<abstract xmlns="http://www.w3.org/1999/xhtml">

This study compares two interpolation methods in the problem of a local GNSS/levelling (quasi) geoid modelling. It uses raw data, no global geopotential model is involved. The methods differ as to the complexity of modelling procedure and theoretical background, they are ordinary kriging/least-squares collocation with constant trend and inverse distance weighting (IDW). The comparison itself was done through leave-one-out and random (Monte Carlo) cross-validation. Ordinary kriging and IDW performance was tested with a local (using limited number of data) and global (using all available data) neighbourhoods using various planar covariance function models in case of kriging and various exponents (power parameter) in case of IDW. For the study area both methods assure an overall accuracy level, measured by mean absolute error, root mean square error and median absolute error, of less than 1 cm. Although the method of IDW is much simpler, a suitably selected parameters (also trend removal) may contribute to differences between methods that are virtually negligible (fraction of a millimetre).
</abstract>

A crossvalidation-based comparison of kriging and IDW in local GNSS/levelling quasigeoid modelling

<abstract xmlns="http://www.w3.org/1999/xhtml">

Accurate and efficient detection of ships from aerial images is an intriguing and difficult task of extreme societal importance due to their implication and association with maritime infractions, and other suspicious actions. Having an automated system with the required capabilities indicates a substantial reduction in the related man-hours of characterization and the overall underlying processes. With the advent of various image processing techniques and advancements in the field of machine learning and deep learning, specialized methodologies can be created for the said task. An intuition for the enhancement of existing methodologies would be a study on attention-based cognition and the development of improved neural architectures with the available attention modules. This paper offers a novel study and empirical analysis of the utility of various attention modules with U-Net and other subsidiary architectures as a backbone for the task of computationally efficient and accurate ship detection. The best performing models are depicted and explained thoroughly, while considering their temporal performance.
</abstract>

Department of Computer Science and Engineering

Institute of Technology, Nirma Univeristy

Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

<abstract xmlns="http://www.w3.org/1999/xhtml">

Photogrammetric products obtained by processing data acquired with Unmanned Aerial Vehicles (UAVs) are used in many fields. Various structures are analysed, including roads. Many roads located in cities are characterised by heavy traffic. This makes it impossible to avoid the presence of cars in aerial photographs. However, they are not an integral part of the landscape, so their presence in the generated photogrammetric products is unnecessary. The occurrence of cars in the images may also lead to errors such as irregularities in digital elevation models (DEMs) in roadway areas and the blurring effect on orthophotomaps. The research aimed to improve the quality of photogrammetric products obtained with the Structure from Motion algorithm. To fulfil this objective, the Yolo v3 algorithm was used to automatically detect cars in the images. Neural network learning was performed using data from a different flight to ensure that the obtained detector could also be used in independent projects. The photogrammetric process was then carried out in two scenarios: with and without masks. The obtained results show that the automatic masking of cars in images is fast and allows for a significant increase in the quality of photogrammetric products such as DEMs and orthophotomaps.
</abstract>

Vehicle detection and masking in UAV images using YOLO to improve photogrammetric products

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Reports on Geodesy and Geoinformatics

 Reports on Geodesy and Geoinformatics (formerly Reports on Geodesy) is recognized as a semi-annual scientific journal that has appeared since 1991. The volumes of this journal had been prepared and edited by the staff of the Institute of Geodesy and Geodetic Astronomy until 2008 and then during the reorganization of the Faculty of Geodesy and Cartography between years 2008-2011 the volumes were edited and printed by the scientists of the Department of Geodesy and Geodetic Astronomy of Warsaw University of Technology (WUT). Since 2012 the journal has been edited by the scientific staff of the Faculty of Geodesy and Cartography of WUT.  Each paper submitted for publishing in Reports on Geodesy and Geoinformatics is peer-reviewed. The list of our revisers contains more than 200 scientists both from Poland as well as from other countries. Our revisers are recognized specialists from different branches of geodesy and cartography. 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Visit following subsections for more details on each policy:    Publication Ethics and Publication Malpractice Statement  Open Access  Privacy Statement   Reports on Geodesy and Geoinformatics (formerly Reports on Geodesy) in keeping with tradition, is intent on presenting the whole of technological and scientific progress taking place in modern geodesy and cartography, where research and implementation of geoinformatic character play an exceptionally important role in accelerating the development of our discipline. It is for this reason that we have decided to expand the journal's formula so as to encourage a broader group of scientists and specialists in the realms of geodesy, computer science, cartography, photogrammetry, remote sensing, geography, geology, and related sciences who are developing new methods and technologies of geovisualization and are involved in the modeling of space applying GIS technology to collaborate with us. 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Reports on Geodesy and Geoinformatics

Volume 114 (2022): Issue 1 (December 2022)

A crossvalidation-based comparison of kriging and IDW in local GNSS/levelling quasigeoid modelling

Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

Vehicle detection and masking in UAV images using YOLO to improve photogrammetric products

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