Archiwum Fotogrametrii, Kartografii i Teledetekcji 

About: Archiwum Fotogrametrii, Kartografii i Teledetekcji is an academic journal. The journal publishes majorly in the area(s): Point cloud & Mobile mapping. It has an ISSN identifier of 2083-2214. Over the lifetime, 626 publications have been published receiving 1808 citations.

Hexagonal discrete global grid systems for geospatial computing

Abstract: Advanced geospatial applications often involve complex computing operations performed under sometimes severe resource constraints. These applications primarily rely on traditional raster and vector data structures based on square lattices. But there is a significant body of research that indicates that data structures based on hexagonal lattices may be a superior alternative for efficient representation and processing of raster and vector data in high performance applications. The advantages of hexagonal rasters for image processing are discussed, and hexagonal discrete global grid systems for location coding are introduced. The combination provides an efficient, unified approach to location representation and processing in geospatial systems. 1. MOTIVATION Advanced geospatial applications, such as mobile mapping, often perform complex spatial operations on potentially large data sets, with strict controls on the accuracy of internal location representations, and in computing environments that may be severely constrained by resource and size limitations. These systems therefore often place a premium on representational and algorithmic efficiency, and are in a constant state of improvement as more efficient representations and algorithms become available. Among the most fundamental data structures are those used for the representation and storage of raster image data and vector geospatial location data. Because they are so pervasive, even small improvements in efficiency or representational accuracy in these data structures can result in substantial performance increases in an overall system. Data structures for the representation and storage of raster and vector data in geospatial applications have traditionally been built on substrates of square lattices. The common standards have long been raster grids of square pixels and vector coordinates consisting of 2- or 3-tuples of floating point values.

The use of geographic information systems in climatology and meteorology, COST719

Abstract: No permission to reproduce or utilise the contents of this book by any means is necessary, other than in the case of images, diagrams or other material from other copyright holders. In such cases permission of the copyright holders is required ; European Co-operation in the field of Scientific and Technical Research.

Stochastic modelling and analysis of IMU sensor errors

Abstract: The performance of a GPS/INS integration system is greatly determined by the ability of stand-alone INS system to determine position and attitude within GPS outage. The positional and attitude precision degrades rapidly during GPS outage due to INS sensor errors. With advantages of low price and volume, the Micro Electrical Mechanical Sensors (MEMS) have been wildly used in GPS/INS integration. Moreover, standalone MEMS can keep a reasonable positional precision only a few seconds due to systematic and random sensor errors. General stochastic error sources existing in inertial sensors can be modelled as (IEEE STD 647, 2006) Quantization Noise, Random Walk, Bias Instability, Rate Random Walk and Rate Ramp. Here we apply different methods to analyze the stochastic sensor errors, i.e. autoregressive modelling, Gauss-Markov process, Power Spectral Density and Allan Variance. Then the tests on a MEMS based inertial measurement unit were carried out with these methods. The results show that different methods give similar estimates of stochastic error model parameters. These values can be used further in the Kalman filter for better navigation accuracy and in the Doppler frequency estimate for faster acquisition after GPS signal outage.

Model-based automatic 3d building model generation by integrating lidar and aerial images

Abstract: Accurate, detailed, and up-to-date 3D building models are important for several applications such as telecommunication network planning, urban planning, and military simulation. Existing building reconstruction approaches can be classified according to the data sources they use (i.e., single versus multi-sensor approaches), the processing strategy (i.e., data-driven, model-driven, or hybrid), or the amount of user interaction (i.e., manual, semiautomatic, or fully automated). While it is obvious that 3D building models are important components for many applications, they still lack the economical and automatic techniques for their generation while taking advantage of the available multi-sensory data and combining processing strategies. In this research, an automatic methodology for building modelling by integrating multiple images and LiDAR data is proposed. The objective of this research work is to establish a framework for automatic building generation by integrating data- driven and model-driven approaches while combining the advantages of image and LiDAR datasets.