In this work, a new filtering approach is proposed for a fully automatic Digital Terrain Model (DTM) extraction from very high resolution airborne images derived Digital Surface Models (DSMs). Our approach represents an enhancement of the existing DTM extraction algorithm Multi-directional and Slope Dependent (MSD) by proposing parameters that are more reliable for the selection of ground pixels and the pixelwise classification. To achieve this, four main steps are implemented: Firstly, 8 well-distributed scanlines are used to search for minima as a ground point within a pre-defined filtering window size. These selected ground points are stored with their positions on a 2D surface to create a network of ground points. Then, an initial DTM is created using an interpolation method to fill the gaps in the 2D surface. Afterwards, a pixel to pixel comparison between the initial DTM and the original DSM is performed utilising pixelwise classification of ground and non-ground pixels by applying a vertical height threshold. Finally, the pixels classified as non-ground are removed and the remaining holes are filled. The approach is evaluated using the Vaihingen benchmark dataset provided by the ISPRS working group III / 4. The evaluation includes the comparison of our approach, denoted as Network of Ground Points (NGPs) algorithm, with the DTM created based on MSD as well as a reference DTM generated from LiDAR data. The results show that our proposed approach over performs the MSD approach.

An automatic method for the regularisation of building outlines is presented, utilising a combination of data‐ and model‐driven approaches to provide a robust solution. The core part of the method includes a novel data‐driven approach to generate approximate building polygons from a list of given boundary points. The algorithm iteratively calculates and stores likelihood values between an arbitrary starting boundary point and each of the following boundary points using a function derived from the geometrical properties of a building. As a preprocessing step, building segments have to be identified using a robust algorithm for the extraction of a digital elevation model. Evaluation results on a challenging dataset achieved an average correctness of 96·3% and 95·7% for building detection and regularisation, respectively.

Lake Sawa located in Southwest Iraq is a unique natural landscape and without visible inflow and outflow from its surrounding regions. Investigating the environmental and physical dynamics and the hydrological changes in the lake is crucial to understanding the impact of hydrological changes, as well as to inform planning and management in extreme weather events or drought conditions. Lake Sawa is a saltwater lake, covering about 4.9 square kilometers at its largest in the 1980s. In the last decade, the lake has dried out, shrinking to less than 75% of its average size. This contribution focuses on calculating the bank erosion and accretion of Lake Sawa utilizing remote sensing data captured by Landsat platforms (1985–2020). The methodology was validated using higher-resolution Sentinel imagery and field surveys. The outcomes indicated that the area of accretion is significantly higher than erosion, especially of the lake’s banks in the far north and the south, in which 1.31 km2 are lost from its surface area. Further analysis of especially agricultural areas around the lake have been performed to better understand possible reasons causing droughts. Investigations revealed that one possible reason behind droughts is related to the rapid increase in agriculture areas surrounding the lake. It has been found that the agriculture lands have expanded by 475% in 2020 compared to 2010. Linear regression analysis revealed that there is a high correlation (69%) between the expanding of agriculture lands and the drought of Lake Sawa.

Abstract. The aim of this project is to create the required framework to allow the transformation of the Canning River location survey data captured by Dr. J. A. Ludwig Preiss from 1841 into today’s maps and to utilise visualisation techniques to analyse the results. The original survey data includes distances and bearing observations as well as 14 historical maps. Firstly, the old survey data (includes distances and angles measurements) is plotted into a local coordinates system using modern surveying software (MAGNET Office). Then, common points (unchanged locations) are identified by comparison with the plotted and the current paths of the river. A similarity and affine transformation are used to find transformation parameters that allow to geo-locate the plotted river into the current geodetic datum (MGA94). The calculated Root Mean Squared Errors (RMSE) are 21.7 m and 21.1 m obtained by geo-locating the common points using similarity and affine transformation, respectively. For geo-referencing the historical maps, the similarity, projective and Thin Plate Spline (TPS) transformations have been applied. It has been found that one point of interest (referred to as Nairn’s house), which was drawn in one of the historical maps, still exists today (now known as Maddington Homestead). The distances from the actual position of Nairn’s house to its position in the georeferenced maps using similarity, projective and TPS are 11.8 m, 13 m, and 14 m respectively. All the gained information and map details are utilised in creating a dynamic visualisation suitable for comparing the generated map and historical map with modern aerial imaging and DTM data.

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