropland. The total surface area of all polygons was then calculated for each year. A github repository contains both the PyQGIS and GEE code and can be run with no prerequisites. The results of the study indicate a significant increase in cultivation in both zones between 2016 and 2021. For Sondré Est, this change amounted to 40% and 160% for Niassa.Curiously, the largest increase in cultivation seems to occur between 2016 and 2017. This is especially so for Niassa. Nonetheless, increases in cultivation increased with each passing year until the present year of 2021. A number of these fields are suspected to be encroachments, given their proximity to the border of the zone and that many are contiguous with the agricultural fields outside of the zone’s borders. However, it is estimated that a number of the fields are the result of the zones’ resident herders planting fodder or other cereals. The latter assumption is made based on the location of the fields in question (far from the borders of the reserves) and their proximity to permanent structures in the reserves (habitations, wells or park buildings).

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description: Generalization is one of the fundamentals of scientific research. In the context of spatial information, generalization needs to allow for finding common properties but also for spatial contiguity. Therefore, such generalization is often made through regionalization - partitioning of space into spatial clusters or regions. This process is vital for environmental studies, where many patterns and processes are autocorrelated spatially. Examples of regionalizations include delineation of ecoregions, detection of homogeneous zones for precision agriculture, definition of climate regions, and so on. Traditionally spatial generalization was performed manually, often based on a compilation of pre-existing, independently conducted studies. This approach lack of quantitative framework, and thus no systematic checks, modifications or objective updates are possible. Currently, the abundance of remote sensing spatial data, such as satellite imagery, gridded climate data, or land cover maps, allows fast extraction of relevant spatial information on regional and global scales, making possible studies rooted in a clear quantitative framework. Such data, however, still requires spatially-aware generalization to formulate general concepts or claims. Remote sensing data stores information as a set of raster cells, where a single cell is unaware of its spatial context. This is often not enough to understand underlying objects or processes. (Geographic) object-based image analysis (OBIA) (Blaschke 2010) is frequently applied to resolve this issue. It is an approach to partition space consisting of raster cells into homogeneous objects and thus make spatial regionalization possible. Several generalization techniques were developed for OBIA, including a superpixels approach that proved to perform best for image processing and remote sensing data analysis (Csillik 2017). The main idea of superpixels is to create connected groupings of cells with similar values (Ren and Malik 2003; Achanta et al. 2012). Each superpixel represents a desired level of homogeneity while at the same time maintaining spatial structures. Superpixels also carry more information than each cell alone, and thus they can speed up the subsequent processing efforts (Ren and Malik 2003; Achanta et al. 2012). The original superpixels algorithm has, however, two major drawbacks for spatial data problems other than RGB images. Firstly, the algorithm uses the Euclidean distance, which is adequate in many cases, such as RGB images. However, it limits the possible usability for environmental datasets – Euclidean distance is not suitable for many types of spatial raster data (e.g., categorical rasters) and has undesirable properties for multi-dimensional data (e.g., a set of monthly climate data), where the results based on Euclidean distance contradict human intuition (Aggarwal, Hinneburg, and Keim 2001). Secondly, the superpixels technique does not result in regions per se but rather over-segmentation – some spatial objects/regions could be represented by one superpixel, while others could consist of many very similar superpixels. Our preliminary results presented during the GIScience 2021 conference (Nowosad and Stepinski 2021) provide a basis for using other distance measures to create superpixels. The proposed extension can also be used for various scenarios, such as creating regions of similar multi-dimensional spatial and temporal patterns or similarly ranked areas. The extension is also already available as an open-source software in the form of an R package. The supercells package has extensive documentation in the form of a help file and additional vignettes that can be found, together with its installation instructions, at https://jakubnowosad.com/supercells/. The second issue is, however, still not resolved. Many clustering methods exist that could be used for merging similar connected superpixels, including traditional ones such as hierarchical clustering and spatial-aware ones such as SKATER or REDCAP. Wang et al. (2018) developed a REDCAP-based workflow for merging superpixels, which showed good image results and outperformed similar techniques; however, their work was based on the original superpixels algorithm and thus used Euclidean distance on 3-dimensional RGB images only. Additionally, it could be worth testing how good modern unsupervised machine learning techniques would perform in this task. Our main goal is to present the work in progress related to developing a robust method for merging superpixels and thus creating high-quality regionalization. We will test clustering/grouping methods based on three main criteria: accuracy, universality, and computational performance. Accuracy will be obtained based on the resulting regions’ internal homogeneity and their isolation compared to the neighbors. Universality will be tested on several datasets to check if the method works for various scenarios, including RGB images, categorical rasters, spatial time-series, etc. The computational performance will be evaluated based on the time needed for each method’s calculation and their use of computer resources.

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name: A method for universal superpixels-based regionalization (preliminary results)


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description: The Covid-19 outbreak has greatly impacted society behaviours fostering proximity tourism and valorising the social role of peri-urban natural protected areas as key locations for outdoor activities [1]. This shift in habits calls for an adaptation in the next years of the offerings and management of these areas to respond to users' expectations of positive experience opportunities in near-by locations [2]. In the context of digital transformation and peri-urban protected areas, this research investigates the contribution that open geospatial technologies can provide in the creation of new economic, social and cultural values to propose solutions and identify gaps or open issues. The adopted methodology is the “case study approach”, in which real cases are used to design, develop, implement, collect and analyse data to extrapolate information that contributes to a deeper knowledge of the matter. This research is framed in the context of the Interreg INSUBRI.PARKS and among the project’s parks the selected case studies for technological testing are the Parco Gole della Breggia and the Collina del Penz. While being two natural protected areas closely located in the southern part of Switzerland, in the Canton Ticino, they greatly differ for in-place management structure, available offers and users’ type and therefore represents different needs. From the discussion with local tourism organisations and park administrators we have identified three specific aspects that are of particular concern: (a) the creation of 3D digital products, (b) the monitoring of touristic fluxes and (c) the conduction of parks management activities. This work presents the intermediate results of the development and testing of different selected solutions which describes the approach, the issues and the potential of explored solutions with respect to the open source software. 3D digital products - In addition to a more traditional use for conservation scopes and activity planning [3], 3D models can be used to offer positive experiences thanks to an enhanced understanding of specific intangible aspects [4]. For example, in the case study of the Parco Gole della Breggia, it might be difficult for a tourist to fully realise the extent of the anthropic impacts on nature. The area is geologically relevant for the visible calcareous formation hundreds of millions of years old. From 1961 to 2003 the Breggia shores hosted a large cement plant that strongly modified the territory. Today, only a small part of the plant is still in place as a testimonial of the anthropic impacts and element of industrial archeology. To support the perception of the real antropic impact we decide to implement three digital models representing the territory at three key epochs: before the cement plant construction, at the maximum expansion of the plant and at the present state. The present state model can be created by means of laser scanning and photogrammetric surveys while the other two can be realised by digitising historical maps, technical plans and historical pictures. The investigation identified a workflow based on the evaluation of CloudCompare, Riegl RiSCAN Pro and Cyclone 3DR for 3D survey, Regard 3D; GRASS, QGIS and ESRI ArcGIS Pro for spatial data collection and management; Blender, AutoCAD 3D, Rhinoceros and Sketchup for vector modelling of spatial elements; Nubigon and Potree for a better graphical representation and further web dissemination of the results. Monitoring of tourtistic fluxes - The monitoring of touristic flux is important for the correct management of the natural protected areas to assure the Tourism Carrying Capacity (TCC) of trails is not exceeded, to assure adequate economic resources are allocated to maintain the assets, to understand the tourist behaviours and consequently develop strategies and plans to maximise the touristic value of the park [5]. While different solutions were proposed to this scope (accelerometers on iron plates and proximity radar sensors) it is important to capture specific tourist characteristics, like for example the presence of animals, the direction and the use of bicycles or cars. To this aim, Machine Learning models can help to automate the collection of such information by image analyses and object detection [6]. The present paper presents a fully open prototype to implement and deploy a real-time tourist monitoring system composed of: sensing device, data communication, data management and data visualisation platform. The system includes the usage of the YOLO open source solutions for image recognition, the OGC SoS open standard and the istSOS implementation for data management and sharing and the open source Grafana software for data visualisation and analysis. The results from the testing of the prototype in two locations for a period of 6 months is presented supplemented with field validation data. Digital Management of Protected areas - Protected areas are currently managed using different tools that are very often scarcely digitised. This approach does not exploit the potentiality of digitalization and does not foster the capacity to extract insights from data. While different open source project management software exists, none is specifically designed to address natural area management processes. For this reason a novel application, based on an open source platform has been developed and implemented. The cloud solution named Park Asset Management (PAM) is based on the usage of PostgreSQL/PostGIS and OpenLayers in conjunction with KeyCloak authorization platform, the Hasura GraphQL Engine integrated in the Vue.js framework. The containerized application offers the following features: park asset management and information sharing, working task management and execution, rentals management, notification management, offering a map interface and a more classic calendar and table views. This platform enables insights extraction like maintenance cost of itineraries, income from location rental by months and by years, cost and time required to replace items and the frequency of occurrence of events.

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name: Tourism, natural protected areas and Open Source Geospatial technologies


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description: 1. INTRODUCTION In a new initiative to deliver higher-quality data and support improved geospatial analysis, the U.S. Geological Survey (USGS) is upgrading the elevation and hydrography datasets into the 3D National Topography Model (3DNTM), which will include fully integrated hydrography and elevation. The USGS 3D Elevation Program (3DEP) recently completed acquisition of interferometric synthetic aperture radar (IfSAR) elevation data at 5-meter spatial resolution for Alaska (USGS, 2022). Other parts of the United States are being mapped at higher resolution with lidar-derived elevation data. Under the 3DNTM, new hydrography data are acquired through methods that derive or extract the features directly from best available 3DEP elevation data to ensure proper integration of the hydrography and elevation layers. By applying specifications for deriving 1:24,000 or larger scale hydrography from high resolution elevation data (Archuleta and Terziott, 2020; Terziotti and Archuleta, 2020), a tenfold increase in the number of features in the National Hydrography Dataset (NHD) is expected. Consequently, highly automated machine learning methods to extract and validate the hydrography data collection are being investigated. Xu et al. (2021) demonstrated that the U-net fully convolutional neural network (Ronneberger, Fischer, and Brox, 2015) is capable of extracting hydrography from lidar elevation data with 80 to 90 percent accuracy. Stanislawski et al. (2021) applied a similar U-net model using several IfSAR and IfSAR-derived input layers to predict hydrography for a 50-watershed study area in northcentral Alaska, where 68 percent average F1-score accuracies were achieved on test watersheds. Further work to refine U-net predictions of hydrography using IfSAR for the same 50-watershed area in Alaska achieved average F1-scores for test watershed of better than 80 percent (Stanislawski et al., 2022). Research presented in this paper builds upon this earlier work by testing transfer learning methods and scaling-up U-net predictions of hydrography from IfSAR for other areas of Alaska using workflows in high-performance computing environment. 2. METHODS A workflow was developed to automate downloads and processing of IfSAR-derived tiles of digital elevation model (DEM), digital terrain model (DTM), and orthorectified intensity (ORI) data for user-selected watersheds from the 3DEP database. The workflow mosaics common tiles and derives several raster data layers from the DEM that are related to surface hydrology, such as topographic position index and shallow water channel depth. Overall, seventeen data layers are generated and coordinated with identical raster projection systems. The layers were used in U-net modelling for predicting hydrography for the 50-watershed Kobuk River study area (Stanislawski et al., 2022). In this study a transfer learning process begins with the Kobuk River U-net model and subsequently includes additional training data from outside the Kobuk area. Hydrography predictions are then generated from the transfer learning model and assessed. Several levels of refinements to training data are tested and the accuracy of predictions are assessed. Reference data consist of vector hydrography features derived by USGS contractors. The data processing workflows are implemented with Python, linux shell scripts, and opensource software libraries such as the Geospatial Data Abstraction Library (GDAL). Neural network modelling is implemented through TensorFlow, and data processing is completed on a 12-node linux cluster and through the GPU nodes of the USGS Tallgrass computing facilities (https://hpcportal.cr.usgs.gov/hpc-user-docs/Tallgrass/Overview.html). 3. DISCUSSION Mapping hydrography for the state of Alaska is a daunting task, given its vast area and terrain that is difficult to navigate. Big challenges with large high-quality datasets are well suited to take advantage of recent advancements in neural networks (Usery et al., 2021). This research demonstrates the tremendous potential to improve and speed up mapping of surface water features in Alaska, and elsewhere in the world having challenging terrain and limited resources. Reported accuracy scores measure how well a machine can reproduce hydrography generated with meticulous editing by numerous subject matter experts. It is not a score of how well the surface water features are mapped by the model. The human factor in contemporary broad scale mapping efforts cannot be ignored and warrants consideration as a source of uncertainty in the related accuracy metrics. How well the maps fit what is on the ground can only be definitively confirmed by being on the ground at any given point in time, as hydrologic conditions are constantly in flux. Thus, the work here could be used as an aid to human cartographers in their efforts to interpret what is important to the map user. This work could also benefit change detection efforts. As new and better elevation data are collected, automated strategies such as the model presented here could be used to identify regions with significant changes in surface water distribution. This type of automation would be valuable to maintain an accurate national map over time and help address the numerous challenges that society faces related to hydrology.

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name: Scaling-up deep learning predictions of hydrography from IFSAR data in Alaska


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description: Mapping Mt. Ushba – How to create a high-quality topographic map from open data using free software Mt. Ushba is situated in the Greater Caucasus in Georgia, next to the Russian border. With its nearly symmetrical double peak appearance, it is iconic and a symbol of the historic Svaneti region in Georgia, famous for its mountains, botany, and century-old defense towers. Svaneti is becoming an increasingly popular tourist destination in summer and winter. Therefore, the German Alpine Club is interested in providing a new map for this region, which will be produced by the Institute of Cartography of the TU Dresden. In the age of open data, it is consequential that OpenStreetMap will be an essential source of the new map. It should make the project more sustainable and inspire people to use free and open-source software for map production. One basis of each topographic or touristic map is fieldwork, which means organized mapping and editing with OpenStreetMap aiming to verify and to complement map content and coverage[1], carried out by the Institute of Cartography in Mestia (Georgia) in the summer of 2021. Preparing for this work, a comparison with older maps was conducted to identify possible shortcomings and errors in the data. A draft was created using OpenStreetMap and the SRTM elevation model, preparing for the fieldwork. It helped to evaluate the current state of the data, gave a first impression of the mapping area, and was an ostensive basis for data capturing in field. A field book was produced for each participant, containing the map draft as an atlas and information on which data should be collected and which the specific attributes were required. Finally, the data was contributed to OpenStreetMap, and from there, the draft was updated again. In the case of land cover, creating an own classification seemed beneficial in distinguishing between typical vegetation classes in a high mountain area. Showing the vegetation in detail is a feature of Alpine Club map, but using OpenStreetMap data would not detailed enough. In addition, a land cover classification based on remote sensing data is more reliable and ensures better consistent results compared to individual contributions from users with different previous knowledge. Open remote sensing data from the Landsat and Sentinel programs offer good sources for such a task and are also used to monitor the glaciers in this area[II]. R is used as an analysis platform. It is possible to classify rock, glaciers, and specific vegetation types such as alpine rose or open birch stands. For identifying the vegetation, representative examples were collected during the fieldwork by entering them in the atlas and taking sample photographs. Another essential part of a topographic map for a high mountain area map is a good terrain visualization. The SRTM model is beneficial but not detailed enough to create rock depictions, which will be automatically derived by the Piotr tool[iii]. Planet Labs Inc provided high-resolution Rapid Eye and their Dove satellites imagery, suitable for creating a digital elevation model with a spatial resolution of approximately ten meters by applying stereo photogrammetry methods using the AMES Stereo Pipeline[iv]. The result enables a much more precise and understandable representation of the terrain. The terrain points were recorded with special standard GPS devices, the Garmin GPSMAP 66sr, which stores the raw observations for two frequencies. Accuracies in the range of around 0.1 meters[v] can be achieved using professional GNSS software. In order to produce the final topographic map, it is necessary to combine all data components to represent the area around Mt. Ushba. In a first step, the updated OpenStreetMap data is imported into a PostgreSQL database with PostGIS extension. In a second step, an automated generalization is carried out for the selected target scale of 1:33,000, particularly schema transformation, aggregation, and simplification. For the visualization, QGIS is utilized: one project containing all layers with their visualizations served as WMS. It enables team members to view the current map and access all the data without storing it individually locally on their computer. Additional web mapping services were set up to provide georeferenced scans of other available maps of the region to enable a comparison and evaluation of the new derived topographic map product. Because of the wide range of tasks, the work is split into several work packages and ongoing subprojects. Students' master theses within the International Cartography Master program – a cooperate offer of TU Dresden, TU München, TU Wien, and University Twente contributed significantly to the project by implementing and evaluating selected methods required for the map derivation.

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description: OpenStreetMap (OSM) has evolved to one of the most used geographic databases. It is a major knowledge source for many geographic topics addressed by researchers, professionals and the general public. To satisfy these diverse needs and capabilities, the linked communities surrounded the project with an ever growing ecosystem of analyses tools (e.g. OSM Contributors, 2022). The most prominent analysis topic is data quality (Senaratne et al. 2015) where e.g. intrinsic indicators are used to estimate completeness (Brückner et al. 2021). Furthermore the community is also interested in insights such as leader-boards or activity reports (e.g. Neis, 2022). In recent years analyses have also more and more shifted towards doing large scale analyses (e.g. Herfort et al. 2021). This diversity of tools can be a challenge for data users who will find themselves in a universe of highly specialised or complex tools using different programming languages, platforms, interfaces, output formats etc. While there have been efforts to provide users with higher level data insight and analyses platforms, these still mostly concentrate on or are limited to certain topics or regions. To our knowledge no tool exists to analyse and combine topic independent aspects of the data at the highest possible resolution: single OSM elements. The presented software (available at https://gitlab.gistools.geog.uni-heidelberg.de/giscience/ideal-vgi/osm-element-vectorisation) sets out to bridge this gap by integrating multiple aspects of the OSM ecosystem into one workflow that allows the quantitative assessment of selected OSM elements or all elements in a defined region. This enables new insights in a formalised and easy to use manner. The result is a vectorisation of single OSM elements (sometimes also called embedding or feature construction). By producing a machine readable result, the tool can be used for manual data investigations as well as for the ever growing field of machine learning where it can be linked to a range of labels. # Software The tool is centred around a python package providing a command line interface suitable also for novice users. It draws on other sources where necessary such as POST-requests and Java. Further data processing is done using the R scripting language while all data is stored in a PostGIS enhanced PostgreSQL database and can be exported automatically to .csv-files. The AGPL v3 license as well as the code structure and documentation enable others to also use it as a framework to implement their own analyses logic in combination with the current procedure. A default setup using Docker is provided for fast installation including a minimal example. The tool is fully functional and in use in our current research. Yet, it is under active development towards a web interface and functionality extensions. While the development was made with land-use and land-cover (LULC) information in mind, the tool can be seamlessly applied to any polygonal OSM data such as buildings and also supports linear and point data. The tool is resilient towards missing data and can recover from many common issues like failed connections. The backend remains in a sane state throughout the workflow and error messages enable the user to adapt to any failures and simply rerun the tool that will automatically pick up from the last savepoint. Benchmarks have shown that the tool is capable of processing around 1k elements per hour making it a suitable tool for larger analyses of custom regions or element sets.Out of the endless number of possible data aspects, a set of 32 are currently available for the user to choose. These cover aspects concerning the element itself (e.g. object area, geometric complexity and object age) but also the surrounding data (e.g. the mapping saturation and community activeness) and the editors (e.g. their experience, localness or editing software used). # Application To prove its potential, the tool is applied to a set of 1k randomly selected OSM LULC elements. We picked OSM LULC as an example as it has been shown to be valuable for applications such as earth surface monitoring. The results provide a status report on the already available data to the OSM community. It further enables a more informed planning of future activities like organised mapping or data curation efforts and enables data consumers to make informed decisions on data usage by answering the question: What is OSM LULC made of? First, three exemplary hypotheses were tested statistically on a global as well as a continental scale to analyse the triangular relation between elements' size, age and location in terms of population density. In a second step, k-means clustering was used to identify clusters based on the properties of the OSM objects. Before clustering, the data were standardised and stripped of any geographic information as we were hypothesising that the different clusters might be linked to different geographic regions. The results showed that larger objects were more frequently encountered in regions with a lower population density due to the 'natural' factor of higher fragmentation in these areas. Yet, the effect was surprisingly small on a global scale. A general mapping order where areas of high population density are mapped before lower population density areas could not be confirmed globally. This may be caused by a complex interaction between several indicators and regional tendencies, that remains to be fully understood. Regional tendencies were shown e.g. for the age of objects with North America and Europe containing older objects than Africa and Asia. The five k-means clusters formed interesting groups worth further investigation. For example the North American lakes or the complex European elements were each detected as distinct clusters by the algorithm.

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name: OpenStreetMap Element Vectorisation - A tool for high resolution data insights and its usability in the land-use and land-cover domain


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description: Complex quarry districts like Apuan Alps’ marble quarries require remotely sensed high resolution data for soil consumption monitoring over the years: extractive activities lead to environmental challenges that require accurate environmental controls issued by the Tuscan Regional Environmental Agency (ARPAT). The Regional Environmental Information System office (SIRA) over the last 5 and 10 years has developed methods and techniques suitable for both 2D and 3D soil consumption monitoring by using free aerial and satellite images and Open Source Geo-spatial Software for data processing and data dissemination useful in controls’ planning and management. Aerial images and LiDAR acquisition, satellite data, RPAS acquisitions have been tested in order to evaluate their suitability in deriving both 2D and 3D indicators with proper resolution to address required spatial-temporal constraints, i.e. yearly monitoring of high resolution changes (spatial resolution between 50cm and 1m). Due to the size of the Area of Interest (AOI) of the Carrara basins, up to 2.5km x 2.5km, stereo satellite and aerial images can be used to obtain precise terrain models by photogrammetric reconstruction useful in 3D soil consumption monitoring, while middle-resolution (10m) multi-spectral satellite images and high-resolution aerial images (50cm-1m) can be used in 2D soil consumption monitoring and quarries’ area regulations by public bodies (natural soil loss, exhausted areas restorations, debris removals and new disposals). Open-access Sentinel-2 multi-spectral satellite images with 10m of spatial resolution have been used to assess coverage changes; the results have been subsequently refined by manual interpretation over 5 years (2016-2021). Both semi-automatic methods based on spectral distances and machine learning techniques have been used to identify areas affected by extraction activities in QGIS 3.x environment over Sentinel-2 images. Free OGC Web Map Services (WMS) made available by the Tuscan Regional Information System have been used to assess changes highlighted by semi-automatic methods: aerial high-resolution images between 2010 and 2019 have been evaluated by visual photointerpretation, allowing to extend to 10 years the 2D soil consumption assessment over the whole area. Comparison of highlighted 2D changes to regulated areas like mapped debris disposals and quarries’ property limits have been used to check proper developments of extraction activities and proper environmental debris management. In turn, 3D changes have been tracked by comparison of 2009 and 2017 free aerial LiDAR data made available for download by the Tuscan Regional Information System, integrated with two stereo models obtained from 2020 and 2022 Pléiades satellite high resolution images (new acquisitions) freely granted by ESA following Project Proposal id 61779 (“Quarry activity monitoring in Apuan Alps”). Stereo satellite B/W images with 50cm of spatial resolution have been processed by using Open Source stereo processing pipelines in Docker virtual environments, obtaining high precision digital surface models (height precision around 1m) after vegetation filtering. 3D changes detected over the years by elevation algebraic comparison, performed in QGIS 3.x environment, highlight quarries characterized by intense extraction activities (extracted marble blocks, characterized by positive quotas differences) and quarry area management (debris disposing and service infrastructure building, characterized by negative quotas differences). The combined usage of both 2D and 3D changes’ indicators can be challenging in term of proper representation of soil consumption dynamics over the years: while decision makers need a quick and easy access to both 2D and 3D data, web technologies suitable for a proper representation have been developed in very different contexts, making their integration quite complex. While a ‘classical’ 2D webgis client Openlayers or Leaflet-based can be enough to highlight 2D changes and – with some limitations – 3D changes as elevation differences, a ‘true’ 3D visualization environment must be set to track ongoing extraction activities aiming to assess both (a) compliance to authorized extraction plans by public bodies and (b) proper debris management in quarry areas. In addition, 3D web viewers are mainly targeted to represents point clouds or CAD drawings, making very difficult the integration of 2D, 2.5D (Terrain Models) and 3D (extracted volumes) data. A dual 2D/3D webgis client have been developed for proper representation of 2D/3D spatial indicators of ongoing extraction activities in the Carrara marble basin: high resolution images have been served as tiled data, while 2D/3D spatial indicators are served as static and/or tiled vector data. Open-Source libraries have used in data processing, serving and representation inside a map interface. For each quarry included in the Carrara basing, both area limitations and authorized areas for extraction activities have been superimposed over the spatial indicator layers, thus allowing users to easily locate areas subjected to intense extraction activities and to evaluate compliance to sustainability plans and environmental management prescriptions issued by public bodies. 2D and 3D indicators are in progress to be used in prioritizing environmental controls’ planning: this novel application would require a proper scoring system based on the degree of compliance to both environmental management prescriptions and performances mainly in the field of quarry and marble slurry waste management.

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name: 2D/3D soil consumption tracking in a marble quarry district


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description: opic modelling is a branch of Natural Language Processing that deals with the discovery of conversation topics in a document corpus. In social media, it translates into aggregating posts into topics of conversation and observing how these topics evolve over time (hence the “dynamic” adjective [Murakami, 2021]). Conveying the results of topic modelling to an analyst is challenging since the topics often do not lend themselves naturally to meaningful labelling, where relationships between them can involve hundreds of dimensions. Furthermore, the popularity of topics is itself subject to change over time. In this paper, we propose a spatialization technique based on open-source software that reduces the intrinsic complexity of dynamic topic modelling output to familiar topographic objects, namely: ridges, valleys, and peaks. This offers new possibilities for understanding complex relationships that change over time, that overcomes issues with traditional topic modelling visualisation approaches such as network graphs [Karpovich, 2017]. Spatialization [Fabrikant, 2017], a technique that uses spatial metaphors to aid cognitive tasks, has been a research field since the early ‘90s. It can be used to make sense of vast amounts of information by reducing them to a physical landscape. In this work, we consider spatialization of topics in a 3D space where the X-axis is the similarity of topics posted on the same day, the Y-axis is the similarity of topics across time and how their relationships evolve, and the Z-axis is a measure of the topic popularity. With this approach, a topic is therefore reduced to a single point in a 3D space, and the interpolated surface constructed out of these points becomes a landscape with peaks, ridges, and valleys. More precisely, the “valleys” represent less popular topics, while “peaks” are the more popular ones and flat surfaces indicate the average topics. Our team is working on the Australian Data Observatory project, which has been collecting tweets and other social media posts (Instagram, Reddit, YouTube, Flickr, etc)) related to Australia for the last 12 months. Through the use of the new Twitter academic license, the project is harvesting 10s of millions of tweets per month. The social media posts are stored and analyzed daily using the deep learning BERTopic package. The BERTopic output is then stored and served through a ReST API, which is used by different clients (at present these are Jupyter notebooks and a web application). The intended audience of our platform is composed of the average topics domain researchers including social scientists, linguists, and data journalists. The goal is to support big data exploration at scale and overcome the smaller scale cottage industry of social media research that has hitherto been the norm in academia in Australia Topic modelling is often presented using 2D visualizations, such as circles with size proportional to topic popularity and position related to the similarity between topics, The dynamic (temporal) aspect of topic evolution is typically shown with animations that show how topics morph into different ones and wax and wane in popularity or it is ignored completely and researchers just use static topic modelling visualisations. here is merit in trying a different approach for dynamic topic visualisation: namely, to map the social media landscape to the physical one, as this metaphor allows the simultaneous appreciation of time, topic similarity, and popularity while allowing -via zoom operations- the aggregation/disaggregation of topics into bigger/smaller cluster of posts. This 3D landscape naturally aids the end-user in understanding complex highly dimensional data at a scale and volume that would otherwise be impossible. The formation of islands, archipelagos, mountain ranges or valleys related to mainstream topics such as Covid, vaccination, lockdown, through to geopolitical events such as the invasion of Ukraine provides a finger on the pulse of what is being discussed at scale by the broader population across the social media landscape. This approach is currently realised using a web application that enables the “topographic” exploration of the topic landscape with functions to improve the user experience in the areas of topic labelling and inter-topic distance. There are a few criticalities in the proposed visualization: distance between topics has to be drastically reduced in dimensionality from the ones provided by the Deep Learning model to just one (the X-axis); the Y-axis (time) has to be put in relation to a completely different measure (distance between topics) to make it amenable to an interpolation; topic popularity (the Z-axis) has a huge variability leading to irregular surfaces, hence the need for a non-linear scaling of the Z-axis; communicating the meaning of each topic to the user is difficult, as the top terms of each topic may not be meaningful to a human, and make for a poor label. The proposed processing and visualization is developed using only open-source tools and frameworks, leveraging the work of the open-source geospatial community. All the software developed in the course of the Australian Data Observatory project is available under the Apache 2.0 license, and available through the University of Melbourne GitLab source code repository.

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name: Mapping the Chatter: Spatial Metaphors for Dynamic Topic Modelling of Social Media


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description: Spatial data infrastructures prioritize data interoperability to serve their diverse communities. Geospatial knowledge graphs (GKG) are a form of database representation and handling that aim to meet the challenges of data interoperability, reasoning for information storage and knowledge creation, and user access that provide coherent spatial context to a domain of information. This paper discusses the development of a prototype GKG based on national topographic databases. Geospatial data are used to test interoperability aspects of ontology creation, faceted search and retrieval using GeoSPARQL (Open Geospatial Consortium, 2022), and user interface for data visualization and evaluation. The challenges are to capture and represent geographic semantics inherent in the source data, to integrate data from outside sources through SPARQL Protocol and RDF Query Language (SPARQL) queries and to visualize the data using a cartographic user interface. Poore (2003) identified four levels of data interoperability: articulation, sharing, integration, and alignment. These concepts are carried into the semantic technology design and application. Called the Map as Knowledge Base (MapKB), the approaches use software components to build a system architecture aligned with available standardized vocabularies and is composed entirely of free and open-source software for geospatial data The application was created in the context of The National Map of the U.S. Geological Survey (USGS). For purposes of data interoperability, the GKG ontology, queries, and visualization were studied for the system. Data pre-processing involved creating a GKG ontology. The ontology was semi-automatically transformed from source databases through the application of rules on schema attribute, domain, and metadata files to create classes, properties, and other triple resources of Resource Description Framework (RDF) and Web Ontology Language (OWL) (Hayes and Patel-Schneider, 2014; Hitzler and others, 2012). An R2RML file was created using Web-Karma for transforming the feature-level instance data using the ontology and confirmed using standards specifications (University of Southern California, 2016; Das and others, 2012). The converted data and ontology are imported into a triplestore for data handling. A cartographic user interface (UI) was created as a foundation for the visualization and interaction of users with the triplestore graphs. The general guidelines given by the information search process model serves to guide UI functionality (Kuhlthau, 2004). The user interface offers menu search options by namespace for typically retrieving initial results. Multiple graphs can be visualized at once. Other queries can be performed on the initial results appearing on a map or table by faceted search and by query builder interfaces for SPARQL. An advanced feature description function retrieves related properties to support browsable graph searches. Linked Open Data were retrieved using SPARQL endpoints to test linking triples. Some GeoSPARQL support was created for geospatial queries on feature geometries of the GKG use cases. The automated transformation ontology revealed aspects of data silos that were known to exist. However, the ontology model created a new perspective of data resources across the enterprise, where resource semantics could be streamlined for reuse. This was demonstrated in the post-processing stage of the ontology creation. The system and ontology design were validated through reasoning of semantically related data and pre-determined competency questions relevant to reasoning results. An ontology pattern of aligning feature classes represented as codes and geometries of The National Map matched to the GeoSPARQL ontology feature and geometry classes was validated using reasoners. The ontology for feature interoperability provided inferred information for competency questions such as “What type of feature is classified as FCode 73002,” or “How are streams represented geometrically?” The GKG alignment with Linked Open Data used some specific widely used vocabularies to be reused between graphs, and problems encountered could be resolved by designing a better metadata annotation approach for structural alignment in addition to syntax matching. Multiple GeoSPARQL queries executing topological relations on features were successfully demonstrated with a pre-built query to find specified buildings on a road section between two cross streets. Such a query can depend on the shape of the road, building distance from the roadway, and other factors. The queries required a change in viewpoint from machine computation to landscape cognition creating related semantic factors, and then were followed by GeoSPARQL function computation. This project tested some key challenges for GKG applications for spatial data infrastructure interoperability including data transformation, ontology design, information search and retrieval, and multi-modality cartographic visualization. Completing the resulting ontology from automated data transformation for knowledge representation is still a cognitive activity. RDF and OWL vocabulary were sufficiently expressive to demonstrate linking and reasoning successes. Improved metadata annotation systems are needed for on-the-fly entity resolution. Although initial tests of GeoSPARQL techniques were successful, the full capabilities of SPARQL as a rule-based reasoning tool would need further research for queries that leverage the full semantic capabilities of knowledge graphs and for their portrayal. Disclaimer Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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name: A knowledge graph prototype for national topographic data


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description: Analysis of Free and open Land Cover maps for agricultural land use planning at the local level The area of agricultural land for food production is limited and is constantly decreasing both in the world and in Bosnia and Herzegovina. According to the National Action Plan in Bosnia and Herzegovina (B&H), up to 1,600 ha of land are lost annually (NEAP BiH 2003). The prevention of degradation and sustainably controlled land use should be the most important parts of the land protection policy of every country and the local community. In order for this policy to be implemented properly, relevant indicators of the state of land resources are necessary (Predic et al. 2021). According to the Law on Agricultural Land of the Republic of Srpska, municipalities and cities are obliged to prepare a planning document “Groundwork for Agricultural Land Protection, Use and Restructuring (The groundwork)”. The Groundwork is made according to the FAO (Food and Agriculture Organization) model which consists of an inventory of land and climate resources, agro-ecological zoning, and economic-ecological zoning. With GIS modeling of existing data (pedology, digital elevation model, climate data,...) new relevant data were created (bonity, agro-ecological zoning, suitability of cultivation…). It is intended for municipal authorities in decisions making in the process of land use and protection. GIS layer of the current condition of land cover and land use (hereinafter LC/LU) is one of the most important GIS layers for creating Groundwork. It is necessary to make a precise GIS layer on a large scale in order to obtain relevant data on agricultural land and land use. The most precise method of making LC/LU is manual mapping of LC/LU classes with orthophotos and high-resolution satellite images combined with field verification. The critical point of this method is that it is time consuming. On the other hand, "free" land cover data is available, such as Corine Land Cover (hereinafter CLC), OpenStreetMap,... In this paper, using free open source programs, a comparison of two sets of data representing land cover was performed: manually vectorized data with an orthophoto image of LC/LU and CLC. The aim of this paper is to determine the relevance of CLC data for the needs of land use planning at the level of administrative units in B&H. The study area is the municipality of Laktaši with an area of 38807 ha for which the LC/LU was created in 2018 at the same time as the CLC for B&H. The first phase of the comparison is the synchronization of LC/LU-CLC classifications. LC/LU classification is The Land Cover Classification System, (FAO LCCS, 2000) which is modifiable for the conditions of B&H. Both the LC/LU and the CLC classifications consist of classes divided into three levels. The main difference between LC/LU and CLC is that the LC/LU classification is primarily intended for the detailed identification of agricultural land. The LC/LU nomenclature is dominated by classes that represent agricultural land both in terms of land cover and in terms of use (18 out of a total of 36 classes). The smallest mapped area in LC/LU depends on the significance of a LC/LU class. For example, for the arable land class, it is 0.5 ha, and for the permanent crops class, it is 0.1 ha. The main reason is the fragmentation of properties in B&H (85% is dominated by less than 0.5 ha plots). Unlike the CLC classification, which discusses artificial surfaces in great detail and has 11 classes in the third level (111 Continuous urban fabric,.., 121 Industrial or commercial units,…,142 Sport and leisure facilities), LC/LU classification has only 2 classes for artificial surfaces: Built up and Built up dominates. In this class, the minimum mapped area is 0.025 ha because it is necessary to accurately separate land areas that are temporarily or permanently lost to agriculture. Regardless of the above differences, it is possible to synchronize LC/LU and CLC classifications through third level classes. In the study area (Laktaši municipality) LC/LU GIS layer contains 23 out of 36 LC/LU classes (10707 polygons), and CLC layer 16 out of 44 classes (177 polygons). In the study area, the CLC classification did not recognize 11 classes of LC/LU, of which 8 classes are precisely characterized by agricultural areas (greenhouses, vineyards, nurseries, meadows…). The entire process of comparing and analyzing data was performed using QGIS with the support of the Python programming language. Using QGIS, the union of LC/LU and CLC polygons (14044 polygons) was performed. Using the Python programming language, an error matrix was created and the parameters of the quality of land cover maps were recalculated (Bratic et al., 2020). The obtained results show the accuracy of CLC with respect to LC/LU reference. Although the overall accuracy is 70%, the class-level results are showing that during the creation of CLC layers, a significant part of non-agricultural areas was marked as agricultural classes. For example, 19.4% LC/LU forest class and 42.4% LC/LU class built up, in CLC were mapped as arable dominated class. From the above example, in the studied area, a significalntly larger area of agricultural land was present in relataion to the actual state. After analyzing the results, it was concluded that the CLC in the studied area is not a sufficiently precise GIS basis for agricultural land use planning at the local level. However, it can be a good starting point for making of LC/LU, which would significantly shorten the time of its creating.

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description: Computing Global Harmonic Parameters for Flood Mapping using TU Wien’s SAR Datacube Software Stack. Synthetic Aperture Radar (SAR) backscatter is adept in differentiating standing water, due to its low signal, compared to most non-water surface cover types. However, the temporal transition from non-water to water is critical to identifying floods. Hence objects with permanent or seasonally low backscatter become ambiguous and difficult to classify. TU Wien's flood mapping algorithm utilizes a pixel-wise harmonic model derived from SAR datacube (DC) (Bauer-Marschallinger et al., in review) to account for these patterns. Designed to be applied globally in near real-time, our method applies Bayes inference on SAR data in VV polarization. In this method, the harmonic model generates the non-flooded reference distribution, which we then compare against flooded distribution to delineate floods within incoming Sentinel-1 IW GRDH scenes. In the harmonic modeling, we estimate each location's expected temporal backscatter variation, explained by a set of Fourier coefficients. Following recommendations in the literature, a seven coefficient formulation was adopted (Schlaffer et al.,2015) and is here on referred to as our harmonic parameters (HPARs). The HPARs include the backscatter mean and three iterations of two sinusoidal coefficients. This model acts as a smoothened proxy for the measurements in the time series, thus allowing for a seasonally varying backscatter reference to be estimated for any given day-of-year However, generating the harmonic model at a global scale and with high resolution presents significant logistical and technical challenges. Therefore, harmonic modeling of remotely sensed time series is often performed on specialized infrastructures (Liu et al., 2020), such as Google Earth Engine (GEE) (Gorelick et al., 2017) or other highly customized setups (Zhou et al., 2021), where the pixel-wise analysis of multi-year data requires well-defined I/O, data chunking, and parallelization strategies to generate the HPARs in reasonable time and cost. While harmonic analysis is not new, to our knowledge, production and application at a global scale using dense SAR time series have yet to be implemented, let alone operationally utilized. To prepare for global near real-time flood mapping effort, HPARs were systematically computed using a global DC organizing Sentinel-1 IW GRDH datasets. In the DC structure, individual images are stacked, allowing for data abstraction in the spatial and temporal dimensions, making it ideal for time-series analysis. However, for this abstraction to be realized, a rich set of software solutions is needed to implement the 3-dimensional data model. In this contribution, we present our SAR DC software stack and its utilization to compute the aforementioned global harmonic parameters. We show a set of portable and loosely coupled Python packages developed by the TU Wien GEO Microwave Remote Sensing (MRS) group capable of forming a global data cube with minimal overhead from individual satellite images. The stack includes, among others, open-source packages for: ``` high-level data cube abstraction - yeoda, spatial reference and hierarchical tiling system - Equi7Grid, lower-level data access and I/O – veranda, spatial file and folder-based naming and handling – geopathfinder, and product tagging and metadata management – medali.``` The detailed description of the preprocessing and storage infrastructure used for this global DC is outlined by Wagner et al., 2021. Here, we focus on the software interfaces. Moreover, given the preprocessed datasets, the logical entry point is through yeoda, which abstracts well-structured Earth observation data collections as a DC, making high-level operations such as filtering and data loading possible. This level of abstraction is supported by the other components of the software stack, which addresses the organization and lower-level access to the individual files. In a nutshell, the DC is simply a collection of raster datasets in GeoTIFF file format co-registered in the same reference grid. To deploy for large-scale operations, a well-defined grid system is required to deal with high-resolution raster data. A tiling system fulfilling this requirement is the Equi7grid, based on seven equidistant continental projections found to minimize raster image oversampling. Interacting with this tiling system on an abstract level is possible via our in-house developed Equi7Grid package. The tiling system follows a hierarchy of directories to manage the datasets on disk. Moreover, for individual files, a predefined naming convention is applied to indicate spatial, temporal, and ancillary information from product metadata that becomes transparent to yeoda. This setup of customizable file naming schemes is easily managed through the geopathfinder package.

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