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El ruido ambiental, es una de las principales formas de contaminación en las ciudades y que afecta la calidad de vida de las personas. En este artículo, se presenta una metodología que utiliza un enfoque basado en la Información Geográfica Voluntaria (VGI, por sus siglas en inglés) para el monitoreo, análisis y predicción del ruido ambiental, lo cual puede resultar muy útil para plantear alternativas que mejoren la vida en una ciudad. El presente trabajo considera las fases de adquisición de los datos, análisis y procesamiento de los datos, así como la visualización de la información, considerando la temporalidad de los mismos y tomando en cuenta niveles de análisis a nivel macro y micro para la superficie de estudio. Se presentan algunos detalles del diseño y desarrollo de un sistema de información geográfica, compuesto por una aplicación web de mapas, una aplicación para dispositivos móviles denominada “NoiseMonitor”, el análisis geoespacial y los métodos de aprendizaje automático (máquinas de soporte vectorial y redes neuronales artificiales) para la predicción de ruido ambiental; utilizando información contextual; es decir, algunos datos relacionados con la ciudad. Se busca aprovechar la disposición de los ciudadanos de participar colaborativamente para monitorear su entorno y ser considerados como sensores humanos, lo cual a diferencia con los enfoques tradicionales, el costo asociado al desarrollo e implementación de este proyecto es mucho menor.

More than 250 years ago, Giovanni Battista Nolli, an Italian architect, engineer and cartographer, was concerned with how and where space is or is not publicly accessible. In his map 'La nuova topografia di Roma Comasco', he mapped publicly accessible interior and exterior spaces of Rome with an impressively high level of detail as a figure-ground map. Since Nolli’s time, both the character and diversity of public spaces as well as cartographic technology have changed. This research project aims to adapt some ideas behind Nolli’s map for today’s circumstances on the basis of open data, and seeks to develop methods for processing volunteered geographical information from OpenStreetMap (OSM) to identify, categorize, and map public spaces based on thematic and geometric information. [ABSTRACT FROM AUTHOR]

An essential task of a state is to provide everything for the essential needs of its citizens. This includes the provision of a functional infrastructure and information on land, transport routes and topography, as summarised under the term geospatial base data or Fundamental Geospatial Data Themes (UN-GGIM 2019, European Parliament and of the Council 2007). The task of the authorities established for this purpose, such as the National Mapping and Cadastral Agencies, is to provide this base data, while complying with certain quality standards. These data are needed, among other things, for planning purposes, to secure land transactions, for disaster preparedness, generally to support government and private activities. It should be noted that special traceable quality characteristics can be attributed to these official data, e.g. with regard to the structure and quality of the data and their spatial coverage, often also with regard to update cycles and methodology and licensing conditions. Generally, these are also provided as meta-data according to ISO 19115. [ABSTRACT FROM AUTHOR]

Volunteered Geographic Information (VGI) such as data derived from the OpenStreetMap (OSM) project is a popular data source for freely available geographic data. Normally, untrained contributors gather these data. This fact is frequently a cause of concern regarding the quality and usability of such data. In this study, the quality of OSM land use and land cover (LULC) data is investigated for an area in southern Germany. Two spatial data quality elements, thematic accuracy and completeness are addressed by comparing the OSM data with an authoritative German reference dataset. The results show that the kappa value indicates a substantial agreement between the OSM and the authoritative dataset. Nonetheless, for our study region, there are clear variations between the LULC classes. Forest covers a large area and shows both a high OSM completeness (97.6%) and correctness (95.1%). In contrast, farmland also covers a large area, but for this class OSM shows a low completeness value (45.9%) due to unmapped areas. Additionally, the results indicate that a high population density, as present in urbanized areas, seems to denote a higher strength of agreement between OSM and the DLM (Digital Landscape Model). However, a low population density does not necessarily imply a low strength of agreement.

The integration of user-generated content made in a collaborative environment is being increasingly considered a valuable input to reference maps, even from official map agencies such as USGS and Ordnance Survey. In Brazil, decades of lack of investment has resulted in a topographic map coverage that is both outdated and unequally distributed throughout the territory. This paper aims to analyze the spatial distribution of updates of OpenStreetMap in rural and urban areas in the country to understand the patterns of user updates and its correlation with other economic and developmental variables. This analysis will contribute to generating the knowledge needed in order to consider the use of this data as part of a reference layer of the National Spatial Database Infrastructure as well to design strategies to encourage user action in specific areas.

We aim to describe and report on a novel graft preservation technique using a human gelatin thrombin matrix with rifampin for the treatment of vascular graft infections. Eight patients with vascular graft infections were included, one with bilateral infections, for a total of nine cases from January 2016 through June 2021. All the patients underwent wound exploration and placement of human gelatin thrombin matrix with rifampin. No deaths or allergic reactions had been reported at the 30-day follow-up, with only one major amputation. The graft and limb salvage rates were 77.8% at the 1-year follow-up. The mean time to a major amputation was 122 days, and the mean time to graft excision was 30 days., Competing Interests: None., (© 2023 The Author(s).)

Pluvial floods are increasingly threatening urban environments worldwide due to human-induced climate change. High-resolution, state-of-the-art pluvial flood models are urgently needed to inform climate change adaptation and disaster risk reduction measures but are generally not empirically tested because of the rarity of local high-intensity precipitation events and the lack of monitoring capabilities. Volunteered Geographic Information (VGI) collected by professionals, non-professionals and citizens and made available on the internet can be used to monitor the dynamic extent of a pluvial flood during and after an extreme rain event but is sometimes considered to be unreliable. In this paper, we explore the general utility of VGI to evaluate the performance of pluvial flood models and gain new insights to improve these models. As background for our research, we use the capital city of Budapest, which recently suffered three heavy rainfall events in just five years (2015, 2017 and 2020). For each pluvial flood event, we collected photographic evidence from different online media sources and estimated the associated water depths at various locations in the city from the image context. These were compared with the results of a 2D pluvial flood model that has been shown to provide comparable results to other state-of-the-art inundation models and is easily transferred to other urban areas due to its reliance on open data sources. We introduce a general methodology for comparing VGI with model data by probing different spatial resolutions. Our findings highlight untapped potential and fundamental challenges in using VGI for model evaluation. It is proposed that VGI may become an essential tool and improve the confidence in model-based risk assessments for climate change adaptation and disaster risk reduction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Abstract Public participatory mapping is a method of crowdsourcing where the lay public can contribute spatial information for a range of applications including conservation planning. When used to collect wildlife observation data, participatory mapping becomes a type of "geographic citizen science" that involves collaboration with members of the public. While the potential of crowdsourcing to assist in wildlife conservation appears to be large, the quality and validity of the observational data collected remain a key concern. In this study, we examined the quality and validity of spatial data collected in a public participatory mapping project implemented in northern New South Wales (Australia) in 2018 where the public was asked to identify and map the location and frequency of koala (Phascolarctos cinereus) sightings using an internet mapping application. The iconic koala is a nationally-listed threatened species and has wide public recognition, making it an ideal test of our approach to examining the value of citizen science for wildlife. We assessed the validity of koala observation data from two perspectives of validity-as-accuracy (positional accuracy and data completeness) and validity-as-credibility (characteristics of spatial data contributors). To assess validity-as-accuracy , we analysed the distribution of citizen observations of koala sightings compared to an expert-derived probability distribution of koalas (likelihood model). To assess validity-as-credibility , we analysed the survey data to determine which participant characteristics increased the credibility of observational data. We found significant spatial association between crowdsourced koala observations and the likelihood model to validate koala locations, but there was under-reporting in more rural, remote areas. Significant variables contributing to accuracy in koala observations included participant knowledge of koalas, age, length of residence, and formal education. We also compared the crowdsourced results to a field-based citizen science koala observation project implemented in the same region and found crowdsourced participatory mapping provided comparable, if not superior results. Crowdsourced koala observations can augment field-based koala research by covering large geographic areas while engaging a broader public in conservation efforts. However, effective geographic citizen science projects require a significant commitment of resources, including the creation of community partnerships, to obtain high quality spatial data. Highlights • Evaluates validity of crowdsourced observation data for wildlife conservation (koala) • Compared accuracy of citizen observations against authoritative koala distribution model • Analysed citizen characteristics as predictors of koala observation accuracy • Found significant spatial association between citizen observations and koala model • Participant knowledge of koalas, age, length of residence, and formal education were related to observation accuracy [ABSTRACT FROM AUTHOR]

Abstract The terms NIMBY (Not-In-My-Back-Yard) and YIMBY (Yes-In-My-Back-Yard) describe negative and positive attitudes toward proposed development projects respectively. These attitudes are posited to be influenced by geographic (spatial) discounting wherein the distance from domicile may contribute to local opposition or support. In contrast to specific development projects, the potential influence of NIMBY/YIMBY in a general land use planning process has not been systematically evaluated. In this study, we analyzed empirical data from a public participation GIS (PPGIS) process implemented for a general plan revision to examine the evidence for geographic discounting for a range of land uses using mapped preferences by community residents. Using distance analysis, we found significant evidence for geographic discounting by land use type with variable discount rates influenced by location of residence and the spatial configuration of land use in the planning area represented by zoning. The findings were consistent with NIMBY/YIMBY expectations with the exception of residential development where the results were more ambiguous. Residents want future land uses with amenities (open space, recreation, and trails) closer to domicile and more intensive, developed land uses (commercial, tourism, events, parking) further away. The findings have potentially broad implications because general/comprehensive planning—a requirement of most local governments in the U.S.—is operationalized through land use zones that appear subject to spatial discounting and the manifestation of potential NIMBY/YIMBY effects in the planning process. Future research should examine other planning contexts such as large urban areas with a greater diversity of land uses. Highlights • Describes participatory mapping process for general land use planning. • Examines evidence for geographic (spatial) discounting by land use category. • Finds different spatial discount rates apply to different types of land use. • Spatial discounting influenced by location of residence and current land use. • Presents implications for general land use planning and zoning processes. [ABSTRACT FROM AUTHOR]

As cities face increasing pressure to develop long-term sustainability strategies, the need for detailed quantitative data on urban resources, and their behaviour over time, has become critical. Building stocks are a city's largest socio-cultural and economic resource and account for around 40% of total energy consumption in developed countries. Despite ongoing problems with access to data on stock composition and dynamics, advances are now being made, particularly within sustainability science. Automated approaches to the analysis of historical building attribute data are also allowing long-term patterns of change in cities to be better understood. This is of significance to the conservation sector, and to the development of effective conservation strategies. At the same time, knowledge held by the conservation sector is of growing importance to sustainability science. This paper selects specific advances within this new research landscape, and identifies their importance in developing a more scientific, data-driven approach to the analysis of older stock. It concludes with an introduction to a new type of data collection and visualisation platform being developed for London, as a result of this review. [ABSTRACT FROM AUTHOR]

Zoning is a ubiquitous land use planning and regulatory mechanism whose purpose is to provide for orderly community growth and development by segregating land uses that are deemed incompatible. The delineation of zones and related land use ordinances are traditional components of an expert-driven, local government process that produces a general or comprehensive land use plan as required by law. Public participation in the development of general land use plans has rarely used participatory mapping methods that engage the general public to explicitly inform zoning decisions. In this study, we demonstrate how participatory mapping methods can assess the consistency, compatibility, and potential conflict of zoning with public values and preferences in a general plan revision process using a coastal community in California as a case study. We describe the participatory mapping design, data collection, and data analyses in a workflow to illustrate the methods, and present the strengths and limitations of the approach for use in a general land use planning process. Future research should expand these methods to assess the potential effects of resident domicile and “NIMBYism” on the results, and importantly, assess the impact of public participatory mapping in land use decisions if actually implemented by local government authorities. [ABSTRACT FROM AUTHOR]

With the advancement of physical sensors and scientific modelling, we fortunately are able to track, monitor and even predict most of natural destructive forces, e.g. hurricanes and tornadoes. Compared to other natural disasters, earthquakes are particularly traumatic because they occur without explicit and timely warning and therefore are extremely difficult, if at all possible, to detect timely. Meanwhile, anomalous animal behaviours have been widely observed the day even several days before an Earthquake. Therefore, animals can be used as intelligent geo-sensors to tell or estimate when and where an earthquake will potentially occur. This paper presents a framework synthesizing crowdsourcing reports of anomalous animal behaviour from both active sources (designed mobile app and websites) and passive sources (social networks like Twitter, Facebook) for earthquake early prediction. To demonstrate the effectiveness of the proposed framework, a proof-of-concept prototype is then developed to collect, visualize, analyse and mine such crowdsourcing data to detect a potential earthquake. [ABSTRACT FROM AUTHOR]

The theory and practice of participatory mapping (PM) has expanded significantly over the last two decades with proliferation of a wide range of methods and applications. The potential for synthesis and integration across four broad domains of PM ( indigenous/rural/community , urban/regional , environmental/natural resource , and mapping technology ) was examined at the Participatory Mapping/GIS 2017 conference held at California Polytechnic State University (San Luis Obispo, USA) Jul 31-Aug 3, 2017. At the conference, PM leaders in each of the four domains participated in working groups to: (1) identify the key issues, including “barriers” and “knowledge gaps” that limit effective PM outcomes, and (2) identify the most important research priorities. This paper summarizes the findings of the working groups for the purpose of identifying common and unique challenges across the four PM domains and to discuss the desirability of stronger integration of PM knowledge and practice. In the indigenous/rural/community domain, achieving clarity in PM purpose and building trust in the process were identified as the most critical issues; in the environmental/natural resources domain, wider use and adoption of PM to inform policy and management decisions through stakeholder engagement was considered most important; and in the urban/regional domain, developing urban indicators and adapting PM to complex and heterogeneous urban environments were identified as important needs. The key issue in the domain of PM technology was understanding how technology influences PM usability and user behavior for the development and implementation of appropriate PM technology. The most significant cross-cutting theme to emerge across all PM domains was the need to evaluate PM outcomes to provide evidence of success. [ABSTRACT FROM AUTHOR]

Volunteered geographic information (VGI) can be considered a subset of crowdsourced data (CSD) and its popularity has recently increased in a number of application areas. Disaster management is one of its key application areas in which the benefits of VGI and CSD are potentially very high. However, quality issues such as credibility, reliability and relevance are limiting many of the advantages of utilising CSD. Credibility issues arise as CSD come from a variety of heterogeneous sources including both professionals and untrained citizens. VGI and CSD are also highly unstructured and the quality and metadata are often undocumented. In the 2011 Australian floods, the general public and disaster management administrators used the Ushahidi Crowd-mapping platform to extensively communicate flood-related information including hazards, evacuations, emergency services, road closures and property damage. This study assessed the credibility of the Australian Broadcasting Corporation’s Ushahidi CrowdMap dataset using a Naïve Bayesian network approach based on models commonly used in spam email detection systems. The results of the study reveal that the spam email detection approach is potentially useful for CSD credibility detection with an accuracy of over 90% using a forced classification methodology. [ABSTRACT FROM AUTHOR]

At the present, OpenStreetMap (OSM) is considered as one of the most successful and popular VGI (Volunteered Geographic Information) projects. It provides a platform that all the registered members coming from different areas in the world can cooperate with each other to mapping our world. Besides, OSM attracts more and more people, companies and even the governmental agencies because of its free and open source. Studies have proofed that both the quantity and quality of OSM data in several western countries, i.e. Germany, France and the Netherland are even better than the authority data. In recent years, the quantity of the OSM data and the number of contributors in China increased rapidly, but the overall distribution of OSM data is very fit with the distribution of population and the economic development and it displays an uneven development in different provinces and cities in China. Besides, the state of the OSM in China is just similar to that in Germany in 2010 in terms of data quantity and quality, although China is about 25 times to Germany regarding land area and the smartphone penetration in China and Germany does not have a large distance (51.7% to 68.8%). Why is the development of OSM in China so poor and backward when comparing that with western countries, although the environment in hardware and software in China are similar to the western countries? Attempting to answer this question, this paper presents a user survey in China. Mainly, knowledges and experiences about OSM and OSM contribution were asked in the user survey. The user survey was conducted both by paper and pen and by using online platform. Totally, over 1200 participants with the age range from 15 to 80 and a huge diverse of background took part in the user survey. In this paper, we would like to describe the design of the questions for the user survey at first. Then we will demonstrate the results of the user survey, as well as the analysis and conclusions, which can be drawn from the user survey. [ABSTRACT FROM AUTHOR]

A ‘fit-for-purpose’ approach is developed, tested and presented for cadastral surveys through increased owners’ participation using new technology and m-government services. Three case studies are reported, for urban, suburban and rural areas, with a combined use of two mobile applications: a commercial software package (ESRI’s Collector for ArcGIS) and an opensource self-developed application named BoundGeometry. The parameters of time, quality and accuracy are assessed and the identified difficulties are classified. It is concluded that the method is applicable both in developed and developing countries, and each time adjustable to the available infrastructure. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study that explored if and how an OpenStreetMap (OSM) data import task can contribute to OSM community growth. Different outreach techniques were used to introduce a building import task to three targeted OSM user groups. First, existing OSM members were contacted and asked to join the data import project. Second, several local community events were organized with Maptime Miami to engage local mappers in OSM contribution activities. Third, the import task was introduced as an extra credit assignment in two GIS courses at the University of Florida. The paper analyzes spatio-temporal user contributions of these target groups to assess the effectiveness of the different outreach techniques for recruitment and retention of OSM contributors. Results suggest that the type of prospective users that were contacted through our outreach efforts and their different motivations play a major role in their editing activity. Results also revealed differences in editing patterns between newly recruited users and already established mappers. More specifically, long-term engagement of newly registered OSM mappers did not succeed, whereas already established contributors continued to import and improve data. In general, we found that an OSM data import project can add valuable data to the map, but also that encouraging long-term engagement of new users, whether it be within the academic environment or outside, proved to be challenging. [ABSTRACT FROM AUTHOR]

Environmental noise is a big problem related to the environmental pollution in cities, which affects the quality of people life. In this paper, a methodology that uses an approach based on Volunteered Geographic Information (VGI) for the monitoring, analysis and prediction of environmental noise is proposed. It can be very useful to propose alternatives and initiatives that improve the life in a city. So, this work is composed of the following stages: data acquisition, analysis and, data processing, as well as the information visualization, considering the temporality of the same and taking into account macro and micro levels of analysis for the study surface. In addition, some details of the design and development of a geographic information system are presented, consisting of a web-mapping system, an application for mobile devices called "NoiseMonitor", geospatial analysis and machine learning methods (support vector machines and artificial neural networks) for the prediction of environmental noise; by using contextual information; that is, some data related to the city. This kind of work seeks to take advantage of the willingness of citizens to participate collaboratively to sense their environment and be considered as human sensors, which unlike traditional approaches, the cost associated with the development and implementation of this project is much lower. Likewise, a case study based on the Mexico City is presented and discussed, particularly the fourth quadrant of the Historic Center of the City, which is very representative for the variety of environmental noise that is generated in that area. The application domain of this approach is oriented towards big data from a collaborative perspective, Internet of Things and smart cities. [ABSTRACT FROM AUTHOR]

Data quality and fitness for purpose can be assessed by data quality measures. Existing ontologies of data quality dimensions reflect, among others, which aspects of data quality are assessed and the mechanisms that lead to poor data quality. An understanding of which source of information is used to judge about data quality and fitness for purpose is, however, lacking. This article introduces an ontology of data quality measures by their grounding, that is, the source of information to which the data is compared to in order to assess their quality. The ontology is exemplified with several examples of volunteered geographic information (VGI), while also applying to other geographical data and data in general. An evaluation of the ontology in the context of data quality measures for OpenStreetMap (OSM) data, a well-known example of VGI, provides insights about which types of quality measures for OSM data have and which have not yet been considered in literature. [ABSTRACT FROM AUTHOR]

Over the last decade new voluntary mapping patterns are commonly known as VGI – Volunteered Geographic Information – that is, geo-localized information created voluntarily and consciously by web users. These are supported by platforms such as OpenStreetMap that have been shown in many emergency cases and not, a valid source of data, such detailed to be used for rescue operations. Another completely open source platform that has revolutionized the world of geographic information and how to make reports is Ushaidi that through interactive maps represents testimonies, reports, diaries, and citizen reports.

The Heat Energy Assessment Technologies (HEAT) project uses high-resolution airborne thermal imagery, Geographic Object-Based Image Analysis (GEOBIA), and a Geoweb environment to allow the residents of Calgary, Alberta, Canada to visualize the amount and location of waste heat leaving their houses, communities, and the city. To ensure the accuracy of these measures, the correct emissivity of roof materials needs to be known. However, roof material information is not readily available in the Canadian public domain. To overcome this challenge, a unique Volunteered Geographic Information (VGI) application was developed using Google Street View that engages citizens to classify the roof materials of single dwelling residences in a simple and intuitive manner. Since data credibility, quality, and accuracy are major concerns when using VGI, a private Multiple Listing Services (MLS) dataset was used for cross-verification. From May–November 2013, 1244 volunteers from 85 cities and 14 countries classified 1815 roofs in the study area. Results show (I) a 72% match between the VGI and MLS data; and (II) in the majority of cases, roofs with greater than, or equal to five contributions have the same material defined in both datasets. Additionally, this research meets new challenges to the GEOBIA community to incorporate existing GIS vector data within an object-based workflow and engages the public to provide volunteered information for urban objects from which new geo-intelligence is created in support of urban energy efficiency.

There has been enormous progress in geospatial data acquisition in the last decade. Centralized data collection, mainly by land surveying offices and local government agencies, has changed dramatically to voluntary data provision by citizens. Among a broad list of initiatives dealing with user generated geospatial information, OpenStreetMap (OSM) is one of the most famous crowd-sourced products. It is believed that the quality of collected information remains a valid concern. Therefore, qualitative assessment of OSM data as the most significant instance of volunteered geospatial information (VGI) is a considerable issue in the geospatial information community. One aspect of VGI quality assessment pertains to its comparison with institutionally referenced geospatial databases. This paper proposes a new quality metric for assessment of VGI accuracy and as well as for quality analysis of OSM dataset by evaluating its consistency with that of a reference map produced by Municipality of Tehran, Iran. A gridded map is employed and heuristic metrics such as Minimum Bounding Geometry area and directional distribution (Standard Deviational Ellipse), evaluated for both VGI and referenced data, are separately compared in each grid. Finally, in order to have a specific output as an integrated quality metric for VGI, its consistency with ground-truth data is evaluated using fuzzy logic. The results of this research verify that the quality of OSM maps in the study area is fairly good, although the spatial distribution of uncertainty in VGI varies throughout the dataset.

Availability of up-to-date geographic datasets, including the information voluntarily created by ordinary people, is crucial for disaster management in areas affected by catastrophic events and humanitarian crises. OpenStreetMap (OSM) is the most popular Volunteered Geographic Information (VGI) project that has been extensively used for collaborative mapping in areas affected by natural disasters. This article provides an overview on how OSM data are exploited in disaster response scenarios and illustrates their specific application during the recent Italian earthquakes.

Volunteered geographic information (VGI) encourages citizens to contribute geographic data voluntarily that helps to enhance geospatial databases. VGI’s significant limitations are trustworthiness and reliability concerning data quality due to the anonymity of data contributors. We propose a data-driven model to address these issues on OpenStreetMap (OSM), a particular case of VGI in recent times. This research examines the hypothesis of evaluating the proficiency of the contributor to assess the credibility of the data contributed. The proposed framework consists of two phases, namely, an exploratory data analysis phase and a learning phase. The former explores OSM data history to perform feature selection, resulting in “OSM Metadata” summarized using principal component analysis. The latter combines unsupervised and supervised learning through K-means for user-clustering and multi-class logistic regression for user classification. We identified five major classes representing user-proficiency levels based on contribution behavior in this study. We tested the framework with India OSM data history, where 17% of users are key contributors, and 27% are unexperienced local users. The results for classifying new users are satisfactory with 95.5% accuracy. Our conclusions recognize the potential of OSM metadata to illustrate the user’s contribution behavior without the knowledge of the user’s profile information.

Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.

The southeastern coast of China suffers many typhoon disasters every year, causing huge casualties and economic losses. In addition, collecting statistics on typhoon disaster situations is hard work for the government. At the same time, near-real-time disaster-related information can be obtained on developed social media platforms like Twitter and Weibo. Many cases have proved that citizens are able to organize themselves promptly on the spot, and begin to share disaster information when a disaster strikes, producing massive VGI (volunteered geographic information) about the disaster situation, which could be valuable for disaster response if this VGI could be exploited efficiently and properly. However, this social media information has features such as large quantity, high noise, and unofficial modes of expression that make it difficult to obtain useful information. In order to solve this problem, we first designed a new classification system based on the characteristics of social medial data like Sina Weibo data, and made a microblogging dataset of typhoon damage with according category labels. Secondly, we used this social medial dataset to train the deep learning model, and constructed a typhoon disaster mining model based on a deep learning network, which could automatically extract information about the disaster situation. The model is different from the general classification system in that it automatically selected microblogs related to disasters from a large number of microblog data, and further subdivided them into different types of disasters to facilitate subsequent emergency response and loss estimation. The advantages of the model included a wide application range, high reliability, strong pertinence and fast speed. The research results of this thesis provide a new approach to typhoon disaster assessment in the southeastern coastal areas of China, and provide the necessary information for the authoritative information acquisition channel.

In recent years, volunteered-geographic-information (VGI) image data have served as a data source for various geographic applications, attracting researchers to assess the quality of these images. However, these applications and quality assessments are generally focused on images associated with geolocation through textual annotations, which is only part of valid images to them. In this paper, we explore the distribution pattern for most relevant VGI images of specific landmarks to extend the current quality analysis, and to provide guidance for improving the data-retrieval process of geographic applications. Distribution is explored in terms of two aspects, namely, semantic distribution and spatial distribution. In this paper, the term semantic distribution is used to describe the matching of building-image tags and content with each other. There are three kinds of images (semantic-relevant and content-relevant, semantic-relevant but content-irrelevant, and semantic-irrelevant but content-relevant). Spatial distribution shows how relevant images are distributed around a landmark. The process of this work can be divided into three parts: data filtering, retrieval of relevant landmark images, and distribution analysis. For semantic distribution, statistical results show that an average of 60% of images tagged with the building’s name actually represents the building, while 69% of images depicting the building are not annotated with the building’s name. There was also an observation that for most landmarks, 97% of relevant building images were located within 300 m around the building in terms of spatial distribution.

The fear of crime is an established research topic, not only in sociology, environmental psychology and criminology, but also in GIScience. Using spatial analysis to analyse patterns, explore hotspots and determine the significance of respective surveys is one reason for the increase in popularity of such research topics for geographers, cartographers and spatial data scientists. This paper presents the results of an intensive online map-based questionnaire with 1551 respondents from the city of Ostrava, Czech Republic. The respondents marked 3792 points associated with the fear of crime over a ten week period. The perception data were compared with recorded crime data acquired from police department records for the years 2015−2018. This paper explores the spatial autocorrelation from perceived hotspots and from recorded crime hotspots. Our findings fit into the literature confirming results about the locations that most frequently attract fear, but there is still room for more investigations regarding the links between recorded crime and the fear of crime.

Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by both approaches is often questionable, and automated procedures to check the quality are needed for this purpose. In the present study, a convolutional neural network (CNN) architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-based GIS environment designed specifically for a landslide CitSci project. The VGG16 has been used as the base model since it allows finetuning, and high performance could be achieved by selecting the best hyper-parameters. Although the training dataset was small, the proposed CNN architecture was found to be effective as it could identify the landslide photos with 94% precision. The accuracy of the results is sufficient for purpose and could even be improved further using a larger amount of training data, which is expected to be obtained with the help of volunteers.

This work presents an approach to road network extraction in remote sensing images. In our earlier work, we worked on the extraction of the road network using a multi-agent approach guided by Volunteered Geographic Information (VGI). The limitation of this VGI-only approach is its inability to update the new road developments as it only follows the VGI. In this work, we employ a deep learning approach to update the road network to include new road developments not captured by the existing VGI. The output of the first stage is used to train a Convolutional Neural Network (CNN) in the second stage to generate a general model to classify road pixels. Post-processing is used to correct the undesired artifacts such as buildings, vegetation, occlusions, etc. to generate a final road map. Our proposed method is tested on the satellite images acquired over Abu Dhabi, United Arab Emirates and the aerial images acquired over Massachusetts, United States of America, and is observed to produce accurate results.

Crowdsourcing can be a starting-point for the process of spatial data-gathering in the field of land management, cadastral systems and other databases holding spatial data. Such voluntary supply of spatial data on the part of users might significantly improve the acquisition and especially the updating of spatial databases. The crowdsourcing process also has perceivable influence on the quality of formal spatial data. Therefore, this research domain is, together with information technology, a highly promising developing concept with respect to the effectiveness of such activities. Voluntary massive data acquisition, besides professional doubts, also brings several legal, organizational, economic and social concerns that should be resolved in parallel with rapid technological development. Thus far, international cooperation in the domain of voluntary massive spatial data supply does not yet exist to an appropriate extent. The COST ICT action IC1203 "European Network Exploring Research into Geospatial Information Crowdsourcing: software and methodologies for harnessing geographic information from the crowd" (ENERGIC) enables the connections and exchange of scientific methodology and technological sources in the field of massive data acquisition from informal sources.

Crowdsourcing (geo-) information and participatory GIS are among the current hot topics in research and industry. Various projects are implementing participatory sensing concepts within their workflow in order to benefit from the power of volunteers, and improve their product quality and efficiency. Wheelmap is a crowdsourcing platform where volunteers contribute information about wheelchair-accessible places. This article presents information about the technical framework of Wheelmap, and information on how it could be used in projects dealing with accessibility and/or multimodal transportation. [ABSTRACT FROM AUTHOR]

Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging "Big Data" era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets. [ABSTRACT FROM AUTHOR]

Volunteered geographic information (VGI) has been widely adopted as an alternative for authoritative geographic information in disaster management considering its up-to-date data. OpenStreetMap, in particular, is now aiming at crisis mapping for humanitarian purpose. This paper illustrated that natural disaster played an essential role in updating OpenStreetMap data after Haiti was hit by Hurricane Matthew in October, 2016. Spatial-temporal analysis of updated OSM data was conducted in this paper. Correlation of features was also studied to figure out whether updates of data were coincidence or the results of the hurricane. Spatial pattern matched the damaged areas and temporal changes fitted the time when disaster occurred. High level of correlation values of features were recorded when hurricane occurred, suggesting that updates in data were led by the hurricane. [ABSTRACT FROM AUTHOR]

Over the last decade new voluntary mapping patterns are commonly known as VGI - Volunteered Geographic Information - that is, geo-localized information created voluntarily and consciously by web users. These are supported by platforms such as OpenStreetMap that have been shown in many emergency cases and not, a valid source of data, such detailed to be used for rescue operations. Another completely open source platform that has revolutionized the world of geographic information and how to make reports is Ushaidi that through interactive maps represents testimonies, reports, diaries, and citizen reports. [ABSTRACT FROM AUTHOR]

The Internet is increasingly a source of data for geographic information systems, as more data becomes linked, available through application programing interfaces (APIs), and more tools become available for handling unstructured web data. While many web data extraction and structuring methods exist, there are few examples of comprehensive data processing and analysis systems that link together these tools for geographic analyses. This paper develops a general approach to the development of spatial information context from unstructured and informal web data sources through the joint analysis of the data's thematic, spatial, and temporal properties. We explore the utility of this derived contextual information through a case study into maritime surveillance. Extraction and processing techniques such as toponym extraction, disambiguation, and temporal information extraction methods are used to construct a semi-structured maritime context database supporting global scale analysis. Geographic, temporal, and thematic content were analyzed, extracted and processed from a list of information sources. A geoweb interface is developed to allow user visualization of extracted information, as well as to support space-time database queries. Joint keyword clustering and spatial clustering methods are used to demonstrate extraction of documents that relate to real world events in official vessel information data. The quality of contextual geospatial information sources is evaluated in reference to known maritime anomalies obtained from authoritative sources. The feasibility of automated context extraction using the proposed framework and linkage to external data using standard clustering tools is demonstrated. [ABSTRACT FROM AUTHOR]

Land information is one of the basic requirements for land management activities such as land consolidation. However, the dearth of land information on customary lands limits the development and application of land consolidation. This paper presents and discusses the results of an experiment carried out to test the potential of participatory land administration applied on customary lands in support of land consolidation. A brief overview of the evolution of crowdsourced, voluntary, and participatory approaches is provided alongside newly related insights into neogeography and neo-cadastre, and fit-for-purpose and pro-poor land administration. The concept of participatory land administration is then developed in this context. The area of the experiment is in Northern Ghana where the process was developed together with the local farming community. The study involved collecting land information relating to farms over a two-week period, using a mobile app and a satellite image, based on participatory land administration. The results show that Participatory Land Administration can potentially support land consolidation, though further investigation is needed on how it can be integrated into the formal land registration system, into an actual land consolidation project. [ABSTRACT FROM AUTHOR]