Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

THE OPALS DATA MANAGER – EFFICIENT DATA MANAGEMENT FOR PROCESSING LARGE AIRBORNE LASER SCANNING PROJECTS

The fast measurement rate of today's Airborne Laser Scanners results in billions of points for single ALS projects. Efficient algorithms and data management methods are, therefore, a precondition for successful project handling. The software package OPALS (Orientation and Processing of Airborne Laser Scanning data) was especially designed to meet those criteria. Central core of the package is the OPALS Data Manager (ODM). It provides both, fast spatial access to huge point clouds, as well as a flexible attribute schema to store additional point related quantities. Concepts of the spatial data organization and implementation details about the attribute handling are presented. Additionally, design rationales of the ODM, its file format and the system performance are described

Databases, Interpolation Algorithm

Laser scanning had a huge impact on topographic information systems (TIS) and geographic information systems (GIS). Compared to other data acquisition methods, airborne and terrestrial laser scanner systems have a high degree of automation and allow capturing surfaces in various scales. It turns out that laser scanners are both, efficient and economical measuring tools for cultural heritage objects, detailed city models but also country-wide digital terrain models (DTM), to a previously unknown degree of detail. The increase of available data sets leads to higher demands on interpolation algorithms and efficient spatial data structures of TISs. Whereas country or planet-wide surface models are typically described in 2.5D (the terrain heights are described by a bivariate function over the ground plan domain), models of bigger scaled objects (e.g. city models) are nowadays (or least in the near future) modelled by full 3D approaches. Consequently TISs of the future will have to combine both, 2.5D efficiency and 3D capability. Such a strategy including an efficient data handling, as currently developed for SCOP-NG (SCOP Next Generation), will be presented within this article. 1

Promotion of regional bioenergy initiatives in Poland, Romania and Slovakia Final Report

This publication is printed in English, Polish, Romanian and Slovak languages. English language hardcopies you can order from the Natural Resources Institute Finland (Luke). The contact person is Mr. Pasi Poikonen and his e-mail address is pasi.poikonen at luke.fi. Slovak language hardcopies you can order from the Slovak University of Agriculture. The contact person is Mr. Pavol Otepka (pavol.otepka at uniag.sk). Polish language version hardcopies are available in the Polish partner premises in Warsaw and the contact person is Ms. Karolina Loth-Babut (kloth at kape.gov.pl). Romanian language version hardcopies are available in the Romanian partners´ premises and the contact persons are Ms. Adriana Milandru (ISPE) (adriana.milandru at ispe.ro) and Mr. Marius Duca (ADR Centru) (marius.duca at adrcentru.ro).PromoBio project (Promotion of Regional Bioenergy Initiatives – IEE/10/470/SI2.593725) supported by the European Commission under the Intelligent Energy Europe Programme.The Final Report contains 62 pages, 36 figures and 8 tables. It consists of the following chapters: 1. PromoBio Project framework 2. State-of-the-art bioenergy sectors in the target countries 3. Bioenergy initiatives in target countries 4. Conclusions and recommendations for transfer of experience