Volunteered Geographic Information Data Research Articles

Rapid natural hazard extent estimation from twitter data: investigation for hurricane impact areas

Natural hazards have occurred more frequently in the past years and pose a severe risk to human life. Their extents and, thereby, the most heavily affected areas must be estimated at the earliest to limit damages or initiate rescue services. For such estimations, a widely available data source, which is comparatively responsive to short-time changes, is needed and provided by volunteered geographic information (VGI) data. Tropical cyclones are natural hazard events that can cause enormous spatially extended damage. In this study, we introduce Machine Learning approaches such as Extremely Randomized Tree (ET) and Geographically Weighted Regression for estimating hurricane-impacted regions from VGI data. In addition to the general approximate track extent estimation, we also evaluate the possibilities of temporal estimation of track development from VGI data. Different scenarios are evaluated, and we find that the results mainly depend on the choice of the geographical splits for training and test data for the underlying regression task. Suitable splits lead to R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 99% in the best cases with the ET model. The estimation results are satisfying when considering the temporal aspect and represent a use-case scenario. Such a combination of Machine Learning approaches and VGI is a simple and fast approach for early natural hazard estimation.

An MIU-based deep embedded clustering model for urban functional zoning from remote sensing images and VGI data

Urban functional zoning offers valuable insights into urban morphology and sustainable development. However, the conventional fixed spatial units, such as blocks and grids, cannot easily capture the morphological characteristics inherent in functional union and separation during urban evolution. In this paper, by taking advantage of remote sensing images and geospatial big data, we propose a minimum identification unit (MIU)-based urban functional zoning model. This approach integrates the deep embedded clustering of buildings to generate the spatial unit segmentation, and then identifies the urban function by generating semantic vectors with the Word2Vec model. The effectiveness of the proposed method was tested in the city of Wuhan in China. The results highlight that MIUs provide a more flexible and suitable unit for segmenting urban functional zones compared to traditional street blocks. The proposed method is a feasible way to deal with the semantic redundancy of volunteered geographic information (VGI) data when identifying urban function, and the quality issue only has a significant impact on the minor functional types. Moreover, the building clustering results can effectively reveal the fine-scale urban structure, especially for the administration, manufacturing, and residential types. This demonstrates the potential of our approach in enhancing the understanding of urban morphology and supporting sustainable urban development.

ChineseCTRE: A Model for Geographical Named Entity Recognition and Correction Based on Deep Neural Networks and the BERT Model

Social media is widely used to share real-time information and report accidents during natural disasters. Named entity recognition (NER) is a fundamental task of geospatial information applications that aims to extract location names from natural language text. As a result, the identification of location names from social media information has gradually become a demand. Named entity correction (NEC), as a complementary task of NER, plays a crucial role in ensuring the accuracy of location names and further improving the accuracy of NER. Despite numerous methods having been adopted for NER, including text statistics-based and deep learning-based methods, there has been limited research on NEC. To address this gap, we propose the CTRE model, which is a geospatial named entity recognition and correction model based on the BERT model framework. Our approach enhances the BERT model by introducing incremental pre-training in the pre-training phase, significantly improving the model’s recognition accuracy. Subsequently, we adopt the pre-training fine-tuning mode of the BERT base model and extend the fine-tuning process, incorporating a neural network framework to construct the geospatial named entity recognition model and geospatial named entity correction model, respectively. The BERT model utilizes data augmentation of VGI (volunteered geographic information) data and social media data for incremental pre-training, leading to an enhancement in the model accuracy from 85% to 87%. The F1 score of the geospatial named entity recognition model reaches an impressive 0.9045, while the precision of the geospatial named entity correction model achieves 0.9765. The experimental results robustly demonstrate the effectiveness of our proposed CTRE model, providing a reference for subsequent research on location names.

Enhancing DSS Exploitation Based on VGI Quality Assessment: Conceptual Framework and Experimental Evaluation

The latest advances in spatial information technology have led to the emergence of Volunteered Geographic Information (VGI) as enrichment to existing spatial data sources. Additionally, Decision Support Systems (DSS) are among the fields that have seen major advances. Volunteered Geographic Information (VGI) has great potential as a valuable data source to decision support systems. Several studies have been proposed to integrate VGI data into DSS. However, as VGI data may have different levels of quality, integrating VGI data with poor quality may affect the decision-making process. In fact, VGI data with poor quality. that are obsolete or incomplete, could, if integrated into a spatial DSS, lead to inappropriate analysis results. This paper presents an approach that aims to enhance spatial DSS analysis and exploitation by integrating high quality VGI data that are appropriate to the user requirements, and that have a good indicator completeness and time relevance. The approach introduces a conceptual framework that evaluates VGI data quality and integrates only high quality VGI data into spatial DSS. The proposed approach is experimented on a road maintenance project in Grand-Tunis. We develop the Map-Report prototype, and we evaluate the efficiency of our approach in enhancing data analysis and exploitation in spatial DSS by reducing the error rate and providing accurate and precise analysis results.

SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images

Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building objects, attributes of points of interest (POIs) tags, locations of cellphone users and textures of VHR images. The dimensionality of SALT features was reduced by the autoencoder, and the compressed features were applied to train the ensemble learning model composed of multiple classifiers for optimizing the urban functional zone classification. The effectiveness of the proposed framework was tested in an urbanized region of Nanchang City. The results indicated that the SALT features considering population dynamics and building shapes are comprehensive and feasible for urban functional zone mapping. The autoencoder has been proven efficient for dimension reduction of the original SALT features as it significantly improves the classification of urban functional zones. Moreover, the ensemble learning outperforms other machine learning models in terms of the accuracy and robustness when dealing with multi-classification tasks.

VGI and Satellite Imagery Integration for Crisis Mapping of Flood Events

Timely mapping of flooded areas is critical to several emergency management tasks including response and recovery activities. In fact, flood crisis maps embed key information for an effective response to the natural disaster by delineating its spatial extent and impact. Crisis mapping is usually carried out by leveraging data provided by satellite or airborne optical and radar sensors. However, the processing of these kinds of data demands experienced visual interpretation in order to achieve reliable results. Furthermore, the availability of in situ observations is crucial for the production and validation of crisis maps. In this context, a frontier challenge consists in the use of Volunteered Geographic Information (VGI) as a complementary in situ data source. This paper proposes a procedure for flood mapping that integrates VGI and optical satellite imagery while requiring limited user intervention. The procedure relies on the classification of multispectral images by exploiting VGI for the semi-automatic selection of training samples. The workflow has been tested with photographs and videos shared on social media (Twitter, Flickr, and YouTube) during two flood events and classification consistency with reference products shows promising results (with Overall Accuracy ranging from 87% to 93%). Considering the limitations of social media-sourced photos, the use of QField is proposed as a dedicated application to collect metadata needed for the image classification. The research results show that the integration of high-quality VGI data and semi-automatic data processing can be beneficial for crisis map production and validation, supporting crisis management with up-to-date maps.

A Risk-Based Approach for Enhancing the Fitness of Use of VGI

Volunteered Geographic Information (VGI) phenomena offer an alternative or supplement to the authoritative mechanism of geospatial data acquisition. It aims to allow people without professional geospatial skills or knowledge to participate in the geospatial data collection. VGI has been boosted by recent advances in geospatial technology and applications. VGI applications have shown great potential in various areas such as disaster management and public health. However, VGI suffers from a lack of quality assurance, because VGI contributors may lack knowledge of the geospatial domain and credibility. Moreover, VGI data may have different levels of detail and precision, and may have been collected for different purposes. Appropriate VGI data for a specific application may be less appropriate for another application. End-users may use VGI data without being aware of its appropriateness to their requirements. This may cause a risk that arises when end-users inappropriately use, or be uncertain about using VGI data in their applications. This risk may undermine the VGI project, in which end-users are involved. This paper proposes an approach that aims to enhance VGI quality assurance by measuring the spatio-semantic similarity between user requirements and provided VGI, and to evaluate the fitness-for-use of VGI. The proposed approach is based on an algorithm to help VGI end-users to deal with the risks related to the quality of VGI data. The approach helps end-user make appropriate decisions about VGI data (e.g., considering or not considering VGI data and being careful when using VGI data).

B-DRIVE: A blockchain based distributed IoT network for smart urban transportation

In this paper, I present B-DRIVE—a blockchain-based distributed IoT (Internet of Things) network for smart urban transportation. The network is designed to connect a large fleet of IoT devices, installed on various vehicles and roadside infrastructures, to distributed data storage centers, called as Full-Nodes, to log and disseminate sensor generated data. It connects devices from around the city to multiple Full-Nodes to log timestamped data into the blockchain. These sensors vary from GPS (Global Positioning System), air quality meter, gyrometer to speed cameras in order to facilitate efficient urban mobility. The three identified hardware layers that comprise the network are the IoT layer, Storage layer, and User layer. They consist of Moving/Static-Nodes, Full-Nodes, and Smart devices, respectively. The Moving/Static-Nodes are primarily made up of moving vehicles and road-side infrastructures, respectively, thus acting as various data sources. Whereas, Full-Nodes and Smart devices are institutions and mobile phones, acting as data handler/disseminator and navigator/data visualizer, respectively. The data, or data blocks, received by Full-Nodes get appended into Full and Running-Blockchain, meant for specific purposes. The network is designed to be free from any block mining activity. It provides open access to anonymous sensor data to end-users, especially scientists, policy-makers and entrepreneurs, to develop innovative urban transportation solutions. It is believed that a system like B-DRIVE, along with existing VANETs (Vehicular Ad-hoc NETworks), is capable of answering some of the current urban transportation issues around traffic congestion, navigation, and vehicle parking. Other applications of blockchain data could vary from user activity mapping to VGI (volunteered geographic information) data quality assessment. Two identified limitations of the presented architecture are the low processing power of current IoT devices and the lack of urban IoT infrastructure.

Anomaly detection for volunteered geographic information: a case study of Safecast data

ABSTRACT Volunteered Geographic Information (VGI), defined as geographic information contributed voluntarily by individuals, has grown exponentially with the aid of ubiquitous GPS-enabled technologies. VGI projects have generated a large amount of geographic data, providing a new data source for scientific research. However, many scientists are concerned about the quality of VGI data for research, given the lack of rigorous and systematic quality control procedures. This study contributes to the improvement of quality control procedures by proposing a Cross-Volunteer Referencing Anomaly Detection (CVRAD) method to filter anomalous data, using the crowdsourced Safecast radiation data as a case study. The anomaly detection method is validated using two data sets: (1) an official radiation survey data set collected by the KURAMA car-borne system, (2) a set of anomalous Safecast measurements filtered by Safecast moderators. The validation results show that the proposed CVRAD method outperformed the 1.5 IQR benchmark method in minimizing the overall measurement error and detecting abnormal imports of Safecast measurements, thus demonstrating the effectiveness of the proposed method in improving the overall accuracy of crowdsourced radiation measurements.

Digital Agora – Knowledge acquisition from spatial databases, geoinformation society VGI and social media data

The essence of VGI (volunteered geographic information) lies not only in obtaining spatial data from residents (the classic “citizens as sensors” approach (Goodchild, 2007), but also in stimulating them to be active as far as social (geo)participation shaping the development of urban space. The contemporary challenge of the “smart city” era is the creation of a Digital Agora, which facilitates (and analytically supports) not only social debate on spatial planning, but also enabling the acquisition of spatial knowledge. The article presents the application of geoinformation technology and spatial data mining in the analysis of VGI data and knowledge acquisition for the purpose of sustainable urban development. The aim of the article is to propose a general model of digital social debate on the participatory creation of a smart city and sustainable spatial development. This approach can be used to collect spatial data and transform them into useful spatial knowledge. The article discusses two cases of using the Digital Agora model to collect and analyze spatial data.

Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

Towards Development of a Real-Time Point Feature Quality Assessment Method for Volunteered Geographic Information Using the Internet of Things

One of the most important challenges of volunteered geographic information (VGI) is the quality assessment. Existing methods of VGI quality assessment, either assess the quality by comparing a reference map with the VGI map or deriving the quality from the metadata. The first approach does not work for a real-time scenario and the latter delivers approximate values of the quality. Internet of Things (IoT) networks provide real-time observations for environment monitoring. Moreover, they publish more precise information than VGI. This paper introduces a method to assess the quality of VGI in real-time using IoT observations. The proposed method filters sensor observation outliers in the first step. Then it matches sensors and volunteers’ relationships in terms of location, time, and measurement type similarity using a hypergraph model. Then the quality of matched data is assessed by calculating positional and attribute accuracy. To evaluate the method, VGI data of the water level and quality in Tarashk–Bakhtegan–Maharlou water basin is studied. A VGI quality map of the data is assessed by a referenced authoritative map. The output of this step is a VGI quality map, which was used as a reference to check the proposed method quality. Then this reference VGI quality map and the proposed method VGI quality map are compared to assess positional and attribute accuracy. Results demonstrated that 76% of the method results have less than 20 m positional error (i.e., difference with the reference VGI quality map). Additionally, more than 92% of the proposed method VGI data have higher than 90% attribute accuracy in terms of similarity with the reference VGI quality map. These findings support the notion that the proposed method can be used to assess VGI quality in real-time.

Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing

With the development of Web2.0 and mobile Internet, urban residents, a new type of “sensor”, provide us with massive amounts of volunteered geographic information (VGI). Quantifying the spatial patterns of VGI plays an increasingly important role in the understanding and development of urban spatial functions. Using VGI and social media activity data, this article developed a method to automatically extract and identify urban spatial patterns and functional zones. The method is put forward based on the case of Beijing, China, and includes the following three steps: (1) Obtain multi-source urban spatial data, such as Weibo data (equivalent to Twitter in Chinese), OpenStreetMap, population data, etc.; (2) Use the hierarchical clustering algorithm, term frequency-inverse document frequency (TF-IDF) method, and improved k-means clustering algorithms to identify functional zones; (3) Compare the identified results with the actual urban land uses and verify its accuracy. The experiment results proved that our method can effectively identify urban functional zones, and the results provide new ideas for the study of urban spatial patterns and have great significance in optimizing urban spatial planning.

An Interactive platform for low-cost 3D building modeling from VGI data using convolutional neural network

ABSTRACT The applications of 3D building models are limited as producing them requires massive labor and time costs as well as expensive devices. In this paper, we aim to propose a novel and web-based interactive platform, VGI3D, to overcome these challenges. The platform is designed to reconstruct 3D building models by using free images from internet users or volunteered geographic information (VGI) platform, even though not all these images are of high quality. Our interactive platform can effectively obtain each 3D building model from images in 30 seconds, with the help of user interaction module and convolutional neural network (CNN). The user interaction module provides the boundary of building facades for 3D building modeling. And this CNN can detect facade elements even though multiple architectural styles and complex scenes are within the images. Moreover, user interaction module is designed as simple as possible to make it easier to use for both of expert and non-expert users. Meanwhile, we conducted a usability testing and collected feedback from participants to better optimize platform and user experience. In general, the usage of VGI data reduces labor and device costs, and CNN simplifies the process of elements extraction in 3D building modeling. Hence, our proposed platform offers a promising solution to the 3D modeling community.

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database

ABSTRACT Updating an authoritative Land Use and Land Cover (LULC) database requires many resources. Volunteered geographic information (VGI) involves citizens in the collection of data about their spatial environment. There is a growing interest in using existing VGI to update authoritative databases. This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique, in order to update an authoritative land use database. Each VGI data source is considered to be an independent source of information, which is fused together using Dempster-Shafer Theory (DST). The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency. Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles. The data fusion approach achieved an overall accuracy of 85.6% for the 144 features having at least two contributions when the confidence threshold was set to 0.05. Despite the heterogeneity and limited amount of VGI used, the results are promising, with 99% of the LU polygons updated or enriched. These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally.

Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach

Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes.

A comprehensive quality assessment framework for linear features from Volunteered Geographic Information

ABSTRACT The majority of spatial data provided as Volunteered Geographic Information (VGI) are roads and other linear map features. Such data have been widely used in routing and navigation, road network update, emergency response, urban planning and more. Due to the lack of cartographic standards and issues with volunteer credibility, the quality of VGI linear features remains a concern and could seriously hinder the broad application of VGI data. This research proposes a comprehensive quality assessment framework for VGI linear features which adopts factor analysis to integrate two novel quality metrics with six other commonly used metrics, and further examines the spatial autocorrelation and semantic correlation of VGI linear feature quality. The OpenStreetMap road network of Allegheny County, Pennsylvania (USA) was selected as an example to test the proposed framework. Our results suggest that the proposed metrics, Box-counting dimension difference and Link accuracy are feasible for detecting quality issues and are important supplements to the common quality metrics. The findings also show that significant spatial autocorrelation exists in spatial completeness, positional accuracy, and logical consistency. Road type such as Tertiary, Residential, Service and Link has been proven to be a typical indicator of the different quality elements for VGI linear features.

Geometric and semantic quality assessments of building features in OpenStreetMap for some areas of Istanbul

Abstract Nowadays volunteered geographic information (VGI) and collaborative mapping projects such as OpenStreetMap (OSM) have gained popularity as they not only offer free data but also allow crowdsourced contributions. Spatial data entry in this manner creates quality concerns for further use of the VGI data. In this regard, this article focuses on the assessments of geometric and semantic quality of the OSM building features (BFs) against a large-scale topographic (TOPO) data belonging to some areas of Istanbul. The comparison is carried out based on the one-to-one matched BFs according to a geometric matching ratio. In geometric terms, various parameters of position (i.e. X, Y), size (i.e. area, perimeter and granularity), shape (i.e. convexity, circularity, elongation, equivalent rectangular index, rectangularity and roughness index), and orientation (i.e. orientation angle) elements are computed and compared. In semantic terms, BF type coherences are evaluated. According to the findings of geometric quality, the average positional difference was less than three meters. In addition, the perimeter values tended to decrease while area and granularity values tended to increase in OSM data against TOPO data. Those showed that the level of the detail of the OSM BFs was lower than TOPO BFs in general. This was also confirmed by the decreasing tendency of shape complexity according to the parameters of shape element. Orientation angle differences was often low except for some special cases. It was found that the scale of the OSM dataset, even though not homogenous, approximately corresponded to the lower limit of medium scale maps (i.e. 1:10,000) or a slightly smaller scale. According to the findings of semantic quality, in case of the presence of specific type definition, the coherence was rather high between OSM and TOPO BFs while the most OSM BFs did not have a specific type attribute. This study showed that the matching process needed some improvements while the followed approach was largely successful in the evaluation of the matched buildings from geometric and semantic aspects.

Quality Verification of Volunteered Geographic Information Using OSM Notes Data in a Global Context

Although the data obtained from volunteered geographic information (VGI) are inherently different from public surveys, the quantity of the data are vast and the quality of the data are often poor. To improve the quality of VGI data, the positional accuracy and diversity and interaction of the number of users involved in the regional generation of the data are important. This research proposes a new approach for the accumulation of OpenStreetMap (OSM) data by using OSM Notes and attempts to analyze the geographical distribution and the characteristics of the contents of the contributions, quantitatively and qualitatively. Subsequently, the results demonstrated regional differences in OSM Notes, but it provided users with an understanding of the new features of quality management in OSM, even in regions where OSM activities are not necessarily active. In addition, it was also possible to discover new factors such as the time transition required for the correction and contribution of anonymous users. These results are expected to serve as a tool for users to communicate with each other to resolve data bugs that exist in OSM and provide future researchers with examples of user interaction in global OSM activities.

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Over the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI data: Volunteered Street View Imagery (VSVI). Two VSVI sources, Mapillary and OpenStreetCam, were extracted and analyzed to study road coverage and contribution patterns for four US metropolitan areas. Results show that coverage patterns vary across sites, with most contributions occurring along local roads and in populated areas. We also found that a few users contributed most of the data. Moreover, the results suggest that most data are being collected during three distinct times of day (i.e., morning, lunch and late afternoon). The paper concludes with a discussion that while VSVI data is still relatively new, it has the potential to be a rich source of spatial and temporal information for monitoring cities.