Aerial images are a special class of remote sensing images, as they are intentionally collected with a high degree of overlap. This high degree of overlap complicates existing index strategies such as R-tree and Space Filling Curve (SFC) based index techniques due to complications in space splitting, granularity of the grid cells and excessive duplication of image object identifiers (IOIs). However, SFC based space ordering can be modified to provide scalable management of overlapping aerial images. This involves overcoming similar IOIs in adjacent grid cells, which would naturally occur in SFC based grids with such data. IOI duplication can be minimized by merging adjacent grid cells through the proposed “Designing Adjacent Cell Merge Algorithm” (DACMA). This work focuses on establishing a proper adjacent cell merge metric and merge percentage value. Using a highly scalable, distributed HBase cluster for both a single aerial mapping project, and multiple aerial mapping projects, experiments evaluated Jaccard Similarity (JS) and Percentage of Overlap (PO) merge metrics. JS had significant advantages: (i) generating smaller merged regions and (ii) obtaining over 21% and 36% improvement in reducing query response times compared to PO. As a result, JS is proposed for the merge metric for DACMA. For the merge percentage two considerations were dominant: (i) substantial storage reductions with respect to both straight forward SFC-based cell space indexing and 4SA based indexing, and (ii) minimal impact on the query response time. The proposed merge percentage value was selected to optimize the storage (i.e. space) needs and response time (i.e. time) herein named the "Space-Time Trade-off Optimization Percentage" value (or STOP value) is presented.

State-of-the-art, scalable, indexing techniques in location-based image data retrieval are primarily focused on supporting window and range queries. However, support of these indexes is not well explored when there are multiple spatially similar images to retrieve for a given geographic location. Adoption of existing spatial indexes such as the kD-tree pose major scalability impediments. In response, this work proposes a novel scalable, key-value, database oriented, secondary-memory based, spatial index to retrieve the top$k$most spatially similar images to a given geographic location. The proposed index introduces a 4-dimensional Hilbert index (4DHI). This space filling curve is implemented atop HBase (a key-value database). Experiments performed on both synthetically generated and real world data demonstrate comparable accuracy with MD-HBase (a state of the art, scalable, multidimensional point data management system) and better performance. Specifically, 4DHI yielded 34% - 39% storage improvements compared to the disk consumption of the original index of MD-HBase. The compactness in 4DHI also yielded up to 3.4 and 4.7 fold gains when retrieving 6400 and 12800 neighbours, respectively; compared to the adoption of original index of MD-HBase for respective neighbour searches. An optimization technique termed “Bounding Box Displacement” (BBD) is introduced to improve the accuracy of the top$k$approximations in relation to the results of in-memory kD-tree. Finally, a method of reducing row key length is also discussed for the proposed 4DHI to further improve the storage efficiency and scalability in managing large numbers of remotely sensed images.

Ground-based Whole Sky Imagers (WSIs) are increasingly being used for various remote sensing applications. While the fundamental requirements of a WSI are to make it climate-proof with an ability to capture high resolution images, cost also plays a significant role for wider scale adoption. This paper proposes an extremely low-cost alternative to the existing WSIs. In the designed model, high resolution images are captured with auto adjusting shutter speeds based on the surrounding light intensity. Furthermore, a manual data backup option using a portable memory drive is implemented for remote locations with no internet access.

Many solutions for coarse geolocating of users at the time they post a message exist. However, for many important applications, like traffic monitoring and event detection, finer geolocation at the level of city neighborhoods, i.e., at a sub-city level, is needed. Data-driven approaches often do not guarantee good accuracy and efficiency due to the higher number of sub-city level positions to be estimated and the low availability of balanced and large training sets. We claim that external information sources overcome limitations of data-driven approaches in achieving good accuracy for sub-city level geolocation and we present a knowledge-driven approach achieving good results once the reference area of a message is known. Our algorithm, called Sherloc, exploits toponyms in the message, extracts their semantic from a geographic gazetteer, and embeds them into a metric space that captures the semantic distance among them. We identify the semantically closest toponyms to a message and then cluster them with respect to their spatial locations. Sherloc requires no prior training, it can infer the location at sub-city level with high accuracy, and it is not limited to geolocating on a fixed spatial grid.

This paper introduces a novel LiDAR point cloud data encoding solution that is compact, flexible, and fully supports distributed data storage within the Hadoop distributed computing environment. The proposed data encoding solution is developed based on Sequence File and Google Protocol Buffers. Sequence File is a generic splittable binary file format built in the Hadoop framework for storage of arbitrary binary data. The key challenge in adopting the Sequence File format for LiDAR data is in the strategy for effectively encoding the LiDAR data as binary sequences in a way that the data can be represented compactly, while allowing necessary mutation. For that purpose, a data encoding solution, based on Google Protocol Buffers (a language-neutral, cross-platform, extensible data serialisation framework) was developed and evaluated. Since neither of the underlying technologies is sufficient to completely and efficiently represent all necessary point formats for distributed computing, an innovative fusion of them was required to provide a viable data storage solution. This paper presents the details of such a data encoding implementation and rigorously evaluates the efficiency of the proposed data encoding solution. Benchmarking was done against a straightforward, naive text encoding implementation using a high-density aerial LiDAR scan of a portion of Dublin, Ireland. The results demonstrated a 6-times reduction in data volume, a 4-times reduction in database ingestion time, and up to a 5 times reduction in querying time.

Recent technological developments in the fields of positioning and mobile communications gave rise to the availabilityof massive spatio-temporal open datasets about cities. A proper exploitation of these big datasets by decision makers of smart cities could be very useful to analyse and understand mobility patterns, with the final goal of easing many transportation problems, like parking search and traffic. While many research efforts have been aimed at defining powerful visual analytics tools for exploring vehicular trajectory data, to date almost no specifically tailored tools are available to analyse (on-street) parking data and dynamics. To fill this gap, in this paper we present the current state of an on-going research on the development of a visual analytics tool, meant to support decision makers of smart cities in performing multigranular spatio-temporal explorations of mobility open data, like those about parking. Moreover, the proposed GUI offers the possibility to overlay external spatio-temporal datasets as well as to customize the way this data is rendered, to get a better insight on the parking dynamics and its influencing factors.

The Information Retrieval research has used semantics to provide accurate search results, but the analysis of conceptual abstraction has mainly focused on information integration. We consider session-based query expansion in Geographical Information Retrieval, and investigate the impact of semantic granularity (i.e., specificity of concepts representation) on the suggestion of relevant types of information to search for. We study how different levels of detail in knowledge representation influence the capability of guiding the user in the exploration of a complex information space. A comparative analysis of the performance of a query expansion model, using three spatial ontologies defined at different semantic granularity levels, reveals that a fine-grained representation enhances recall. However, precision depends on how closely the ontologies match the way people conceptualize and verbally describe the geographic space.

This paper proposes a method for automatically inferring semantic type information for a street network from its corresponding geometrical representation. Specifically, a street network is modelled as a probabilistic graphical model and semantic type information is inferred by performing learning and inference with respect to this model. Learning is performed using a maximum-margin approach while inference is performed using a fusion moves approach. The proposed model captures features relating to individual streets, such as linearity, as well as features relating to the relationships between streets such as the co-occurrence of semantic types. On a large street network containing 32,412 street segments, the proposed model achieves precision and recall values of 68% and 65% respectively. One application of this work is the automation of street network mapping.

The task of Simultaneous Localization and Mapping (SLAM) is regularly performed in network spaces consisting of a set of corridors connecting locations in the space. Empirical research has demonstrated that such spaces generally exhibit common structural properties relating to aspects such as corridor length. Consequently there exists potential to improve performance through the placement of priors over these properties. In this work we propose an appearance-based SLAM method which explicitly models the space as a network and in turn uses this model as a platform to place priors over its structure. Relative to existing works, which implicitly assume a network space and place priors over its structure, this approach allows a more formal placement of priors. In order to achieve robustness, the proposed method is implemented within a multi-hypothesis tracking framework. Results achieved on two publicly available datasets demonstrate the proposed method outperforms a current state-of-the-art appearance-based SLAM method.

OpenStreetMap (OSM) has been demonstrated to be a valuable source of spatial data in the context of many applications. However concerns still exist regarding the quality of such data and this has limited the proliferation of its use. Consequently much research has been invested in the development of methods for assessing and/or improving the quality of OSM data. However most of these methods require ground-truth data, which, in many cases, may not be available. In this paper we present a novel solution for OSM data quality assessment that does not require ground-truth data. We consider the semantic accuracy of OSM street network data, and in particular, the associated semantic class (road class) information. A machine learning model is proposed that learns the geometrical and topological characteristics of different semantic classes of streets. This model is subsequently used to accurately determine if a street has been assigned a correct/incorrect semantic class.

Volunteered geographic information (VGI) is generated by heterogenous ‘information communities’ that co-operate to produce reusable units of geographic knowledge. A consensual lexicon is a key factor to enable this open production model. Lexical definitions help demarcate the boundaries of terms, forming a thin semantic ground on which knowledge can travel. In VGI, lexical definitions often appear to be inconsistent, circular, noisy and highly idiosyncratic. Computing the semantic similarity of these ‘volunteered lexical definitions’ has a wide range of applications in GIScience, including information retrieval, data mining and information integration. This article describes a knowledge-based approach to quantify the semantic similarity of lexical definitions. Grounded in the recursive intuition that similar terms are described using similar terms, the approach relies on paraphrase-detection techniques and the lexical database WordNet. The cognitive plausibility of the approach is evaluated in the context of the OpenStreetMap (OSM) Semantic Network, obtaining high correlation with human judgements. Guidelines are provided for the practical usage of the approach.

Significant interaction challenges arise in both developing and using interactive map applications. Users encounter problems of information overload in using interactive maps to complete tasks. This is further exacerbated by device limitations and interaction constraints in increasingly popular mobile platforms. Application developers must then address restrictions related to screen size and limited bandwidth in order to effectively display maps on mobile devices. In order to address issues of user information overload and application efficiency in interactive map applications, we have developed a novel approach for delivering personalized vector maps. Ongoing task interactions between users and maps are monitored and captured implicitly in order to infer individual and group preferences related to specific map feature content. Personalized interactive maps that contain spatial feature content tailored specifically to users' individual preferences are then generated. Our approach addresses spatial information overload by providing only the map information necessary and sufficient to suit user interaction preferences, thus simplifying the completion of tasks performed with interactive maps. In turn, tailoring map content to specific user preferences considerably reduces the size of vector data sets necessary to transmit and render maps on mobile devices. We have developed a geographic information system prototype, MAPPER (MAP PERsonalization), that implements our approach. Experimental evaluations show that the use of personalized maps helps users complete their tasks more efficiently and can reduce information overload.

The context of mobility raises many issues for GIS applications. Mobile device limitations, including pen input whilst in motion, result in interfaces which are difficult to navigate and interact with. However, comparatively little research has been conducted to address the interface mobility problem for GIS. We are particularly concerned with the limited interaction techniques available to users of mobile GIS which play a primary role in contributing to the complexity of using such an application whilst mobile. Our research focuses on multimodal interfaces as a means to present users with a wider choice of modalities for interacting with GIS applications. The focus of this paper concerns a comprehensive user study which demonstrates the benefits, in terms of usability and efficiency, of a multimodal interface for the CoMPASS mobile GIS which we have developed.

Multiple granularities provide an essential support for extracting significant knowledge from spatio-temporal datasets at different levels of details. They enable to zoom-in and zoom-out spatio-temporal datasets, thus enhancing the data modelling exibility and improving the analysis of information. In this paper we investigate the implementation issues arising when a data model and a query language are enriched with spatio-temporal multigranularity. We introduce appropriate representations for space and time dimensions, granularities, granules, and multi-granular values. Finally, we discuss how multigranular spatio-temporal conversions affect data usability and how such important property may be guaranteed.

Information overload is a pervasive problem in many application domains. One way of addressing this problem is to create user profiles that filter out irrelevant information while presenting the users with information matching their interests. This approach has not been widely exploited in GIS. In our spatial application, we log user interactions, and implicitly infer their interests from this information to generate a user interest model. In particular, mouse movements and map browsing behaviour are analysed. Experiments presented in this paper examine the accuracy of implicitly determined spatial interests. Personalisation techniques can subsequently be applied to provide users with the most relevant information with regard to their interests.

High‐resolution spatio‐temporal datasets are being collected every day to record the behaviour of several natural phenomena. However, data‐mining techniques are needed to extract relevant patterns from very large repositories and reveal spatial and temporal patterns in the behaviour of these phenomena. To this aim, we propose a system for mining data with spatial and temporal characteristics, and for visualizing and interpreting the results. Within this system, we have developed two complementary 3D visualization environments, one based on Google Earth and one relying on a Java3D graphical user interface. In this paper, we illustrate the main features of the system we have developed, and report on the main results we have obtained by analysing the Hurricane Isabel dataset.

Several application domains require handling spatio‐temporal data. However, traditional Geographic Information Systems (GIS) and database models do not adequately support temporal aspects of spatial data. A crucial issue relates to the choice of the appropriate granularity. Unfortunately, while a formalisation of the concept of temporal granularity has been proposed and widely adopted, no consensus exists on the notion of spatial granularity. In this paper, we address these open problems, by proposing a formal definition of spatial granularity and by designing a spatio‐temporal framework for the management of spatial and temporal information at different granularities. We present a spatio‐temporal extension of the ODMG type system with specific types for defining multigranular spatio‐temporal properties. Granularity conversion functions are introduced to obtain attributes values at different spatial and temporal granularities.

A large percentage of data managed by a variety of different application domains has spatiotemporal characteristics. Unfortunately, traditional geographical information systems do not allow for an easy representation of temporal aspects of spatial data. Moreover, they do not usually support the representation of data at multiple levels of granularity. In this paper we present a multigranular spatiotemporal data model. Our model extends the ODMG model with multiple spatial and temporal granularities. In particular, the model allows for an uniform management of two kinds of spatiotemporal objects: moving entities (e.g. cars, planes, etc.) and temporal maps (i.e., maps representing the change over time of a given geographic area). It also provides a framework for mapping the movement of an entity such as a car onto an underlying geographic area. The model we propose relies on a standard definition of temporal granularity. On the other hand, the representation of spatial entities at multiple granularities is obtained by applying model oriented map generalization principles. In particular, we consider a set of generalization operators that guarantee topological consistency.

Geo-spatial image databases are employed in a wide range of applications, such as intelligence operations, recreational and professional mapping, urban and industrial planning, and tourism systems. Effective retrieval of relevant images from such digital libraries can employ knowledge about what an image contains, why image contents are important in a particular domain, and how specific images have been used for particular domain tasks. Approaches to annotation for multimedia information retrieval have typically focused on the first two types of knowledge; however, managing the knowledge implicit in using geo-spatial imagery to address particular tasks can be crucial for capturing and making the most effective use of organisational knowledge assets. We are developing case-based knowledge-management support for large geo-spatial image repositories that scaffolds task-based knowledge capture about a content-based sketch query mechanism. This paper describes our task-centric approach to image annotation and retrieval, and it presents our initial implementation of the approach.

In this paper we extend our previous work on shape-based queries to support queries on configurations of image objects. Here we consider spatial reasoning, especially directional and metric object relationships. Existing models for spatial reasoning tend to rely on pre-identified cardinal directions and minimal scale variations, assumptions that cannot be considered as given in our image applications, where orientations and scale may vary substantially, and are often unknown. Accordingly, we have developed the method of varying baselines to identify similarities in direction and distance relations. Our method allows us to evaluate directional similarities without a priori knowledge of cardinal directions, and to compare distance relations even when query scene and database content differ in scale by unknown amounts. We use our method to evaluate similarity between a user-defined query scene and object configurations. Here we present this new method, and discuss its role within a broader image retrieval framework.