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The Sol Katz Award for Geospatial Free and Open Source Software (GFOSS) is awarded annually by OSGeo to individuals who have demonstrated leadership in the GFOSS community. Recipients of the award have contributed significantly through their activities to advance open source ideals in the geospatial realm.
OSGeo
Degree with honors in Physics, PhD in Geodesy and Cartography. She is Professor of GIS at the Politecnico di Milano (PoliMI) and member of the School of Doctoral Studies in Data Science at “Roma La Sapienza”. From 1997 to 2010 she was the Director of the “Geomatics Lab” of PoliMI. From 2011 to 2016 she was the Vice-Rector of PoliMI for the Como Campus. She is the chair of ISPRS WG IV/4 “Collaborative crowdsourced cloud mapping (C3M)”; member of ESA ACEO (Advisory Committee of Earth Observation); co-chair of the United Nations Open GIS Initiative, Chair of the UN-GGIM (Global Geospatial Information Management) Academic Network, mentor of the PoliMI Chapter of YouthMappers (PoliMappers). Her research activity is in the field of geomatics. Her interests have been various, starting from geodesy, radar-altimetry and moving later to GIS, webGIS, geospatial web platform, VGI (Volunteer Geographic Information), Citizen Science, Big Geo Data and Geospatial Artificial Intelligence. She is participating and leading research on these topics within the frameworks of both national and international projects and scientific networks. One of her main interests is in Open Source GIS, where she is playing a worldwide leading role.
Advances in artificial intelligence (AI) and the extension of citizen science to various scientific areas, as well as the generation of big citizen science data, are resulting in AI and citizen science being good partners, and their combination benefits both fields. The integration of AI and citizen science has mostly been used in biodiversity projects, with the primary focus on using citizen science data to train machine learning (ML) algorithms for automatic species identification. In this article, we will look at how ML techniques can be used in citizen science and how they can influence volunteer engagement, data collection, and data validation. We reviewed several use cases from various domains and categorized them according to the ML technique used and the impact of ML on citizen science in each project. Furthermore, the benefits and risks of integrating ML in citizen science are explored, and some recommendations are provided on how to enhance the benefits while mitigating the risks of this integration. Finally, because this integration is still in its early phases, we have proposed some potential ideas and challenges that can be implemented in the future to leverage the power of the combination of citizen science and AI, with the key emphasis being on citizen science in this article.
Maryam Lotfian; Jens Ingensand; Maria Brovelli. The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality. Sustainability 2021, 13, 8087 .
AMA StyleMaryam Lotfian, Jens Ingensand, Maria Brovelli. The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality. Sustainability. 2021; 13 (14):8087.
Chicago/Turabian StyleMaryam Lotfian; Jens Ingensand; Maria Brovelli. 2021. "The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality." Sustainability 13, no. 14: 8087.
Citizen science, the participation of the public in scientific projects, is growing significantly, especially with technological developments in recent years. Volunteers are the heart of citizen science projects; therefore, understanding their motivation and how to sustain their participation is the key to success in any citizen science project. Studies on participants of citizen science projects illustrate that there is an association between participant motivation and the type of contribution to projects. Thus, in this paper, we define a motivational framework, which classifies participant motivation taking into account the typologies of citizen science projects. Within this framework, we also take into account the importance of motivation in initiating and sustaining participation. This framework helps citizen science practitioners to have comprehensive knowledge about potential motivational factors that can be used to recruit participants, as well as sustaining participation in their projects.
Maryam Lotfian; Jens Ingensand; Maria Antonia Brovelli. A Framework for Classifying Participant Motivation that Considers the Typology of Citizen Science Projects. ISPRS International Journal of Geo-Information 2020, 9, 704 .
AMA StyleMaryam Lotfian, Jens Ingensand, Maria Antonia Brovelli. A Framework for Classifying Participant Motivation that Considers the Typology of Citizen Science Projects. ISPRS International Journal of Geo-Information. 2020; 9 (12):704.
Chicago/Turabian StyleMaryam Lotfian; Jens Ingensand; Maria Antonia Brovelli. 2020. "A Framework for Classifying Participant Motivation that Considers the Typology of Citizen Science Projects." ISPRS International Journal of Geo-Information 9, no. 12: 704.
Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations.
Vasil Yordanov; Maria Antonia Brovelli. Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy. Applied Geomatics 2020, 13, 287 -309.
AMA StyleVasil Yordanov, Maria Antonia Brovelli. Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy. Applied Geomatics. 2020; 13 (3):287-309.
Chicago/Turabian StyleVasil Yordanov; Maria Antonia Brovelli. 2020. "Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy." Applied Geomatics 13, no. 3: 287-309.
The epidemic of coronavirus-disease-2019 (COVID-19) started in Italy with the first official diagnosis on 21 February 2020; However, it is not known how many cases were already present in earlier days and weeks, thus limiting the possibilities of conducting any retrospective analysis. We hypothesized that an unbiased representation of COVID-19 diffusion in these early phases could be inferred by the georeferenced calls to the emergency number relevant to respiratory problems and by the following emergency medical services (EMS) interventions. Accordingly, the aim of this study was to identify the beginning of anomalous trends (change in the data morphology) in emergency calls and EMS ambulances dispatches and reconstruct COVID-19 spatiotemporal evolution on the territory of Lombardy region. Accordingly, a signal processing method, previously used to find morphological features on the electrocardiographic signal, was applied on a time series representative of territorial clusters of about 100,000 citizens. Both emergency calls and age- and gender-weighted ambulance dispatches resulted strongly correlated to COVID-19 casualties on a provincial level, and the identified local starting days anticipated the official diagnoses and casualties, thus demonstrating how these parameters could be effectively used as early indicators for the spatiotemporal evolution of the epidemic on a certain territory.
Lorenzo Gianquintieri; Maria Brovelli; Andrea Pagliosa; Gabriele Dassi; Piero Brambilla; Rodolfo Bonora; Giuseppe Sechi; Enrico Caiani. Mapping Spatiotemporal Diffusion of COVID-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities. ISPRS International Journal of Geo-Information 2020, 9, 639 .
AMA StyleLorenzo Gianquintieri, Maria Brovelli, Andrea Pagliosa, Gabriele Dassi, Piero Brambilla, Rodolfo Bonora, Giuseppe Sechi, Enrico Caiani. Mapping Spatiotemporal Diffusion of COVID-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities. ISPRS International Journal of Geo-Information. 2020; 9 (11):639.
Chicago/Turabian StyleLorenzo Gianquintieri; Maria Brovelli; Andrea Pagliosa; Gabriele Dassi; Piero Brambilla; Rodolfo Bonora; Giuseppe Sechi; Enrico Caiani. 2020. "Mapping Spatiotemporal Diffusion of COVID-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities." ISPRS International Journal of Geo-Information 9, no. 11: 639.
Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.
Maria Antonia Brovelli; Yaru Sun; Vasil Yordanov. Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine. ISPRS International Journal of Geo-Information 2020, 9, 580 .
AMA StyleMaria Antonia Brovelli, Yaru Sun, Vasil Yordanov. Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine. ISPRS International Journal of Geo-Information. 2020; 9 (10):580.
Chicago/Turabian StyleMaria Antonia Brovelli; Yaru Sun; Vasil Yordanov. 2020. "Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine." ISPRS International Journal of Geo-Information 9, no. 10: 580.
The need for addressing geoprivacy in location based services has increased the offer of mechanisms that protect location information, however, these algorithms are not always developed to ensure the usability of the data and therefore, their adoption is not wide. In this work, a framework is presented to evaluate the effects of geoprivacy mechanisms on the quality of geodata to provide insights into how the data is affected for geospatial analysis. For this purpose, a toolkit of indices was developed to evaluate different characteristics of the data before and after a geoprivacy mechanism is implemented, providing a criterion to select one of them. The indices measure the changes in the presence of clusters through the quantification of hotspots in hotspot analysis and the difference observed in heatmaps of the concentration of the geodata. Variations in global indices like the Nearest Neighbor Index (NNI) and the orientation of the standard deviational ellipse are also measured. For demonstration, the data of crime arrests in New York was used for the month of January in 2017 and 2018. Five mechanisms were tested with different settings, resulting with the NRand-K algorithm producing fewer alterations to the reference data, preserving its initial characteristics better than the other mechanisms.
Mayra A. Zurbaran; Augusto Salazar; Maria Antonia Brovelli; Pedro M. Wightman. An Evaluation Framework for Assessing the Impact of Location Privacy on Geospatial Analysis. IEEE Access 2020, 8, 158224 -158236.
AMA StyleMayra A. Zurbaran, Augusto Salazar, Maria Antonia Brovelli, Pedro M. Wightman. An Evaluation Framework for Assessing the Impact of Location Privacy on Geospatial Analysis. IEEE Access. 2020; 8 (99):158224-158236.
Chicago/Turabian StyleMayra A. Zurbaran; Augusto Salazar; Maria Antonia Brovelli; Pedro M. Wightman. 2020. "An Evaluation Framework for Assessing the Impact of Location Privacy on Geospatial Analysis." IEEE Access 8, no. 99: 158224-158236.
Public Access Defibrillation (PAD) is the leading strategy in reducing time to first defibrillation in cases of Out-Of-Hospital Cardiac Arrest (OHCA), but PAD programs are underperforming considering their potentiality. Our aim was to develop an analysis and optimization framework, exploiting georeferenced information processed with Geographic Information Systems (GISs), specifically targeting residential OHCAs. The framework, based on an historical database of OHCAs, location of Automated External Defibrillators (AEDs), topographic and demographic information, proposes new strategies for AED deployment focusing on residential OHCAs, where performance assessment was evaluated using AEDs “catchment area” (area that can be reached within 6 min walk along streets). The proposed framework was applied to the city of Milan, Lombardy (Italy), considering the OHCA database of four years (2015–2018), including 8152 OHCA, of which 7179 (88.06%) occurred in residential locations. The proposed strategy for AEDs deployment resulted more effective compared to the existing distribution, with a significant improvement (from 41.77% to 73.33%) in OHCAs’ spatial coverage. Further improvements were simulated with different cost scenarios, resulting in more cost-efficient solutions. Results suggest that PAD programs, either in brand-new territories or in further improvements, could significantly benefit from a comprehensive planning, based on mathematical models for risk mapping and on geographical tools.
Gianquintieri Lorenzo; Brovelli Maria Antonia; Brambilla Piero Maria; Pagliosa Andrea; Villa Guido Francesco; Caiani Enrico Gianluca. Development of a Novel Framework to Propose New Strategies for Automated External Defibrillators Deployment Targeting Residential Out-Of-Hospital Cardiac Arrests: Application to the City of Milan. ISPRS International Journal of Geo-Information 2020, 9, 491 .
AMA StyleGianquintieri Lorenzo, Brovelli Maria Antonia, Brambilla Piero Maria, Pagliosa Andrea, Villa Guido Francesco, Caiani Enrico Gianluca. Development of a Novel Framework to Propose New Strategies for Automated External Defibrillators Deployment Targeting Residential Out-Of-Hospital Cardiac Arrests: Application to the City of Milan. ISPRS International Journal of Geo-Information. 2020; 9 (8):491.
Chicago/Turabian StyleGianquintieri Lorenzo; Brovelli Maria Antonia; Brambilla Piero Maria; Pagliosa Andrea; Villa Guido Francesco; Caiani Enrico Gianluca. 2020. "Development of a Novel Framework to Propose New Strategies for Automated External Defibrillators Deployment Targeting Residential Out-Of-Hospital Cardiac Arrests: Application to the City of Milan." ISPRS International Journal of Geo-Information 9, no. 8: 491.
The focus of this research is addressing a subset of the geovisualization (i.e., geographic visualization) challenges identified in the literature, namely multidimensional vector and raster geospatial data visualization. Moreover, the work implements an approach for multidimensional raster geospatial data processing. The results of this research are provided through a geoportal comprised of multiple applications that are related to 3D visualization of cities, ground deformation, land use and land cover and mobility. In a subset of the applications, the datasets handled are considered to be large in volume. The geospatial data were visualized on dynamic and interactive virtual globes to enable visual exploration. The geoportal is available on the web to enable cross-platform access to it. Furthermore, the geoportal was developed employing open standards, free and open source software (FOSS) and open data, most importantly to ensure interoperability and reduce the barriers to access it. The geoportal brings together various datasets, different both in terms of context and format employing numerous technologies. As a result, the existing web technologies for geovisualization and geospatial data processing were examined and exemplary and innovative software was developed to extend the state of the art.
Candan Eylül Kilsedar; Maria Antonia Brovelli. Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web. ISPRS International Journal of Geo-Information 2020, 9, 434 .
AMA StyleCandan Eylül Kilsedar, Maria Antonia Brovelli. Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web. ISPRS International Journal of Geo-Information. 2020; 9 (7):434.
Chicago/Turabian StyleCandan Eylül Kilsedar; Maria Antonia Brovelli. 2020. "Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web." ISPRS International Journal of Geo-Information 9, no. 7: 434.
All over the world, organizations are increasingly considering the adoption of open source software and open data. In the geospatial domain, this is no different, and the last few decades have seen significant advances in this regard. We review the current state of open source geospatial software, focusing on the Open Source Geospatial Foundation (OSGeo) software ecosystem and its communities, as well as three kinds of open geospatial data (collaboratively contributed, authoritative and scientific). The current state confirms that openness has changed the way in which geospatial data are collected, processed, analyzed, and visualized. A perspective on future developments, informed by responses from professionals in key organizations in the global geospatial community, suggests that open source geospatial software and open geospatial data are likely to have an even more profound impact in the future.
Serena Coetzee; Ivana Ivánová; Helena Mitasova; Maria Antonia Brovelli. Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future. ISPRS International Journal of Geo-Information 2020, 9, 90 .
AMA StyleSerena Coetzee, Ivana Ivánová, Helena Mitasova, Maria Antonia Brovelli. Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future. ISPRS International Journal of Geo-Information. 2020; 9 (2):90.
Chicago/Turabian StyleSerena Coetzee; Ivana Ivánová; Helena Mitasova; Maria Antonia Brovelli. 2020. "Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future." ISPRS International Journal of Geo-Information 9, no. 2: 90.
Citizen science can be thought of as a tremendous catalyst for making Digital Earth a participation model of our world. This chapter presents a wide overview of the concept and practice of citizen science in terms of the technologies and social impact. Definitions of citizen science and various existing approaches to citizen involvement are described, from simple contributions to projects proposed by someone else to the design and planning of science as a bottom-up process. To illustrate these concepts, the relevant example of OpenStreetMap is described in detail, and other examples are mentioned and briefly discussed. Social innovation connected with citizen science is focused on to highlight different levels of direct citizen contributions to scientific research and indirect effects on academia, and studies driven by new questions that may support responsible research and innovation (RRI), governments and public administration in making better informed decisions. Despite its growth and success in relatively few years, citizen science has not fully overcome a number of persistent challenges related to quality, equity, inclusion, and governance. These themes and related complex facets are discussed in detail in the last section of the chapter.
Maria Antonia Brovelli; Marisa Ponti; Sven Schade; Patricia Solís. Citizen Science in Support of Digital Earth. Manual of Digital Earth 2019, 593 -622.
AMA StyleMaria Antonia Brovelli, Marisa Ponti, Sven Schade, Patricia Solís. Citizen Science in Support of Digital Earth. Manual of Digital Earth. 2019; ():593-622.
Chicago/Turabian StyleMaria Antonia Brovelli; Marisa Ponti; Sven Schade; Patricia Solís. 2019. "Citizen Science in Support of Digital Earth." Manual of Digital Earth , no. : 593-622.
The paper presents SIMILE (Italian acronym for “Integrated monitoring system for knowledge, protection and valorization of the subalpine lakes and their ecosystems), a cross-border Italian-Swiss project whose general objectives are the strengthening of the coordinated management of the water of the great subalpine lakes in the so-called Insubric region and the intensification of stakeholder participation in the processes of knowledge and monitoring of the water resource. The project fits the purpose of SDG 6 and involves administrations, monitoring agencies, universities and research centers, and citizens.SIMILE is a system where geospatial data, information, and techniques play a pivotal role. The system strongly benefits the information derived from the analysis of Sentinel 1 and Sentinel 3 imagery, in situ authoritative data, and user-contributed georeferenced data. A Business Intelligence (BI) platform, i.e. a web data-driven decision support system, will allow the integration, analysis, and synthesis of the information derived from the different types of data, heterogeneous in format, coordinate system, information content, and access method. The technologies that will be used are based on open software so as to guarantee the replicability and sustainability of the system.
M. A. Brovelli; M. Cannata; M. Rogora. SIMILE, A GEOSPATIAL ENABLER OF THE MONITORING OF SUSTAINABLE DEVELOPMENT GOAL 6 (ENSURE AVAILABILITY AND SUSTAINABILITY OF WATER FOR ALL). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W20, 3 -10.
AMA StyleM. A. Brovelli, M. Cannata, M. Rogora. SIMILE, A GEOSPATIAL ENABLER OF THE MONITORING OF SUSTAINABLE DEVELOPMENT GOAL 6 (ENSURE AVAILABILITY AND SUSTAINABILITY OF WATER FOR ALL). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W20 ():3-10.
Chicago/Turabian StyleM. A. Brovelli; M. Cannata; M. Rogora. 2019. "SIMILE, A GEOSPATIAL ENABLER OF THE MONITORING OF SUSTAINABLE DEVELOPMENT GOAL 6 (ENSURE AVAILABILITY AND SUSTAINABILITY OF WATER FOR ALL)." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W20, no. : 3-10.
The paper deals with the general presentation of the Urban GEO BIG DATA, a collaborative acentric and distributed Free and Open Source (FOS) platform consisting of several components: local data nodes for data and related service Web deploy; a visualization node for data fruition; a catalog node for data discovery; a CityGML modeler; data-rich viewers based on virtual globes; an INSPIRE metadata management system enriched with quality indicators for each dataset.Three use cases in five Italian cities (Turin, Milan, Padua, Rome, and Naples) are examined: 1) urban mobility; 2) land cover and soil consumption at different resolutions; 3) displacement time series. Besides the case studies, the architecture of the system and its components will be presented.
M. A. Brovelli; P. Boccardo; G. Bordogna; A. Pepe; M. Crespi; M. Munafò; F. Pirotti. URBAN GEO BIG DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W14, 23 -30.
AMA StyleM. A. Brovelli, P. Boccardo, G. Bordogna, A. Pepe, M. Crespi, M. Munafò, F. Pirotti. URBAN GEO BIG DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W14 ():23-30.
Chicago/Turabian StyleM. A. Brovelli; P. Boccardo; G. Bordogna; A. Pepe; M. Crespi; M. Munafò; F. Pirotti. 2019. "URBAN GEO BIG DATA." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W14, no. : 23-30.
FOSS4G stands for Free and Open Source Software for Geospatial. It is the flagship event of OSGeo. Each FOSS4G has its special aura, kindly designed by each Local Organising Committe, sharing the local culture and spirit with the greater community. In 2019, geo-spatial.org, the OSGeo Local Chapter of Romania, won the honour of organising the geospatial event of the year. FOSS4G 2019 was held in Bucharest (Romania), in three of the most important buildings of this city: National Theatre of Bucharest, InterContinental Hotel and Faculty of Geography from the University of Bucharest.Following the established tradition of FOSS4G conferences, at the 2019 edition, an Academic Track ran in parallel with the General Track. The main purpose of this track was to bring together researchers, teachers, developers, users and practitioners carrying out research activities in geospatial domains, with an emphasis on the open source solutions. All types of topics such as results achieved, case studies, work in progress, academic endeavours to conceptualize, assess or teach open source geospatial software and data, were welcomed. The Academic Committee discouraged prevalent presentations of technologies or use cases without properly justifying originality to the scientific state of the art, emphasizing on particular novelty.At this edition, 53 papers were submitted to the Academic Track. These were blind reviewed by 3 reviewers. Finally 38 scientific papers were selected for publication in this volume of the ISPRS Archives. The editors would like to thank all the authors, the members from the Scientific Committee and the Organizing Committee for their valuable contributions. We hope you enjoy reading the proceedings.
A. F. Marin; M. A. Brovelli. ACADEMIC TRACK OF FOSS4G 2019 BUCHAREST – THE ASYMPTOTIC CONNECTION BETWEEN SOFTWARE AND DATA: PREFACE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W14, 1 -2.
AMA StyleA. F. Marin, M. A. Brovelli. ACADEMIC TRACK OF FOSS4G 2019 BUCHAREST – THE ASYMPTOTIC CONNECTION BETWEEN SOFTWARE AND DATA: PREFACE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W14 ():1-2.
Chicago/Turabian StyleA. F. Marin; M. A. Brovelli. 2019. "ACADEMIC TRACK OF FOSS4G 2019 BUCHAREST – THE ASYMPTOTIC CONNECTION BETWEEN SOFTWARE AND DATA: PREFACE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W14, no. : 1-2.
Land cover (LC) maps are crucial to analyze and understand several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, we observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on validation through open source and easy-to-use software. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.
Candan E Kilsedar; Gorica Bratic; Monia E Molinari; Marco Minghini; Maria A Brovelli. Open educational resources for validation of global high-resolution land cover maps. 2019, 1 .
AMA StyleCandan E Kilsedar, Gorica Bratic, Monia E Molinari, Marco Minghini, Maria A Brovelli. Open educational resources for validation of global high-resolution land cover maps. . 2019; ():1.
Chicago/Turabian StyleCandan E Kilsedar; Gorica Bratic; Monia E Molinari; Marco Minghini; Maria A Brovelli. 2019. "Open educational resources for validation of global high-resolution land cover maps." , no. : 1.
The investigation of any kind of extensive spatial phenomena - by means of geospatial data - requires dedicated analysis strategies and software tools in order to properly understand and represent their interactions within the geographical context. In this study, Exploratory Spatial Data Analysis (ESDA) is suggested to investigate underlyingspatial patterns of the soil consumption phenomenon in Italy,together with its interplay with another likely linked macroeconomic variable, i.e. the average income per capita. The analysis is carried out for the whole Italian territory by considering data at a municipal level. A plugin for the Free Open Source Software (FOSS) QGIS, called Hotspot Analysis, is here presented and employed for the study. The output consists of maps depicting the spatial interaction of the investigated variables which can be readily used to identify where spatial clusters and/or outliers are located.Results provide with meaningful insights for the comprehension of soil consumption patterns in Italy and their representation through maps, by demonstrating the benefits given by the integration between ESDA and GIS functionalities.
Daniele Oxoli; Monia Elisa Molinari; Maria Antonia Brovelli. Hotspot Analysis, an open source GIS tool for exploratory spatial data analysis: application to the study of soil consumption in Italy. Rendiconti Online della Società Geologica Italiana 2018, 46, 82 -87.
AMA StyleDaniele Oxoli, Monia Elisa Molinari, Maria Antonia Brovelli. Hotspot Analysis, an open source GIS tool for exploratory spatial data analysis: application to the study of soil consumption in Italy. Rendiconti Online della Società Geologica Italiana. 2018; 46 ():82-87.
Chicago/Turabian StyleDaniele Oxoli; Monia Elisa Molinari; Maria Antonia Brovelli. 2018. "Hotspot Analysis, an open source GIS tool for exploratory spatial data analysis: application to the study of soil consumption in Italy." Rendiconti Online della Società Geologica Italiana 46, no. : 82-87.
Climate issues are nowadays one of the most pressing societal challenges, with cities being identified among the landmarks for climate change. This study investigates the effect of urban land cover composition on a relevant climate-related variable, i.e., the air temperature. The analysis exploits different big geo-data sources, namely high-resolution satellite imagery and in-situ air temperature observations, using the city of Milan (Northern Italy) as a case study. Satellite imagery from the Landsat 8, Sentinel-2, and RapidEye missions are used to derive Local Climate Zone (LCZ) maps depicting land cover compositions across the study area. Correlation tests are run to investigate and measure the influence of land cover composition on air temperature. Results show an underlying connection between the two variables by detecting an average temperature offset of about 1.5 ∘ C between heavily urbanized and vegetated urban areas. The approach looks promising in investigating urban climate at a local scale and explaining effects through maps and exploratory graphs, which are valuable tools for urban planners to implement climate change mitigation strategies. The availability of worldwide coverage datasets, as well as the exclusive use of Free and Open Source Software (FOSS), provide the analysis with a potential to be empowered, replicated, and improved.
Daniele Oxoli; Giulia Ronchetti; Marco Minghini; Monia Elisa Molinari; Maryam Lotfian; Giovanna Sona; Maria Antonia Brovelli. Measuring Urban Land Cover Influence on Air Temperature through Multiple Geo-Data—The Case of Milan, Italy. ISPRS International Journal of Geo-Information 2018, 7, 421 .
AMA StyleDaniele Oxoli, Giulia Ronchetti, Marco Minghini, Monia Elisa Molinari, Maryam Lotfian, Giovanna Sona, Maria Antonia Brovelli. Measuring Urban Land Cover Influence on Air Temperature through Multiple Geo-Data—The Case of Milan, Italy. ISPRS International Journal of Geo-Information. 2018; 7 (11):421.
Chicago/Turabian StyleDaniele Oxoli; Giulia Ronchetti; Marco Minghini; Monia Elisa Molinari; Maryam Lotfian; Giovanna Sona; Maria Antonia Brovelli. 2018. "Measuring Urban Land Cover Influence on Air Temperature through Multiple Geo-Data—The Case of Milan, Italy." ISPRS International Journal of Geo-Information 7, no. 11: 421.
The availability of content constantly generated within theWeb has resulted in an incredibly rich virtual social environment from which it is possible to retrieve almost any sort of information. Since the advent of the social media connection with location-based services, this information has attracted the interest of manifold disciplines connected to the spatial data science. In this context, we introduce the URBAN-GEO BIG DATA (URBAN GEOmatics for Bulk Information Generation, Data Assessment and Technology Awareness), a Project of National Interest funded by the Italian Ministry of Education that aims at contributing to the exploitation of heterogeneous geodata sources such as VGI, geo-crowdsourcing, earth observation, etc. for a better understanding of urban dynamics. The presented work tackles one of the tasks requested by the project, which is connected to an investigation of the use of Twitter as a geodata source for retrieving valuable insights on the citizens’ interaction with mobility services and hubs. The study refers to five Italian cities, namely Milan, Turin, Padua, Rome, and Naples. Data collection is performed through the use of the Twitter streaming application programming interface. Collected data is analyzed by means of natural language processing techniques with Python. Results include a) extractions of mobility-related tweets presented by means of maps enabling the exploration of their spatial distribution within the cities, and b) a classification of the mobility-related tweets by means of sentiment analysis, allowing to investigate citizens’ perceptions of mobility services. A light and reproducible procedure to achieve these results is also outlined. In general terms, the results are intended for providing snapshots of the citizen interaction with both mobility infrastructure and services enabling a better description of mobility patterns and habits within the studied cities. The work leverages the geo-crowdsourced data within the traditional urban management practices in Italy and investigates the benefits, drawbacks, limitations connected to these data sources, which is the ultimate goal of the URBAN-GEO BIG DATA project.
M. E. Molinari; Daniele Oxoli; C. E. Kilsedar; M. A. Brovelli. USER GEOLOCATED CONTENT ANALYSIS FOR URBAN STUDIES: INVESTIGATING MOBILITY PERCEPTION AND HUBS USING TWITTER. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, XLII-4, 439 -442.
AMA StyleM. E. Molinari, Daniele Oxoli, C. E. Kilsedar, M. A. Brovelli. USER GEOLOCATED CONTENT ANALYSIS FOR URBAN STUDIES: INVESTIGATING MOBILITY PERCEPTION AND HUBS USING TWITTER. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018; XLII-4 ():439-442.
Chicago/Turabian StyleM. E. Molinari; Daniele Oxoli; C. E. Kilsedar; M. A. Brovelli. 2018. "USER GEOLOCATED CONTENT ANALYSIS FOR URBAN STUDIES: INVESTIGATING MOBILITY PERCEPTION AND HUBS USING TWITTER." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4, no. : 439-442.
Land cover (LC) maps are crucial to analyze and understand several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, we observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on validation through open source and easy-to-use software. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.
Candan E Kilsedar; Gorica Bratic; Monia E Molinari; Marco Minghini; Maria A Brovelli. Open educational resources for validation of global high-resolution land cover maps. 2018, 1 .
AMA StyleCandan E Kilsedar, Gorica Bratic, Monia E Molinari, Marco Minghini, Maria A Brovelli. Open educational resources for validation of global high-resolution land cover maps. . 2018; ():1.
Chicago/Turabian StyleCandan E Kilsedar; Gorica Bratic; Monia E Molinari; Marco Minghini; Maria A Brovelli. 2018. "Open educational resources for validation of global high-resolution land cover maps." , no. : 1.
OpenStreetMap (OSM) is currently the largest openly licensed collection of geospatial data, widely used in many projects as an alternative to or integrated with authoritative data. One of the main criticisms against this dataset is that, being a collaborative product created mainly by citizens without formal qualifications, its quality has not been assessed and therefore its usage can be questioned for some applications. This paper provides a map matching method to check the spatial accuracy of the building footprint layer, based on a comparison with a reference dataset. Moreover, from the map matching and a similarity check, buildings can be detected and therefore an index of completeness can also be computed. This process has been applied in Lombardy, a region in Northern Italy, covering an area of 23,900 km2 and comprising respectively about 1 million buildings in OSM and 2.8 million buildings in the authoritative dataset. The results of the comparison show that the positional accuracy of the OSM buildings is at least compatible with the quality of the reference dataset at the scale of 1:5000 since the average deviation, with respect to the authoritative map, is below the expected tolerance of 3 m. The analysis of completeness, given in terms of the number of buildings appearing in the authoritative dataset and not present in OSM, shows an average percentage in the whole region equal to 57%. However, worth noting that the opposite, namely the number of buildings in OSM and not in the reference dataset, is not zero, but corresponds to 9%. The OSM building map can therefore be considered to be a valid base map for direct use (territorial frameworks, map navigation, urban analysis, etc.) and for derived use (background for the production of thematic maps) in all those cases where an accuracy corresponding to 1:5000 is required. Moreover it could be used for integrating the authoritative map at this scale (or smaller) where it is not complete and a rigorous quality certification in terms of metric precision is not required.
Maria Antonia Brovelli; Giorgio Zamboni. A New Method for the Assessment of Spatial Accuracy and Completeness of OpenStreetMap Building Footprints. ISPRS International Journal of Geo-Information 2018, 7, 289 .
AMA StyleMaria Antonia Brovelli, Giorgio Zamboni. A New Method for the Assessment of Spatial Accuracy and Completeness of OpenStreetMap Building Footprints. ISPRS International Journal of Geo-Information. 2018; 7 (8):289.
Chicago/Turabian StyleMaria Antonia Brovelli; Giorgio Zamboni. 2018. "A New Method for the Assessment of Spatial Accuracy and Completeness of OpenStreetMap Building Footprints." ISPRS International Journal of Geo-Information 7, no. 8: 289.
The open and direct collaboration at the creation, improvement, and documentation of source code and software applications – enabled by the web – is recognized as a peculiarity of the Free and Open Source Software for Geospatial (FOSS4G) projects representing, at the same time, one of their main strengths. With this in mind, it turns out to be interesting to perform an extensive monitoring of both the evolution and the geographical arrangement of the developers’ communities in order to investigate their actual extension, evolution and degree of activity. In this work, a semi-automatic procedure to perform this particular analysis is described. The procedure is mainly based on the use of the GitHub Search Application Programming Interface by means of JavaScript custom modules to perform a census of the users registered with a collaborator role to the repositories of the most popular FOSS4G projects, hosted on the GitHub platform. The collected data is processed and analysed using Python and QGIS. The results – presented through tables, charts, and thematic maps – allow describing both dimensions as well as the geographical heterogeneity of the contributing community of each individual project, while enabling to identify the most active countries – in terms of the number of contributors – in the development of the most popular FOSS4G. The limits of the analysis, including technical constraints and considerations on the significance of the developers' census, are finally highlighted and discussed.
Daniele Oxoli; H.-K. Kang; M. A. Brovelli. A SEMI-AUTOMATIC PROCEDURE FOR A DEMOGRAPHIC ANALYSIS OF THE FOSS4G DEVELOPERS’ COMMUNITY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, XLII-4/W8, 171 -174.
AMA StyleDaniele Oxoli, H.-K. Kang, M. A. Brovelli. A SEMI-AUTOMATIC PROCEDURE FOR A DEMOGRAPHIC ANALYSIS OF THE FOSS4G DEVELOPERS’ COMMUNITY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018; XLII-4/W8 ():171-174.
Chicago/Turabian StyleDaniele Oxoli; H.-K. Kang; M. A. Brovelli. 2018. "A SEMI-AUTOMATIC PROCEDURE FOR A DEMOGRAPHIC ANALYSIS OF THE FOSS4G DEVELOPERS’ COMMUNITY." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W8, no. : 171-174.