Yerach Doytsher

Finding corresponding objects when integrating several geo-spatial datasets

When integrating geo-spatial datasets, a join algorithm is used for finding sets of corresponding objects (i.e., objects that represent the same real-world entity). Algorithms for joining two datasets were studied in the past. This paper investigates integration of three datasets and proposes methods that can be easily generalized to any number of datasets. Two approaches that use only locations of objects are presented and compared. In one approach, a join algorithm for two datasets is applied sequentially. In the second approach, all the integrated datasets are processed simultaneously. For the two approaches, join algorithms are given and their performances, in terms of recall and precision, are compared. The algorithms are designed to perform well even when locations are imprecise and each dataset represents only some of the real-world entities. Results of extensive experiments show that one of the algorithms has the best (or close to the best) performances under all circumstances. This algorithm has a much better performance than applying sequentially the one-sided nearest-neighbor join.

Location-based algorithms for finding sets of corresponding objects over several geo-spatial data sets

When integrating geo‐spatial data sets, a join algorithm is used for finding sets of corresponding objects (i.e., objects that represent the same real‐world entity). This article investigates location‐based join algorithms for integration of several data sets. First, algorithms for integration of two data sets are presented and their performances, in terms of recall and precision, are compared. Then, two approaches for integration of more than two data sets are described. In one approach, all the integrated data sets are processed simultaneously. In the second approach, a join algorithm for two data sets is applied sequentially, either in a serial manner, where in each join at least one of the joined data sets is a single source, or in a hierarchical manner, where two join results can be joined. For the two approaches, join algorithms are given. The algorithms are designed to perform well even when location of objects are imprecise and each data set represents only some of the real‐world entities. Results of extensive experiments with the different approaches are provided and analyzed. The experiments show the differences, in accuracy and efficiency, between the approaches, under different circumstances. The results also show that all our algorithms have much better accuracy than applying the commonly used one‐sided nearest‐neighbor join.

Toward a spatial 3D cadastre in Israel

Abstract This paper presents results of research dealing with geodetic and cadastral aspects of utilizing space above and below the surface. The research is being conducted at the Geodetic Engineering Division of the Technion—Israel Institute of Technology, as part of the doctoral studies of the first author. The principal objectives of the research are to find a cadastral–geodetic solution for utilizing above and below surface space and for defining the characteristics of the future analytical, three-dimensional and spatial cadastre that will replace the existing two-dimensional graphical surface cadastre in Israel. The research objectives are being realized by attaining the secondary research objectives: defining the future cadastral reality and developing a spatial cadastral model; defining guidelines for transition from the surface cadastre to the spatial cadastre; developing a model for registering property rights in all three spaces; developing models for managing spatial cadastre information and creating the geodetic–cadastral background for a legal solution of utilizing all land space.

Efficient integration of road maps

Integration of two road maps is finding a matching between pairs of objects that represent, in the maps, the same real-world road. Several algorithms were proposed in the past for road-map integration; however, these algorithms are not efficient and some of them even require human feedback. Thus, they are not suitable for many important applications (e.g., Web services) where efficiency, in terms of both time and space, is crucial. This paper presents two efficient algorithms for integrating maps in which roads are represented as polylines. The main novelty of these algorithms is in using only the locations of the endpoints of the polylines rather than trying to match whole lines. Experiments on real-world data are given, showing that our approach of integration based on matching merely endpoints is efficient and accurate (that is, it provides high recall and precision).

Ad hoc matching of vectorial road networks

In integration of road maps modeled as road vector data, the main task is matching pairs of objects that represent, in different maps, the same segment of a real-world road. In an ad hoc integration, the matching is done for a specific need and, thus, is performed in real time, where only a limited preprocessing is possible. Usually, ad hoc integration is performed as part of some interaction with a user and, hence, the matching algorithm is required to complete its task in time that is short enough for human users to provide feedback to the application, that is, in no more than a few seconds. Such interaction is typical of services on the World Wide Web and to applications in car-navigation systems or in handheld devices. Several algorithms were proposed in the past for matching road vector data; however, these algorithms are not efficient enough for ad hoc integration. This article presents algorithms for ad hoc integration of maps in which roads are represented as polylines. The main novelty of these algorithms is in using only the locations of the endpoints of the polylines rather than trying to match whole lines. The efficiency of the algorithms is shown both analytically and experimentally. In particular, these algorithms do not require the existence of a spatial index, and they are more efficient than an alternative approach based on using a grid index. Extensive experiments using various maps of three different cities show that our approach to matching road networks is efficient and accurate (i.e., it provides high recall and precision). General Terms:Algorithms, Experimentation

Storing routes in socio-spatial networks and supporting social-based route recommendation

Cellular phones and GPS-based navigation systems allow recording the location history of users, to find places the users frequently visit and routes along which the users frequently travel. This provides associations between users and geographic entities. Considering these associations as edges that connect users of a social network to geographical entities on a spatial network yields an integrated socio-spatial network. Queries over a socio-spatial network glean information on users, in correspondence with their location history, and retrieve geographical entities in association with the users who frequently visit these entities. In this paper we present a graph model for socio-spatial networks that store information on frequently traveled routes. We present a query language that consists of graph traversal operations, aiming at facilitating the formulation of queries, and we show how queries over the network can be evaluated efficiently. We also show how social-based route recommendation can be implemented using our query language. We describe an implementation of the suggested model over a graph-based database system and provide an experimental evaluation, to illustrate the effectiveness of our model.

Establishing an urban digital cadastre: analytical reconstruction of parcel boundaries

A new method for generating a spatially accurate, legally supportive and operationally efficient cadastral database of the urban cadastral reality is described. The definition and compilation of an accurate cadastral database (achieving a standard deviation smaller than 0.1 m) is based on an analytical reconstruction of cadastral boundaries rather than on the conventional field reconstruction process. The new method is based on GPS control points and traverse networks for providing the framework; the old field books for defining the links between the various original ground features; and a geometrical and cadastral adjustment process as the conceptual basis. A pilot project that was carried out in order to examine and evaluate the new method is described.

Orthogonal polynomials supported by region growing segmentation for the extraction of terrain from lidar data

Light Detection and Ranging (lidar) systems supply a massive amount points in 3D space with no semantic information helping knowing the objects they represent. To identify points that were reflected from the terrain, numerous algorithms have been developed in recent years. Many of them apply local operators that tend to face difficulties with complex scenes while their performance also varies between landscapes. In this paper, we present a filtering method that integrates a global approach using orthogonal polynomials with a local one that is region-based. The algorithm makes use of only a few parameters, and no fine-tuning is required between landscapes. Applying the algorithm over areas with varying topography and objects such as bridges, tunnels, and complex building, shows an improved performance compared to results obtained by others. This improvement is reflected in a lower than usual rate of misclassification errors for data acquired over different landscapes.

Voxel based volumetric visibility analysis of urban environments

Abstract The objective of this work is to develop integrated volumetric visibility analysis and modelling for environmental and urban systems. This work involves interdisciplinary research efforts that focus primarily on architecture design discipline and Geoinformatics. The work integrates an advanced Spatial Openness Index (SOI) model within a realistic geo-visualised Geographical Information System (GIS) environment. It is based on the assumption that the measured volume of visible space can indicate the perceived density. Most previous work aimed at computing visibility in open terrain and was based on the common Line of Sight (LOS) approach. Open terrain is usually defined as a 2·5D Digital Elevation Model (DEM) and the visibility analysis is carried out by computing profiles from a view point to all the DEM points. Applying the DEM/LOS method in a 3D urban environment causes severe difficulties: a 2·5D DEM structure is unable to accurately model 3D objects (and especially complex buildings), exact visibility computation is a long process and requires a very detailed scanning of the 3D objects. Accordingly, and in order to bypass these difficulties, a new approach has been developed – an approach which is based on subdividing the urban environment volume into voxels (volume elements, representing a value on a regular grid in 3D space). Implementing a spatial intersection between the buildings and the 3D grid of voxels on the one hand, and applying a sophisticated computation sequence of one-time handling a voxel on the other, enables the efficient computation of the visibility in a fast, flexible and accurate process. Moreover, in contrast to the common approach of a binary visibility decision – a point can be visible versus invisible; the suggested approach enables to compute visibility as a continuous figure with in-between values from fully visible up to fully invisible. This visibility model measures the volume of visible space at any required view point. This model enables accurate 3D simulation of the built environment regarding built structure and surrounding vegetation. A 3D model of our case-study, the Neve-Shaanan neighborhood in Haifa, was developed. The paper introduces the model, explains its main attributes and demonstrates the procedure of evaluating/measuring a realistic built environment. The model is planned to be assessed using subjective residents’ evaluation. The results of this research have shown its potential contribution to professional users, such as researchers, designers and city planners, at the same time as being easily used by non-professionals such as city dwellers, contractors and developers.

Heuristic algorithms for route-search queries over geographical data

In a geographical route search, given search terms, the goal is to find an effective route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geographical entities that are relevant to the given terms. A route is effective if it does not exceed a given distance limit whereas the ranking scores of the visited entities, with respect to the search terms, are maximal. This paper introduces route-search queries, suggests three semantics for such queries and deals with the problem of efficiently answering queries under the different semantics. Since the problem of answering route-search queries is a generalization of the traveling salesman problem, it is unlikely to have an efficient solution, i.e., there is no polynomial-time algorithm that solves the problem (unless P=NP). Hence, in this work we consider heuristics for the problem. Methods for effectively computing routes are presented. The methods are compared analytically and experimentally. For these methods, experiments on both synthetic and real-world data illustrate their efficiency and their effectiveness in computing a route that satisfies the constraints of a route-search query.