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The digital elevation model (DEM) is an invaluable product in numerous geospatial applications from orthorectification of aerial photographs to hydrological modelling and advanced 3D visualisation. With the current aerial laser scanning methods, superior quality digital elevation models can be produced over land areas, but surfaces over water bodies are visually problematic, especially for streams in 3D. We present a method to generate smooth, monotonically decreasing elevation surfaces over water bodies in DEMs. The method requires the point cloud data and the polygons delineating the water bodies as input data. We show how DEM visualisations improve by applying the presented method.

A viable approach for tackling the challenges of integration and analysis of geospatial raster data is to pre-process datasets into a common framework and store them into a cloud repository, accessible through a set of well-defined access protocols. This paper describes an initiative called GeoCubes Finland, where the aim is to provide a number of country-wide raster geodatasets in a common schema. In addition to more traditional access methods, a custom Application Programming Interface (API) has been designed for supporting the various tasks related to retrieval, use, visualisation and analysis of the contained raster datasets.

The increasing quantity and use of high-resolution raster data has put its management in the forefront of development. In this paper, we describe an approach that can be used to assess the capabilities of Array Database Management Systems (DBMSs) regarding the management and processing of raster data. The paper presents a framework that can be used to compare the functionalities of Array DBMSs and benchmark them. The main feature of the framework is assessing functionality using both targeted test cases and benchmarking. This assessment is followed by leveraging the gained experiences to assess non-functionality using characteristics from existing quality models. The framework can be extended by further DBMSs, benchmarks and additional hardware resources. The assessment was first implemented for the community editions of SciDB and rasdaman. The study presents some key initial observations regarding the particular Array DBMSs.

Providers of geospatial data are facing the challenge of di-verse user needs when delivering their products to different user groups. Academic researchers represent a user group with quite specific requirements, like good support for anal-ysis and high-performance computing. A national infrastruc-ture providing both geospatial data and powerful geocompu-ting facilities for research use is being developed in Finland. The part of the infrastructure described in this paper focuses on the management, storage and efficient delivery of raster-formatted geospatial data by applying the concept of datacube.

An initiative has been started in Finland to build a harmo-nized raster geodata repository, called GeoCubes Finland, to help academic users in carrying out geospatial analysis operations in research projects of diverse disciplines. The aim of the initiative is to expand the adoption of GI methods and tools to new sectors of academic research. GeoCubes Finland is being built in the context of a large-scale program developing national geodata infrastructure for academic studies. The main building blocks of the GeoCubes Finland initiative are: a harmonized, multi-resolution raster geodata storage, a set of standardized mechanisms for accessing the raster contents of the storage, and support facilities for cloud-based geocomputing to help users carry out spatial analysis with the contained datasets.

This paper presents a methodology for using a generic data-driven spatial access method as a communication mechanism for vario-scale data in a server-client setting. As a complete data set is often quite large, it is managed at the server side and supporting different scale levels is important. We show that a generic R-tree like grouping method, commonly used for efficiently organizing and retrieving data from a database, can be used in a networked architecture and that it allows off-loading processing tasks from a server to a client. This helps in making web services more scalable. The method supports efficient retrieval of partial data by a client and makes it possible to reuse data by means of caching. This can make communication more efficient.

Visibility computed from a LiDAR point cloud offers several advantages compared to using a gridded digital height-model. With a higher resolution and detailed information, point cloud data can provide precise analysis as well as an opportunity to avoid the process of generating a surface representation or a solid model. Also a better inclusion of vegetation is expected. This paper describes motivations for using point cloud in visibility analysis and also makes a comparison of using two different data to perform visibility analysis.