AUTOMATED ROAD BREACHING TO ENHANCE EXTRACTION OF NATURAL DRAINAGE NETWORKS FROM ELEVATION MODELS THROUGH DEEP LEARNING

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

Abstract. The National Map provides geospatial data that support various uses such as resource management, disaster response, and science investigations. To properly support these needs, data themes of the National Map must be regularly updated and spatially integrated as the features on the ground change because of environmental or man-made events. The elevation theme of the National Map is managed through the 3D Elevation Program (3DEP), which is currently (2019) coordinating collection of high resolution (HR) elevation data for the United States over an eight-year period (Sugarbaker et al. 2014). Through this program, lidar point-cloud data are being collected for the conterminous United States, Hawaii, and U.S. territories, with coarser resolution interferometric synthetic aperture radar (ifsar) data being collected for the remote areas of Alaska. HR digital elevation models (DEMs) can be generated at 1 and 3 meter resolution from the lidar point-cloud data and are also furnished by 3DEP.This research develops automated methods to update the hydrography and transportation themes of the National Map in a manner that integrates with the HR elevation and image layers. Surface water drainage networks can be extracted from a HR DEM using flow-direction and flow-accumulation modelling, but results of these methods vary depending on environmental conditions and the existence of anthropogenic features that may affect the accuracy of the elevation model, such as vegetative cover, roads, bridges, and other urban structures. Hydrologic conditioning or enforcement of a HR DEM overcome some of these issues and improve flow modelling for drainage network extraction through techniques such as filtering (Passalaqua et al. 2010), filling sinks (Tarboton 1997), cutting channels through embankments at culvert and bridge locations (Poppenga et al. 2012), or burning-in existing streams (Maidment 1996). However, drainage network extraction results can vary substantially with these techniques and the methods generally require some manual intervention and/or tuning of parameters (Poppenga et al. 2013). Consequently, additional work is needed to streamline and further automate such methods for the various landscape conditions within the United States.

High-resolution (HR) digital elevation models (DEMs) have been found to be critical for many applications, as they provide accurate basic geodata, as well as more information and accurate results. However, despite the importance of HR DEM, many areas across the world, particularly in developing countries, lack access to them. Thus, researchers inspired by the success of super resolution (SR) on image enhancement, especially the use of deep learning (DL) approaches, instead of using high-precision equipment to obtain HR DEMs, have recently presented and are discussing the concept of DEM SR. This paper provides a review of such a DEM SR technique. It first explains the basic idea of SR, then describes DEM SR, and finally, a review of DEM SR algorithms proposed in the literature is presented, describing the main approaches and some of the shortcomings. This review shall provide the geoscientific community with information on an emerging alternative technique for acquiring HR DEM that is more cost-effective and can contribute to open data, which is widely recognised as the key engine for achieving the Sustainable Development Goals (SDGs).

Generating high-resolution DEMs in mountainous regions using ICESat-2/ATLAS photons

Flood models predict inundation extents, and can be an important source of information for flood risk studies. Accurate flood models require high resolution and high accuracy digital elevation models (DEM); current global DEMs do not capture the topographic details in floodplains, and this often leads to inaccurate prediction of flood extents by flood models. Flood extents obtained from remotely sensed data provide indirect information about topography. Here, we attempt to use this information along with model predictions to produce better floodplain topography. The algorithm we describe is a two-step process: first, we reduce the noise along the observed flood boundaries for all particles. Then, the model predictions from these modified DEMs are assimilated with observations using a particle batch smoother. We implemented the algorithm for a synthetic test case. For the nominal case, we observed a significant improvement in accuracy in terms of RMSE (35% reduction), bias (20%) and standard deviation (40%). We conducted sensitivity analysis by using priors of varying bias (0.5 m, 1 m, 2 m) and standard deviation (1 m, 2 m, 4 m). The bias reduced to ~0.5 m or below in all the cases: the reduction in bias varied from 11% to 76%. The standard deviation of errors in the final estimate was almost half of the prior: the reduction varied from 40% to 49%. The reduction in RMSE ranged between 35% and 67%. For the case with 2 m bias and 4 m standard deviation (SRTM-like error levels), bias went down to 0.48 m (76% reduction), and standard deviation reduced to 2.24 m (44% reduction). Flood inundation maps produced from the final estimate DEMs also improved on its prior. For the 2 m bias cases, true positive rate (TPR) for peak inundation went from ~30% to more than 57% in all three cases. The algorithm produces promising results, and this type of analysis can be performed in data-poor floodplains where high resolution DEMs do not exist.

Accurate delineation of drainage networks is critical for many hydrologically related applications. The commonly used methods for drainage network extraction from digital elevation models (DEMs) have limitations in low-relief terrain areas. High-quality DEMs are required for effectively applying these methods in extracting drainage networks in low-relief terrains. Airborne light detection and ranging (LiDAR) offers high-accuracy terrain data. With LiDAR data, high-accuracy and high-resolution DEMs can be generated. The results of drainage network extraction for two sub-catchments on the western Victorian Volcanic Plains (VVP) are reported. Drainage networks and some parameters describing drainage network composition, including the stream orders, the numbers of streams and the stream lengths, were derived from both the LiDAR DEM and the Vicmap DEM. The LiDAR-derived DEM is shown to offer significantly more detail, especially for delineating low-order stream (headwater) segments in sub-catchments of low-relief terrain.

Machine Learning (ML) is extensively used in diverse topic domains, including geographical information. Despite its limitations, using Digital Elevation Models (DEMs) is gradually being considered in various operational applications. This study explores the application of machine learning algorithms, Shuttle Radar Topography Mission (SRTM) data with different resolutions, and data collected by Unmanned Aerial Vehicle (UAV) technology to produce high-resolution DEMs. The proposed construction approach is based on eight algorithms Linear Regression, Decision Tree, Random Forest, Ridge, Lasso, SVR, K-Neighbors Regressor, and XGB Regressor to deal with the complex features of urban topography to reconstruct high-resolution urban DEMs. The proposed algorithms were applied to two different study areas in Egypt. The results were contrasted with a reference DEM obtained from the ground measurement data (UAV). The numerical accuracy and terrain feature preserving effects of the Linear Regression algorithm in the first study area can generate reconstructed DEMs that better match the Reference DEMs, show lower mean absolute error (MAE), mean square error (MSE), and improve the accuracy of the terrain parameters by the overall fitness R2 of .971. The results showed that the Linear Regression algorithm is the most accurate with an R2 of .985. Compared to other commonly used methods, the current proposed approach offers a cost-effective and innovative method for acquiring high-resolution DEMs in other data-scarce regions, resulting in superior results. Our research is a comprehensive examination of geographical artificial intelligence (Geo AI), which is a term that refers to the processing of geographic information.

The spatial variability of input parameters plays a crucial role in the interpretation of geomorphic indices, with digital elevation models (DEMs) being the primary data source. However, the influence of DEM resolution on these indices has rarely been investigated. This study investigated the influence of DEM resolution on the assessment of tectonic activity using the normalized stream length–gradient (SLk) index, which reflects variations along river profiles. The SLk index is sensitive to changes in river gradients that may result from active faulting or differential uplift, making it a valuable tool for identifying zones of active tectonic deformation. Therefore, understanding the impact of DEM resolution on SLk analysis is critical for accurately detecting and interpreting subtle tectonic signals, particularly in intraplate regions where deformation is slow and geomorphic expressions are faint and discontinuous. By comparing high-resolution LiDAR-derived DEMs (L-DEMs) and low-resolution topographic map-derived DEMs (T-DEMs), we analyzed the SLk index distributions along the Yangsan Fault, Korean Peninsula, an intraplate setting with Quaternary activity. According to the results, SLk anomalies derived from L-DEMs had a continuous distribution along the fault, closely aligning with known surface ruptures and indicating active tectonic deformation. In contrast, SLk anomalies derived from T-DEMs were sporadic and less continuous, especially in low-relief landscapes such as alluvial fans and floodplains, highlighting the limitations of T-DEMs in detecting fault-related features. High-resolution DEMs were better able to capture finer-scale geomorphic features, such as fault scarps, deflected streams, and lineaments associated with active tectonics, providing a more comprehensive view of fault-related deformation. This discrepancy highlights the importance of resolution choice in tectonic assessments, as low-resolution DEMs may underestimate the tectonic activities of intraplate faults by missing subtle topographic variations. While the choice of DEM resolution may depend on study area, scope, and data availability, high-resolution DEMs are critical for identifying tectonic activity in intraplate regions where geomorphic features of faulting due to slow deformation are subtle and dispersed.

A 10-metre meshed high resolution Digital Elevation Model (DEM) has been integrated with Geographic Information Systems (GIS). High resolution DEM plays a crucial role in future GIS applications such as constructing 3D systems in Japan, but it is not sufficient for precise requirements from various applications. Also, a one-metre high resolution pansharpen image from the IKONOS satellite has been integrated with a digital map on the GIS system at the same time. IKONOS images cover 3-dimensional surfaces constructed by 10 m DEM. A precise GIS map of Kitakyushu City in Japan has been used as a digital map, which is quite accurate.

At the end of the nineties the emergence of high resolution (1 m) digital elevation models (DEMs) settled the context of high precision geomorphological analysis. These new elevation models permitted to reveal structures that remained heretofore undetectable. Earth scientists henceforth benefit from a field of analysis with a textural richness that was never attained before. However, the complexity and the volume of the data reveal a series of questions and problems. The storage size has increased, the computational processes have become heavier, and the visual or digital interpretation has become more complex. Moreover, these new models make it possible to characterize and analyse much smaller phenomena than previously. Traditional DEMs with resolutions ranging from 10 m to 90 m can be used to analyse a valley or a hillside. Transposed to a cartographic scale, this corresponds at best to a 1 : 25 000 ratio. As for high resolution DEMs, they show much more detailed structural levels and can be used to analyse geomorphological features of 2 – 3 meters, this corresponding to scales ranging from 1 : 10 000 to 1 : 1 000. Yet, in the abundance offered by this growing resolution, large geomorphological structures are still present, including the finer structures. They are even the actuators of processes relevant to larger cartographic scales. Consequently, high resolution DEMs contain a multitude of structures, which exist throughout their interactions with other structures at other scales. This is the context of the present study. Geomorphometry – the quantitative counterpart of exploratory geomorphology – permits to explore and quantify a wide range of shapes and terrain indicators. At higher resolution however, the methods of this discipline can hardly be used. Geomorphometrical methods are based on a geometric model (a quadratic surface) and few of these methods can be applied it in a multiscale context. Furthermore inappropriate techniques are frequently used, hence the idea to move to a multiscale approach called the wavelet transform. The latter had previously been explored by few researchers within the geomorphometry community, but never thoroughly to micro- and to mesoscales. Due to the non-stationarity of DEMs, the wavelet transform was preferred to the Fourier transform in order to decompose DEMs into multiscale spaces. This facilitates a coherent navigation from scale to scale, but also makes new scale specific phenomena emerge for different frequencies. The wavelet transform is a technique widely used in image analysis. It allows decomposing a signal according to its frequency components, but also according to the position of the frequencies in the signal. Its multi-scale capacity is an effective analytical tool in multiple domains. More particularly in geomorphology, structural components – specific to a specific phenomenon – are well determined in these sub-spaces specific to the scale continuum. Finally an in-depth analysis of the phenomena enabled us to understand processes and their and their phenomenological inter-dependencies. In order to understand the effects and outcomes of the approach we developed an artificial landslide. We then computed some profiles and analysed the autocorrelation, slope attenuation and local fractal indicator. The resulting high-pass information of the wavelet transform has also been analysed and filtered using several types of filters. In a case study we used a real-world landslide to validate the transform and to understand its impact on geological structures. Within this case-study we conducted a web-based survey that allowed the participants to analyse the landslide using wavelet results and to make comments on the potential of the wavelet transform in the field of geomorphometry. Moreover, important contributions of this thesis are new algorithms that allow the illustration of the structural coherence in relation to each subspace. These are based on the theory of vision of Marr and on structure tensors. The results of our studies show a high consistency. The wavelet transform thereby extends the range of tools in geomorphometry. The different structural scale levels show that such these methods are needed to better understand the phenomenology of geomorphological processes.

Drainage network extraction from a high-resolution DEM using parallel programming in the .NET Framework

Very high resolution digital elevation models (DEM) provide the opportunity to represent the micro-level detail of topographic surfaces, thus increasing the accuracy of the applications that are depending on the topographic data. The analyses of micro-level topographic surfaces are particularly important for a series of geospatially related engineering applications. However, the generation of very high resolution DEM using, for example, LiDAR data is often extremely computationally demanding because of the large volume of data involved. Thus, we use a high-performance and parallel computing approach to resolve this big data-related computational challenge facing the generation of very high resolution DEMs from LiDAR data. This parallel computing approach allows us to generate a fine-resolution DEM from LiDAR data efficiently. We applied this parallel computing approach to derive the DEM in our study area, a bottomland hardwood wetland located in the USDA Forest Service Santee Experimental Forest. Our study demonstrated the feasibility and acceleration performance of the parallel interpolation approach for tackling the big data challenge associated with the generation of very high resolution DEM.

Global flood hazard models have recently become a reality thanks to the release of open access global digital elevation models, the development of simplified and highly efficient flow algorithms, and the steady increase in computational power. In this commentary we argue that although the availability of open access global terrain data has been critical in enabling the development of such models, the relatively poor resolution and precision of these data now limit significantly our ability to estimate flood inundation and risk for the majority of the planet’s surface. The difficulty of deriving an accurate ‘bare-earth’ terrain model due to the interaction of vegetation and urban structures with the satellite-based remote sensors means that global terrain data are often poorest in the areas where people, property (and thus vulnerability) are most concentrated. Furthermore, the current generation of open access global terrain models are over a decade old and many large floodplains, particularly those in developing countries, have undergone significant change in this time. There is therefore a pressing need for a new generation of high resolution and high vertical precision open access global digital elevation models to allow significantly improved global flood hazard models to be developed.

Quantifying landscape form can provide crucial insight into the interactions between tectonics and climate. River long profile morphology, quantified by metrics such as channel steepness, is the most commonly used tool to investigate topographic form, with many studies relating long profile morphology to uplift rate, precipitation, sediment properties, or lithology, for example. River long profiles record the signal of external forcing over large spatial scales (i.e. tens of kilometres). This has many advantages: for example, it is a convenient scale for analysing variations in large-scale processes, such gradients in tectonic uplift. It also means that high resolution digital elevation models (DEMs) are not required and therefore river long profiles can be extracted globally. However, analysis of river long profiles over tens of kilometres can also result in signal smoothing and subsequent loss of finer scale tectonic or climatic signatures encoded into the landscape.Tectonic and climatic processes do not only leave their fingerprint in the long profiles of rivers. Hilltops, hillslopes, and valleys make up the majority of Earth’s landscapes by area, yet their morphology has received much less attention than that of rivers. This is in part due to the difficulty in accurately extracting hilltops and valley morphology from DEMs, especially on a global scale. Here, I show that we can now extract hilltop and valley metrics from high-resolution (< 15 m) DEMs over orogenic to continental scales using new topographic analysis techniques and high-performance computing facilities. I argue that by combining hilltop, hillslope, and valley metrics, we can obtain more information about tectonic and climatic processes than from river profiles alone.

High-resolution Digital Elevation Model (DEM) data provides essential information for pluvial flood simulation. Although the increased accessibility and quality of publicly available DEM datasets can facilitate geospatial analysis at various scales, existing DEM datasets with global coverage mostly lack sufficient spatial resolution for pluvial flood simulations, which require detailed topographic information to be included in the simulation. Simulating flood scenarios with low-resolution DEMs (>30m) can result in substantial deviations from real cases. This issue becomes even more severe for flood-prone areas in data-scarce developing countries.Image super-resolution is a technique for reconstructing low-resolution information into high-resolution data. Various deep-learning models have been employed for this task, primarily focusing on generating high-resolution natural-colour images. However, the effects of these deep learning models on enhancing the resolution of DEM data have not been extensively investigated. One of the state-of-the-art super-resolution models, the Residual Channel Attention Network (RCAN), has gained popularity due to its accuracy and efficiency. Leveraging publicly available low-resolution global DEM data and high-resolution regional DEM data, this study assesses the performance of RCAN models in a DEM super-resolution task. The experimental results suggest that, compared to conventional interpolation methods, the tested RCAN model exhibits superior performance in constructing high-resolution DEM data. The generated super-resolution DEM data were then tested in pluvial flood simulations and achieved substantially higher realism in modelling floodwater distribution. The proposed method for constructing super-resolution DEMs opens up the possibility of simulating flooding at hyper-resolution globally.