David B. Kidner

Database design for a multi-scale spatial information system

Abstract Growth in the available quantities of digital geographical data has led to major problems in maintaining and integrating data from multiple sources, required by users at differing levels of generalization. Existing GIS and associated database management systems provide few facilities specifically intended for handling spatial data at multiple scales and require time consuming manual intervention to control update and retain consistency between representations. In this paper the GEODYSSEY conceptual design for a multi-scale, multiple representation spatial database is presented and the results of experimental implementation of several aspects of the design are described. Object-oriented, deductive and procedural programming techniques have been applied in several contexts: automated update software, using probabilistic reasoning; deductive query processing using explicit stored semantic and spatial relations combined with geometric data; multiresolution spatial data access methods combining poini,...

Higher-order interpolation of regular grid digital elevation models

The fundamental aim of a digital elevation model (DEM) is to represent a surface accurately, such that elevations can be estimated for any given location. It is, therefore, necessary to have efficient and precise algorithms for the computation of surface elevations between given points. The hypothesis presented here, is that higher-order interpolation techniques will always be more accurate than the likes of the popular bilinear algorithm. This hypothesis will be evaluated through an assessment of the accuracy with which DEMs can be interpolated to higher spatial resolutions. A variety of interpolation techniques are assessed, ranging from the one-term level plane to the 36-term biquintic polynomial. In general, techniques that take account of the local terrain neighbourhood are more consistent and accurate, reducing the rms. error by up to 20% of the bilinear interpolant.

Advances in the data compression of digital elevation models

Abstract The maintenance and dissemination of spatial databases requires efficient strategies for handling the large volumes of data that are now publicly available. In particular, satellite and aerial imagery, radar, LiDAR, and digital elevation models (DEMs) are being utilised by a sizeable user-base, for predominantly environmental applications. The efficient dissemination of such datasets has become a key issue in the development of web-based and distributed computing environments. However, the physical size of these datasets is a major bottleneck in their storage and transmission. The problem is often exaggerated when the data is supplied in less efficient, proprietary or national data formats. This paper presents a methodology for the lossless compression of DEMs, based on the statistical correlation of terrain data in local neighbourhoods. Most data and image compression algorithms fail to capitalise fully on the inherent redundancy in spatial data. At the same time, users often prefer a uniform solution to all their data compression requirements, but these solutions may be far from optimal. The approach presented here can be thought of as a simple pre-processing of the elevation data before the use of traditional data compression software frequently applied to spatial data sets, such as GZIP. Identification and removal of the spatial redundancy in terrain data, with the use of optimal predictors for DEMs and optimal statistical encoders such as Arithmetic Coding, gives even higher compression ratios. Both GZIP and our earlier approach of combining a simple linear prediction algorithm with Huffman Coding are shown to be far from optimal in identifying and removing the spatial redundancy in DEMs. The new approaches presented here typically halve the file sizes of our earlier approach, and give a 40–62% improvement on GZIP-compressed DEMs.

Accuracy and reliability of map-matched GPS coordinates: the dependence on terrain model resolution and interpolation algorithm

The global positioning system (GPS) has become the most extensively used positioning and navigation tool in the world. Applications of GPS abound in surveying, mapping, transportation, agriculture, military planning, GIS, and the geosciences. However, the positional and elevation accuracy of any given GPS location is prone to error, due to a number of factors. This has serious implications for some applications, such as real-time navigational systems. GPS accuracy can be significantly improved with additional data, possibly from multiple sources, and especially from multiple receivers. In the case of a single GPS receiver, its position and elevation can be considerably improved with the use of spatial data. For vehicle tracking, map matching can be employed to intelligently snap the GPS location to a road centreline, while height aiding can augment the GPS solution by utilising a digital terrain model (DTM), thereby reducing the number of satellites required to determine a position. This paper describes the use of map matching and height aiding, and examines the effect of different terrain resolutions (Ordnance Survey 1:50,000 and 1:10,000 scale DTMs) on plan position and elevation accuracy for vehicle tracking. Furthermore, the users choice of interpolation algorithm for estimating heights from the DTM is investigated. The results of the experiments described in this paper demonstrate that height aiding alone reduces the mean error in elevation from 22.5 to 17.5m for of a single GPS receiver, and the mean error in plan position from 6 to 5m. However, map matching and height aiding combined, reduces the elevation RMSE of a single GPS receiver from 22.5m to approximately 4m (1:50,000 scale DTM) and down to 0.8m (1:10,000 scale DTM), while the plan position RMSE is reduced from 5.9 to 3.2m (either DTM). It is also demonstrated that when the number of satellites visible to the receiver is reduced, or the satellite geometry is poor, map matching and height aiding considerably improves the plan and elevation accuracy. The use of a higher-order interpolant (e.g. a bicubic or biquintic polynomial) is shown to slightly improve performance, compared to a bilinear interpolant, for the lower-resolution DTM, but has little overall benefit for the higher resolution DTM.

Modelling and prediction of GPS availability with digital photogrammetry and LiDAR

This paper describes an automated method for predicting the number of satellites visible to a GPS receiver, at any point on the Earths surface at any time. Intervisibility analysis between a GPS receiver and each potentially visible GPS satellite is performed using a number of different surface models and satellite orbit calculations. The developed software can work with various ephemeris data, and will compute satellite visibility in real time. Real‐time satellite availability prediction is very useful for mobile applications such as in‐car navigation systems, personal navigations systems and LBS. The implementation of the method is described and the results are reported.

Parallel Processing for Terrain Analysis in GIS: Visibility as a Case Study

The application of parallel processing to computationally intensive GIS problems has been advocated and illustrated by many researchers over the last twenty years. Despite this, GIS users have been slow to capitalize on the potential which the technology offers. Whilst today’s processors are adequate for the majority of GIS uses, some applications are too processor-intensive to be deemed viable for serial machines. This is particularly true of many digital terrain modelling applications, which has been the primary focus of parallel processing in GIS to date.This paper considers the problem of parallelizing line-of-sight (LOS) calculations in determining the visibility indices of entities such as elevation vertices in a digital terrain model (DTM). This is a requirement of site selection for a particular development, especially if visibility, or more specifically, visual intrusion is likely to be a key factor in gaining planning approval. To demonstrate the simplicity and applicability of parallelizing such GIS problems, this paper presents some parallel approaches in an efficient data organization, framework using a Transputer network. Speed-up performance can be increased by a factor of twelve using a simple network of twenty Transputers. As vast quantities of spatial data become available, particularly DTMs at larger scales and denser resolution, the demands for parallel processing will inevitably increase. It is hoped that the continued experiences of today’s researchers at applying parallel processing to well-defined problems will benefit the GIS users of tomorrow.

GIS and Wind Farm Planning

There is an increasing realisation that unless ‘greenhouse gas’ emissions are reduced, global warming may result in dire consequences for our future generations. Sustainable development in the form of renewable energy offers a way forward for the production of natural, clean electricity. Such sources include energy from the sun, wind, oceans, waste, the Earth, and the fall of water. The European Commission (EC) and individual countries of the European Union (EU) have programmes which aim to stimulate the development of renewable energy technologies to the fullest practical extent, where they have prospects of being economically attractive and environmentally acceptable.

The use of LiDAR in digital surface modelling: issues and errors.

Modelling and extracting 3D geographical data presents numerous challenges that require continual research to attempt to evolve an efficient, reliable and accurate solution. LiDAR data capture and analysis has become a preferred acquisition choice for elevation data because the resulting quality and level of detail far exceeds traditional methods for large survey areas. As with any data collection system, LiDAR is prone to errors. Analysing these errors, ascertaining causes and producing error correction strategies is vital if accurate and confident results are to be obtained. Eight years of LiDAR datasets (from 1998 to 2005) have been closely analysed for a large coastal area of South Wales. This article provides a detailed and accurate summary of the identified LiDAR data issues and subsequent errors which affect the accuracy of end products such as Digital Surface Models (DSMs).

Parallel distributed viewshed analysis

1. ABSTRACT The paper describes a number of distributed approaches to implementing a parallel vklbility a]g~rithm for Viewshed analysis. The problem can be simplified by considering a range of domain partitioning strategies for optimizing tie proc=sor worldoads. The best approaches are shown to work 22 times faster across a network of 24 processors. Such strategies allow traditional GIS functionality to be extended into new problem areas or to higher resolution spatial data using existing computing resources. Ke~~vords Intervisilility and viewshed analysis, digital terrain modeliig, DEhL parallel computing, distributed computing

Multiscale Terrain and Topographic Modelling with the Implicit TIN

The Multiscale Implicit Triangulated Irregular Network (TIN) provides a storage and access scheme for generating triangulated terrain models that adapt their content and level of detail to the requirements of the user. The scheme combines storage of data representing the terrain surface, and two and three dimensional terrain features, with a retrieval and triangulation procedure that generates a constrained Delaunay triangulation at run time. The feature content and level of detail may be specified by the user, thereby providing a flexible facility that adapts to the requirements of a wide range of applications, whether global or local, exploratory or precise. This paper provides an overview of the scheme and illustrates its application for a variety of queries requiring multiscale representations.