Fusion of Urban TanDEM-X raw DEMs using variational models

Recently, a new global Digital Elevation Model (DEM) with pixel spacing of 0.4 arcseconds and relative height accuracy finer than 2m for flat areas (slopes 20%) was created through the TanDEM-X mission. One important step of the chain of global DEM generation is to mosaic and fuse multiple raw DEM tiles to reach the target height accuracy. Currently, Weighted Averaging (WA) is applied as a fast and simple method for TanDEM-X raw DEM fusion in which the weights are computed from height error maps delivered from the Interferometric TanDEM-X Processor (ITP). However, evaluations show that WA is not the perfect DEM fusion method for urban areas especially in confrontation with edges such as building outlines. The main focus of this paper is to investigate more advanced variational approaches such as TV-L1 and Huber models. Furthermore, we also assess the performance of variational models for fusing raw DEMs produced from data takes with different baseline configurations and height of ambiguities. The results illustrate the high efficiency of variational models for TanDEM-X raw DEM fusion in comparison to WA. Using variational models could improve the DEM quality by up to 2m particularly in inner-city subsets.Comment: This is the pre-acceptance version, to read the final version, please go to IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing on IEEE Xplor

Deep learning methods applied to digital elevation models: state of the art

Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of SpainPID2019-106195RB- I00/AEI/10.13039/50110001103

Mapping three-dimensional geological features from remotely-sensed images and digital elevation models.

Accurate mapping of geological structures is important in numerous applications, ranging from mineral exploration through to hydrogeological modelling. Remotely sensed data can provide synoptic views of study areas enabling mapping of geological units within the area. Structural information may be derived from such data using standard manual photo-geologic interpretation techniques, although these are often inaccurate and incomplete. The aim of this thesis is, therefore, to compile a suite of automated and interactive computer-based analysis routines, designed to help a the user map geological structure. These are examined and integrated in the context of an expert system. The data used in this study include Digital Elevation Model (DEM) and Airborne Thematic Mapper images, both with a spatial resolution of 5m, for a 5 x 5 km area surrounding Llyn Cow lyd, Snowdonia, North Wales. The geology of this area comprises folded and faulted Ordo vician sediments intruded throughout by dolerite sills, providing a stringent test for the automated and semi-automated procedures. The DEM is used to highlight geomorphological features which may represent surface expressions of the sub-surface geology. The DEM is created from digitized contours, for which kriging is found to provide the best interpolation routine, based on a number of quantitative measures. Lambertian shading and the creation of slope and change of slope datasets are shown to provide the most successful enhancement of DEMs, in terms of highlighting a range of key geomorphological features. The digital image data are used to identify rock outcrops as well as lithologically controlled features in the land cover. To this end, a series of standard spectral enhancements of the images is examined. In this respect, the least correlated 3 band composite and a principal component composite are shown to give the best visual discrimination of geological and vegetation cover types. Automatic edge detection (followed by line thinning and extraction) and manual interpretation techniques are used to identify a set of 'geological primitives' (linear or arc features representing lithological boundaries) within these data. Inclusion of the DEM data provides the three-dimensional co-ordinates of these primitives enabling a least-squares fit to be employed to calculate dip and strike values, based, initially, on the assumption of a simple, linearly dipping structural model. A very large number of scene 'primitives' is identified using these procedures, only some of which have geological significance. Knowledge-based rules are therefore used to identify the relevant. For example, rules are developed to identify lake edges, forest boundaries, forest tracks, rock-vegetation boundaries, and areas of geomorphological interest. Confidence in the geological significance of some of the geological primitives is increased where they are found independently in both the DEM and remotely sensed data. The dip and strike values derived in this way are compared to information taken from the published geological map for this area, as well as measurements taken in the field. Many results are shown to correspond closely to those taken from the map and in the field, with an error of < 1°. These data and rules are incorporated into an expert system which, initially, produces a simple model of the geological structure. The system also provides a graphical user interface for manual control and interpretation, where necessary. Although the system currently only allows a relatively simple structural model (linearly dipping with faulting), in the future it will be possible to extend the system to model more complex features, such as anticlines, synclines, thrusts, nappes, and igneous intrusions

Geovisualization

Geovisualization involves the depiction of spatial data in an attempt to facilitate the interpretation of observational and simulated datasets through which Earth's surface and solid Earth processes may be understood. Numerous techniques can be applied to imagery, digital elevation models, and other geographic information system data layers to explore for patterns and depict landscape characteristics. Given the rapid proliferation of remotely sensed data and high-resolution digital elevation models, the focus is on the visualization of satellite imagery and terrain morphology, where manual human interpretation plays a fundamental role in the study of geomorphic processes and the mapping of landforms. A treatment of some techniques is provided that can be used to enhance satellite imagery and the visualization of the topography to improve landform identification as part of geomorphological mapping. Visual interaction with spatial data is an important part of exploring and understanding geomorphological datasets, and a variety of methods exist ranging across simple overlay, panning and zooming, 2.5D, 3D, and temporal analyses. Specific visualization outputs are also covered that focus on static and interactive methods of dissemination. Geomorphological mapping legends and the cartographic principles for map design are discussed, followed by details of dynamic web-based mapping systems that allow for greater immersive use by end users and the effective dissemination of data

Visualisation of Uncertainty in 30m Resolution Global Digital Elevation Models: SRTM v3.0 and ASTER v2

Geospatial visualisation presents us with innovative techniques of assessing uncertainty in digital elevation datasets. It gives the viewer immediate feedback on potential problems and heightens understanding of effects not easily appreciated when dealing with numerical statistics only. This study evaluated the performance of 30-metre resolution SRTM version 3.0 and ASTER GDEM version 2 over Lagos, Nigeria. Both datasets were examined by direct comparison with 176 highly accurate Ground Control Points (GCPs) coordinated by GPS (Global Positioning System) observation. The basis of comparison was on the elevation differences between the Digital Elevation Models (DEMs) and the GCPs at coincident points. The performance of both DEMs was visualised in 2D and 3D space by comparing pixel values and surface models. In the assessment, the absolute vertical accuracies of SRTM v3.0 and ASTER v2 are 4.23m and 28.73m respectively. The accuracy of SRTM for the study site proved to be higher than the value of 16m presented in the original SRTM requirement specification. ASTER did not meet up with its 17m overall accuracy specification.KEYWORDS: Uncertainty, Visualisation, Digital Elevation Model, SRTM, ASTER

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Digital elevation model correction in urban areas using extreme gradient boosting, land cover and terrain parameters

The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artifacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30m DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The correction achieved significant accuracy gains which are competitive with other proposed methods. The root mean square error (RMSE) of Copernicus DEM improved by 46 to 53% while the RMSE of AW3D DEM improved by 72 to 73%. These results showcase the potential of gradient boosted trees for enhancing the quality of DEMs, and for improved hydrological modelling in urban catchments.Comment: 8 page

Validation of Global Digital Elevation Models in Lagos State, Nigeria

International audienceSatellite-derived Digital Elevation Models (DEM) are fast replacing the classical method of elevation data acquisition by ground survey methods. The availability of free and easily accessible DEMs is no doubt of great significance and importance, and a valuable resource in the quest to accurately model the earth's surface topography. However, the suitability of Digital Elevation Models in simulating the topography of the earth at micro, local and regional scales is still an active area of research. The accuracy of Digital Elevation Models vary from one location to another. As such, it is important to conduct local and regional assessments to inform the global user community on the relative performance of these DEMs. This study evaluates the accuracy of the 30-metre Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Models version 2, the 1-kilometre GTOPO30, the 90-metre Shuttle Radar Topography Mission v4 and the 1-kilometre Shuttle Radar Topography Missionv2.1 Digital Elevation Models by validating with highly accurate GPS check-points over Lagos, Nigeria. With a Root Mean Square Error of 3.75m, the results show that Shuttle Radar Topography Mission v4 has the highest vertical accuracy followed by Shuttle Radar Topography Mission v2.1 (Root Mean Square Error: 5.73m), Advanced Spaceborne Thermal Emission and Reflection Radiometer (Root Mean Square Error: 21.70m), and GTOPO30 which shows the lowest vertical accuracy (Root Mean Square Error: 29.41m). By conducting the accuracy assessment of these products in Lagos, this study informs efforts directed at the exploitation of these Digital Elevation Models for topographic mapping and other scientific and environmental application

Cloud-based Lineament Extraction of Topographic Lineaments from NASA Shuttle Radar Topography Mission Data

© 2016 The Authors. This paper presents initial results of a JavaTM based, feature extraction tool, which represents a standard implementation of a hill-shading algorithm that transforms a 2D image to pseudo 3D image to enhance edge contrast in combination with an edge detection Canny algorithm that performs segmentation to produce multidirectional sun-shaded images and their edges. Our goal is to firstly automate this processes in JavaTM to obtain multidirectional optimization of edge discovery and secondly scale this algorithm to the complete SRTM raster collection at multiple pixel resolutions to document the distribution of Earth topographic discontinuities from continental to regional and local scales, respectively on the order of 1000s, 100s and 10s of kilometers. This tool will support the automatic extraction of lineaments of the transformed images to predict the existence of linear features that can be often found in association with ore deposits and landslides, if they represent tectonic lineaments. The collection of processed big data, represents a multi-scale data repository that may find use for these and other geological and environmental applications. We present preliminary outputs from a case study conducted in the Flin-Flon greenstone belt in Canada, which is well known for its base-metal endowment. In this study, two main shaded relief images with multidirectional illumination were created in Java each with four azimuth angles of the light sources and from which our developed tool extracts automatically multiple lineaments. The extracted lineaments represent both positive and negative elevation breaks, due to sudden slope inversions identifying dominantly crest lines and valleys. Preliminary results show good agreement with drainage networks, mapped fault lines and orientations of structures measured in the field. The main trends of the extracted lineaments of both images are NW-SE, N-S, E-W and NE-SW