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Road accidents are a worldwide problem, affecting millions of people annually. One way to reduce such accidents is to predict risk areas and alert drivers. Advanced research has been carried out on identifying accident-influencing factors and potential highway risk areas to mitigate the number of road accidents. Machine learning techniques have been used to build prediction models using a supervised classification based on a labeled dataset. In this work, we experimented with many machine learning algorithms to discover the best classifier for the Brazilian federal road hotspots associated with severe or nonsevere accident risk using several features. We tested with SVM, random forest, and a multi-layer perceptron neural network. The dataset contains a ten-year road accident report by the Brazilian Federal Highway Police. The feature set includes spatial footprint, weekday and time when the accident happened, road type, route, orientation, weather conditions, and accident type. The results were promising, and the neural network model provided the best results, achieving an accuracy of 83%, a precision of 84%, a recall of 83%, and an F1-score of 82%. [ABSTRACT FROM AUTHOR]

The use of location-based sensors has increased exponentially. Tracking moving objects has become increasingly common, consolidating a new field of research that focuses on trajectory data management. Such trajectories may be semantically enriched using sensors and social media. This enables a detailed analysis of trajectory behavior patterns. One of the problems in this field is the search for a semantic trajectory database that is flexible and adaptable; flexibility in the sense of retrieving trajectories that are closest to the user's query and not just based on exact matching. Adaptability refers to adjusting to different types of semantic trajectories. This article proposes a new approach for representing and querying semantic trajectories based on text-processing techniques. Furthermore, we describe a framework, called SETHE (SEmantic Trajectory HuntEr), that performs similarity queries on semantically enriched trajectory databases. SETHE can be adapted according to the aspect types posed in user queries. We also presented an evaluation of the proposed framework using a real dataset, and compare our results with those of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Thanks to the built-in GPS device embedded in almost all smartphones, the facility of tracking users’ positions fostered new research opportunities. Among these opportunities, of particular interest in this work is the field of route and destination prediction. Suggesting a user to take a deviation to avoid a congested route is among the potential benefits of our research. Many of the approaches available in the literature consolidate the Markov model as suitable to prediction. Moreover, the Prediction by Partial Matching (PPM) compression technique has presented encouraging results for predicting route and destination. Thus, this paper proposes a novel predictor that combines Markov model with PPM technique, extracting the better of these two approaches. Our user-personalized predictor is able to predict the route and destination automatically in a real-time manner, including places never visited by the user. We evaluated our model with real world data collected from 21 users, obtaining a precision rate between 63% and 82%. [ABSTRACT FROM AUTHOR]

Due to the large number of photos that are currently being generated, it is very important to have techniques to organize, search for, and retrieve such images. Photo annotation plays a key role in these mechanisms because it can link raw data (photos) to specific information that is essential for human beings to handle large amounts of content. However, the generation of photo annotation is still a difficult problem to solve as part of a well-known challenge called the semantic gap. In this paper, a literature review was conducted with the aim of investigating the most popular methods employed to produce photo annotations. Based on the papers surveyed, we identified that People ('Who?'), Location ('Where?'), and Event ('Where? When?') are the most important features of photo annotation. We also established comparisons between similar photo annotation methods, highlighting key aspects of the most commonly used approaches. Moreover, we provide an overview of a general photo annotation process and present the main aspects of photo annotation representation comprising formats, context of usage, advantages and disadvantages. Finally, we discuss ways to improve photo annotation methods and present some future research guidelines. [ABSTRACT FROM AUTHOR]

The advent of volunteered geographical information (VGI) has contributed to the growth of the amount of user-contributed spatial data around the world. Spatial data acquired from crowdsourcing environments may contain valuable information which can be useful in other research fields, such as Digital gazetteers, commonly used in Geographic Information Retrieval. Digital gazetteers have a powerful role in the geoparsing process. They need to be kept up-to-date and as comprehensive as possible to enable geoparsers to perform lookup and then resolve toponym recognition precisely over digital documents. The detection of toponyms in digital texts such as social media posts is a bottom line for discovering useful spatially related information such as complaints regarding urban areas. In this context, this article proposes a method for gazetteer enrichment leveraging VGI data sources. Indeed VGI environments are not originally developed to work as gazetteers, however, they often contain more detailed and up-to-date information than gazetteers. Our method is applied within a geoparser environment by adapting its heuristics set besides enriching the corresponding gazetteer. A case study was performed by geoparsing Twitter posts focusing solely on the messages aiming at evaluating the performance of the enriched system. The obtained results were encouraging and have provided a good basis for discussion. [ABSTRACT FROM AUTHOR]

In recent years, spatial data infrastructures (SDIs) have gained great popularity as a solution to facilitate interoperable access to geospatial data offered by different agencies. In order to enhance the data retrieval process, current infrastructures usually offer a catalog service. Nevertheless, such catalog services still have important limitations that make it difficult for users to find the geospatial data that they are interested in. Some current catalog drawbacks include the use of a single record to describe all the feature types offered by a service, the lack of formal means to describe the semantics of the underlying data, and the lack of an effective ranking metric to organize the results retrieved from a query. Aiming to overcome these limitations, this article proposes SESDI (Semantically-Enabled Spatial Data Infrastructures), which is framework that reuses techniques of classic information retrieval to improve geographic data retrieval in a SDI. Moreover, the framework proposes several ranking metrics to solve spatial, semantic, temporal and multidimensional queries. [ABSTRACT FROM AUTHOR]

The recent popularity of digital cameras has posed a new problem: how to efficiently store and retrieve the very large number of digital photos captured and chaotically stored in multiple locations without any annotation. This paper proposes an infrastructure, called PhotoGeo, which aims at helping users with the people photo annotation, event photo annotation, storage and retrieval of personal digital photos. To achieve the desired objective, PhotoGeo uses new algorithms that make it possible to annotate photos with the key metadata to facilitate their retrieval, such as: the people who were shown in the photo ( who); where it was captured ( where); the date and time of capture ( when); and the event that was captured. The paper concludes with a detailed evaluation of these algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, Spatial Data Infrastructures (SDIs) play an important role in government agencies, at different levels: global, national, and local. They aim to improve the management and sharing of geospatial data. Nonetheless, these SDIs have been developed as information islands, in which a user's query is compared to metadata described only in their own catalog services. The lack of interaction among SDIs limits the potential of these infrastructures in providing geospatial data to a larger audience. This article presents a distributed architecture, based on a federation of SDIs which interact among themselves, using query propagation. This propagation facilitates data discovery and sharing. We also describe a distributed query processing service used to enable the resource discovery in distributed infrastructures. [ABSTRACT FROM AUTHOR]

Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data. [ABSTRACT FROM AUTHOR]