Quality assessment of DEM derived from topographic maps for geomorphometric purposes

Szypuła, Bartłomiej

doi:10.1515/geo-2019-0066

Cited by 37 publications

(23 citation statements)

References 31 publications

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“…Nowadays, DEM provides the most standard method to derive the desired parameters such as curvature, drainage network, slope, and aspect for managing the geomorphological process [61]. The extracted topographic parameters rely on the DEM resolution and accuracy, and hence, large-scale DEMs at a high resolution, or small cell size, are more reliable to draw out these elements [62,63]. The interpolation quality is based on the required number and distribution of the selected GCPs [20,[64][65][66], but it is influenced by data diffusion rather than its density [23].…”

Section: Resultsmentioning

confidence: 99%

Impact of interpolation techniques on the accuracy of large-scale digital elevation model

et al. 2020

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AbstractThere is no doubt that the tremendous development of information technology was one of the driving factors behind the great growth of surveying and geodesy science. This has spawned modern geospatial techniques for data capturing, acquisition, and visualization tools. Digital elevation model (DEM) is the 3D depiction of continuous elevation data over the Earth’s surface that is produced through many procedures such as remote sensing, photogrammetry, and land surveying. DEMs are essential for various surveying and civil engineering applications to generate topographic maps for construction projects at a scale that varies from 1:500 to 1:2,000. GIS offers a powerful tool to create a DEM with high resolution from accurate land survey measurements using interpolation methods. The aim of this research is to investigate the impact of estimation techniques on generating a reliable and accurate DEM suitable for large-scale mapping. As a part of this study, the deterministic interpolation algorithms such as ANUDEM (Topo to Raster), inverse distance weighted (IDW), and triangulated irregular network (TIN) were tested using the ArcGIS desktop for elevation data obtained from real total station readings, with different landforms to show the effect of terrain roughness, data density, and interpolation process on DEM accuracy. Furthermore, comparison and validation of each interpolator were carried out through the cross-validation method and numerous graphical representations of the DEM. Finally, the results of the investigations showed that ANUDEM and TIN models are similar and significantly better than those attained from IDW.

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Section: Resultsmentioning

confidence: 99%

Impact of interpolation techniques on the accuracy of large-scale digital elevation model

et al. 2020

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“…This paper also introduced the mean absolute error (MAE) [44] and root mean square error (RMSE) [45] to evaluate the quality of a reconstructed DEM. The MAE (formula (3)) can better reflect the actual situation of prediction error.…”

Section: Evaluation Methods Of Experimental Resultsmentioning

confidence: 99%

Super-resolution reconstruction of a digital elevation model based on a deep residual network

Jiao

Wang

et al. 2020

Open Geosciences

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The digital elevation model (DEM) is an important basic data tool applied in geoscience applications. Because of its high cost and long development cycle of enhancing hardware performance, designing the related models and algorithms to improve the resolution of DEM is of considerable significance. At present, there is little research on DEM super-resolution based on deep learning, and the results of the reconstructed DEMs obtained by existing methods are inaccurate. Therefore, deepening of the network layers is utilized to improve the accuracy of a reconstructed DEM. This paper designs a neutral network model with 30 convolutional layers to learn the feature mapping relationship between a low- and high-resolution DEM. To avoid the problem of network degradation caused by increasing the number of convolutional layers, residual learning is introduced to accelerate the convergence speed of the model, thereby preferably realizing the DEM super-resolution process. The results show that DEM super-resolution based on a deep residual network is better than that obtained using a neural network with fewer convolutional layers, and the reconstructed result of the DEM based on a deep residual network is remarkably improved in terms of the peak signal to noise ratio and visual effect.

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“…30 m) and accuracy (ca. 5 m to several hundred) [27]. Still, they have been used in solar resource map creation and operating a service that returns a site's horizon profile, available from JRC's PVGIS website [28].…”

Section: Identifying Suitable Open Geospatial Datasets and Previous Workmentioning

confidence: 99%

Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Skeie

Gustavsen

2021

Energies

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In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

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Quality assessment of DEM derived from topographic maps for geomorphometric purposes

Cited by 37 publications

References 31 publications

Impact of interpolation techniques on the accuracy of large-scale digital elevation model

Impact of interpolation techniques on the accuracy of large-scale digital elevation model

Super-resolution reconstruction of a digital elevation model based on a deep residual network

Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

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