Maria Antonia Brovelli
Biographic Data
| ID | 7574231 |
|---|---|
| NAME | Maria Antonia Brovelli |
| GIVEN NAMES | Maria Antonia |
| FAMILY NAME | Brovelli |
| SIGNATURE | BROVELLI M A |
| AFFILIATIONS | Politecnico di Milano |
| ORCID | 0000-0003-3161-5561 |
| VERIFIED | Yes |
| TOTAL WORKS | 17 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 17 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
High-Resolution Global Land Cover Maps and Their Assessment Strategies
Global High-Resolution Land Cover Maps (GHRLCs), characterized by spatial resolutions higher than 30 m per pixel, have become essential tools for environmental monitoring, urban planning, and climate modeling. Over the past two decades, new GHRLCs have emerged, offering increasingly detailed and timely representations of Earth’s surface. This review provides an in-depth analysis of recent developments by examining the data sources, methodologies,…
Characterising Active Mobility in Urban Areas Through Street Network Indices
In the context of sustainable development, the concept of active mobility plays a key role in modern urban areas. To evaluate active mobility in these areas, we formulate a framework for characterising active mobility by calculating street network indices using global, free, and open data. This framework comprises the download and processing of pedestrian, cycling, driving, and public transport street networks from OpenStreetMap, the selection of…
Bridging Humanitarian Mapping and the Sustainable Development Goals
The Sustainable Development Goals (SDGs) have become the global framework for evaluating the effectiveness of humanitarian projects. Humanitarian mapping is considered a popular voluntary geographic information technique that provides data for disaster response. Although humanitarian mapping has contributed significantly to the SDGs, there is a lack of in-depth studies on the state of this relationship. This paper aims to assess the potential rel…
Investigating the Performance of Open-Vocabulary Classification Algorithms for Pathway and Surface Material Detection in Urban Environments
Mapping pavement types, especially in sidewalks, is essential for urban planning and mobility studies. Identifying pavement materials is a key factor in assessing mobility, such as walkability and wheelchair usability. However, satellite imagery in this scenario is limited, and in situ mapping can be costly. A promising solution is to extract such geospatial features from street-level imagery. This study explores using open-vocabulary classificat…
Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models
The Ca Mau Peninsula, situated in the Mekong Delta of Vietnam, features low-lying terrain. In addition to the challenges posed by climate change, land subsidence in the area is exacerbated by the overexploitation of groundwater and intensive agricultural practices. In this study, we assessed the land subsidence susceptibility in the Ca Mau Peninsula utilizing three boosting machine learning models: AdaBoost, Gradient Boosting, and Extreme Gradien…
State-of-art in modelling particulate matter (PM) concentration: A scoping review of aims and methods
Air pollution is the one of the most significant environmental risks to health worldwide. An accurate assessment of population exposure would require a continuous distribution of measuring ground-stations, which is not feasible. Therefore, significant efforts are spent in implementing air-quality models. However, a complex scenario emerges, with the spread of many different solutions, and a consequent struggle in comparison, evaluation and replic…
Development of a Voice Virtual Assistant for the Geospatial Data Visualization Application on the Web
Voice assistants can elevate interaction in geospatial data web platforms. This research introduces a voice assistant in the BStreams platform and focuses on understanding user commands in the geospatial domain. We developed a specialised geospatial discourse framework through structured prototyping. A survey with 66 participants revealed prevalent English geospatial terminologies. Using ChatGPT, we found the term suggestions aligned with survey …
Estimating Daily NO2 Ground Level Concentrations Using Sentinel-5P and Ground Sensor Meteorological Measurements
Environmental and health deterioration due to the increasing presence of air pollutants is a pressing topic for governments and organizations. Institutions such as the European Environment Agency have determined that more than 350,000 premature deaths can be attributed to atmospheric pollutants. The measurement of trace gas atmospheric concentrations is key for environmental agencies to fight against the decreased deterioration of air quality. NO…
Population Space–Time Patterns Analysis and Anthropic Pressure Assessment of the Insubric Lakes Using User-Generated Geodata
Human activities are one of the main causes of lake-water pollution and eutrophication. The study of human pressure around lakes is of importance to understand its effects on the lakes natural resources. Social media data is a valuable space–time-resolved information source to detect human dynamics. In this study, user-generated geodata, namely users’ location records provided by the Facebook Data for Good program, are used to assess population p…
Generating High-Granularity Covid-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning
The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Servic…
Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of Covid-19 Diffusion in the Lombardy Region (Italy)
this is the first study relating COVID-19 estimated speed of diffusion with indicators of exposure to NH 3 . As NH 3 could induce oxidative stress, its role in creating a pre-existing fragility that could have facilitated SARS-CoV-2 replication and worsening of patient conditions could be speculated
Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web
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 visualizati…
Mapping Spatiotemporal Diffusion of Covid-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities
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 rel…
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
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 histori…
Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine
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 chan…
A Framework for Classifying Participant Motivation that Considers the Typology of Citizen Science Projects
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 pa…
Processing LiDAR Data
Point cloud filtering, or ground filtering, is the main preliminary step in Light Detection and Ranging (LiDAR) data processing, and allows the separation of ground and nonground points. It is the basis for the generation of digital terrain models (DTMs) and the identification of ground features. Other steps in DTM generation, starting from the point cloud, are similar to those generally applied to field measurements: error filtering, resampling …
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Processing LiDAR Data
Point cloud filtering, or ground filtering, is the main preliminary step in Light Detection and Ranging (LiDAR) data processing, and allows the separation of ground and nonground points. It is the basis for the generation of digital terrain models (DTMs) and the identification of ground features. Other steps in DTM generation, starting from the point cloud, are similar to those generally applied to field measurements: error filtering, resampling …
Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web
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 visualizati…
Mapping Spatiotemporal Diffusion of Covid-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities
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 rel…
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
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 histori…
Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine
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 chan…
A Framework for Classifying Participant Motivation that Considers the Typology of Citizen Science Projects
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 pa…
Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of Covid-19 Diffusion in the Lombardy Region (Italy)
this is the first study relating COVID-19 estimated speed of diffusion with indicators of exposure to NH 3 . As NH 3 could induce oxidative stress, its role in creating a pre-existing fragility that could have facilitated SARS-CoV-2 replication and worsening of patient conditions could be speculated
Population Space–Time Patterns Analysis and Anthropic Pressure Assessment of the Insubric Lakes Using User-Generated Geodata
Human activities are one of the main causes of lake-water pollution and eutrophication. The study of human pressure around lakes is of importance to understand its effects on the lakes natural resources. Social media data is a valuable space–time-resolved information source to detect human dynamics. In this study, user-generated geodata, namely users’ location records provided by the Facebook Data for Good program, are used to assess population p…
Generating High-Granularity Covid-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning
The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Servic…
Development of a Voice Virtual Assistant for the Geospatial Data Visualization Application on the Web
Voice assistants can elevate interaction in geospatial data web platforms. This research introduces a voice assistant in the BStreams platform and focuses on understanding user commands in the geospatial domain. We developed a specialised geospatial discourse framework through structured prototyping. A survey with 66 participants revealed prevalent English geospatial terminologies. Using ChatGPT, we found the term suggestions aligned with survey …
Estimating Daily NO2 Ground Level Concentrations Using Sentinel-5P and Ground Sensor Meteorological Measurements
Environmental and health deterioration due to the increasing presence of air pollutants is a pressing topic for governments and organizations. Institutions such as the European Environment Agency have determined that more than 350,000 premature deaths can be attributed to atmospheric pollutants. The measurement of trace gas atmospheric concentrations is key for environmental agencies to fight against the decreased deterioration of air quality. NO…
Investigating the Performance of Open-Vocabulary Classification Algorithms for Pathway and Surface Material Detection in Urban Environments
Mapping pavement types, especially in sidewalks, is essential for urban planning and mobility studies. Identifying pavement materials is a key factor in assessing mobility, such as walkability and wheelchair usability. However, satellite imagery in this scenario is limited, and in situ mapping can be costly. A promising solution is to extract such geospatial features from street-level imagery. This study explores using open-vocabulary classificat…
Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models
The Ca Mau Peninsula, situated in the Mekong Delta of Vietnam, features low-lying terrain. In addition to the challenges posed by climate change, land subsidence in the area is exacerbated by the overexploitation of groundwater and intensive agricultural practices. In this study, we assessed the land subsidence susceptibility in the Ca Mau Peninsula utilizing three boosting machine learning models: AdaBoost, Gradient Boosting, and Extreme Gradien…
State-of-art in modelling particulate matter (PM) concentration: A scoping review of aims and methods
Air pollution is the one of the most significant environmental risks to health worldwide. An accurate assessment of population exposure would require a continuous distribution of measuring ground-stations, which is not feasible. Therefore, significant efforts are spent in implementing air-quality models. However, a complex scenario emerges, with the spread of many different solutions, and a consequent struggle in comparison, evaluation and replic…
High-Resolution Global Land Cover Maps and Their Assessment Strategies
Global High-Resolution Land Cover Maps (GHRLCs), characterized by spatial resolutions higher than 30 m per pixel, have become essential tools for environmental monitoring, urban planning, and climate modeling. Over the past two decades, new GHRLCs have emerged, offering increasingly detailed and timely representations of Earth’s surface. This review provides an in-depth analysis of recent developments by examining the data sources, methodologies,…
Characterising Active Mobility in Urban Areas Through Street Network Indices
In the context of sustainable development, the concept of active mobility plays a key role in modern urban areas. To evaluate active mobility in these areas, we formulate a framework for characterising active mobility by calculating street network indices using global, free, and open data. This framework comprises the download and processing of pedestrian, cycling, driving, and public transport street networks from OpenStreetMap, the selection of…
Bridging Humanitarian Mapping and the Sustainable Development Goals
The Sustainable Development Goals (SDGs) have become the global framework for evaluating the effectiveness of humanitarian projects. Humanitarian mapping is considered a popular voluntary geographic information technique that provides data for disaster response. Although humanitarian mapping has contributed significantly to the SDGs, there is a lack of in-depth studies on the state of this relationship. This paper aims to assess the potential rel…
Computer Science (14 works) · Geography (12 works) · Cartography (6 works) · Environmental Science (6 works) · Data mining (5 works) · Data science (4 works) · Geospatial analysis (4 works) · Medicine (4 works) · Remote sensing (4 works) · Air Quality and Health Impacts (3 works)