Готові списки джерел за темами / DEM void filling

Добірка наукової літератури з теми "DEM void filling"

Автор: Grafiati

Опубліковано: 6 вересня 2023

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Статті в журналах з теми "DEM void filling"

Zhang, Chunsen, Shu Shi, Yingwei Ge, Hengheng Liu, and Weihong Cui. "DEM Void Filling Based on Context Attention Generation Model." ISPRS International Journal of Geo-Information 9, no. 12 (December 7, 2020): 734. http://dx.doi.org/10.3390/ijgi9120734.

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Анотація:

The digital elevation model (DEM) generates a digital simulation of ground terrain in a certain range with the usage of 3D point cloud data. It is an important source of spatial modeling information. Due to various reasons, however, the generated DEM has data holes. Based on the algorithm of deep learning, this paper aims to train a deep generation model (DGM) to complete the DEM void filling task. A certain amount of DEM data and a randomly generated mask are taken as network inputs, along which the reconstruction loss and generative adversarial network (GAN) loss are used to assist network training, so as to perceive the overall known elevation information, in combination with the contextual attention layer, and generate data with reliability to fill the void areas. The experimental results have managed to show that this method has good feature expression and reconstruction accuracy in DEM void filling, which has been proven to be better than that illustrated by the traditional interpolation method.

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Qiu, Yue, and Liu. "Void Filling of Digital Elevation Models with a Terrain Texture Learning Model Based on Generative Adversarial Networks." Remote Sensing 11, no. 23 (November 28, 2019): 2829. http://dx.doi.org/10.3390/rs11232829.

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Анотація:

Digital elevation models (DEMs) are an important information source for spatial modeling. However, data voids, which commonly exist in regions with rugged topography, result in incomplete DEM products, and thus significantly degrade DEM data quality. Interpolation methods are commonly used to fill voids of small sizes. For large-scale voids, multi-source fusion is an effective solution. Nevertheless, high-quality auxiliary source information is always difficult to retrieve in rugged mountainous areas. Thus, the void filling task is still a challenge. In this paper, we proposed a method based on a deep convolutional generative adversarial network (DCGAN) to address the problem of DEM void filling. A terrain texture generation model (TTGM) was constructed based on the DCGAN framework. Elevation, terrain slope, and relief degree composed the samples in the training set to better depict the terrain textural features of the DEM data. Moreover, the resize-convolution was utilized to replace the traditional deconvolution process to overcome the staircase in the generated data. The TTGM was trained on non-void SRTM (Shuttle Radar Topography Mission) 1-arc-second data patches in mountainous regions collected across the globe. Then, information neighboring the voids was involved in order to infer the latent encoding for the missing areas approximated to the distribution of training data. This was implemented with a loss function composed of pixel-wise, contextual, and perceptual constraints during the reconstruction process. The most appropriate fill surface generated by the TTGM was then employed to fill the voids, and Poisson blending was performed as a postprocessing step. Two models with different input sizes (64 × 64 and 128 × 128 pixels) were trained, so the proposed method can efficiently adapt to different sizes of voids. The experimental results indicate that the proposed method can obtain results with good visual perception and reconstruction accuracy, and is superior to classical interpolation methods.

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Takaku, J., T. Tadono, M. Doutsu, F. Ohgushi, and H. Kai. "UPDATES OF ‘AW3D30’ ALOS GLOBAL DIGITAL SURFACE MODEL IN ANTARCTICA WITH OTHER OPEN ACCESS DATASETS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 401–8. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-401-2021.

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Анотація:

Abstract. In 2016, the first processing of the semi-global digital surface models (DSMs) utilizing all the archives of stereo imageries derived from the Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) was successfully completed. The dataset was freely released to the public in 30 m grid spacing as the ‘ALOS World 3D - 30m (AW3D30)’, which was generated from its original version processed in 5 m or 2.5 m grid spacing. The dataset has been updated since then to improve the absolute/relative height accuracies with additional calibrations. However, the most significant update that should be applied for improving the data usability is the filling of void areas, which correspond to approx. 10% of semiglobal coverage, mostly due to cloud covers. In 2020, we completed the filling process by using other open-access digital elevation models (DEMs) such as Shuttle Radar Topography Mission (SRTM) DEM, Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), ArcticDEM, etc., except for Antarctica. In this paper, we report on the filling process of the remaining voids in Antarctica by using other open-access DEMs such as Reference Elevation Model of Antarctica (REMA) DSM, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X, TDX) 90m DEM, and ASTER GDEM to complete the void-free semi-global AW3D30 datasets.

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Zhou, Guoqing, Bo Song, Peng Liang, Jiasheng Xu, and Tao Yue. "Voids Filling of DEM with Multiattention Generative Adversarial Network Model." Remote Sensing 14, no. 5 (March 1, 2022): 1206. http://dx.doi.org/10.3390/rs14051206.

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Анотація:

The digital elevation model (DEM) acquired through photogrammetry or LiDAR usually exposes voids due to phenomena such as instrumentation artifact, ground occlusion, etc. For this reason, this paper proposes a multiattention generative adversarial network model to fill the voids. In this model, a multiscale feature fusion generation network is proposed to initially fill the voids, and then a multiattention filling network is proposed to recover the detailed features of the terrain surrounding the void area, and the channel-spatial cropping attention mechanism module is proposed as an enhancement of the network. Spectral normalization is added to each convolution layer in the discriminator network. Finally, the training of the model by a combined loss function, including reconstruction loss and adversarial loss, is optimized. Three groups of experiments with four different types of terrains, hillsides, valleys, ridges and hills, are conducted for validation of the proposed model. The experimental results show that (1) the structural similarity surrounding terrestrial voids in the three types of terrains (i.e., hillside, valley, and ridge) can reach 80–90%, which implies that the DEM accuracy can be improved by at least 10% relative to the traditional interpolation methods (i.e., Kriging, IDW, and Spline), and can reach 57.4%, while other deep learning models (i.e., CE, GL and CR) only reach 43.2%, 17.1% and 11.4% in the hilly areas, respectively. Therefore, it can be concluded that the structural similarity surrounding the terrestrial voids filled using the model proposed in this paper can reach 60–90% upon the types of terrain, such as hillside, valley, ridge, and hill.

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Zhou, Ping, Jing Hong Du, and Xi Xiang Duan. "Study on Initial States of Steel Balls in Mill by Discrete Element Method." Advanced Materials Research 546-547 (July 2012): 120–24. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.120.

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Анотація:

Based on Discrete Element Method(DEM), initial state models of steel balls were establisheded by Particle Flow Code in three Dimensions (PFC 3D), the initial void rate of steel balls at different filling rate were calculated. The results showed that at the same filling rate, the initial void rate of steel balls decreased as steel ball’s diameter decreased. The initial void rate of steel balls with one diameter and grading steel balls both increased gradully as ball filling rate increased, but the initial void rate of grading steel balls were smaller than that of steel balls with one diameter. The Stratification phenomenon will occur after steel balls in grading scheme reached to the initial equilibrium sates, that is, Large steel balls moved near the mill’s center, but small steel balls moved away from the mill’s center and close to the cylinder of mill, which is benifical to improve grinding effeciency.

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Yan, Wai Yeung. "Scan Line Void Filling of Airborne LiDAR Point Clouds for Hydroflattening DEM." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 6426–37. http://dx.doi.org/10.1109/jstars.2021.3089288.

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Takaku, J., T. Tadono, M. Doutsu, F. Ohgushi, and H. Kai. "UPDATES OF ‘AW3D30’ ALOS GLOBAL DIGITAL SURFACE MODEL WITH OTHER OPEN ACCESS DATASETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 24, 2020): 183–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-183-2020.

Повний текст джерела

Анотація:

Abstract. In 2016 we first completed the global data processing of digital surface models (DSMs) by using the whole archives of stereo imageries derived from the Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS). The dataset was freely released to the public in 30 m grid spacing as the ‘ALOS World 3D - 30m (AW3D30)’, which was generated from its original version processed in 5 m or 2.5 m grid spacing. The dataset has been updated since then to improve the absolute/relative height accuracies with additional calibrations. However the most significant update that should be applied for improving the data usability is the filling of void areas, which correspond to approx. 10% of global coverage, mostly due to cloud covers. In this paper we introduce the updates of AW3D30 filling the voids with other open-access DSMs such as Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), ArcticDEM, etc., through inter-comparisons among these datasets.

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Zhou, Liwen, Peng Han, Kun Liu, Lianghua Feng, and Guangqiang Liu. "Particulate Scale Multiparticle Finite Element Method Modeling on the 2D Compaction and Release of Copper Powder." Mathematical Problems in Engineering 2019 (November 16, 2019): 1–10. http://dx.doi.org/10.1155/2019/5269302.

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Анотація:

Herein, two-dimensional (2D) single-action die compaction process of copper (Cu) powder was simulated by the multiparticle finite element method (MPFEM) at particulate scale. The initial packing structure, generated by the discrete element method (DEM), was used as an input for the FEM model, where the mesh division of each particle was discretized. The evolution of macro- and microscopic properties, such as relative density, stress distribution, particle deformation, void filling behavior, and force transmission, during compaction and pressure release processes have been systematically studied. The results revealed that the force is mainly concentrated on largely deformed regions of the particles during compaction and formed a contact force network, which hindered the densification process. In the compact, the shorter side of the large void edges rendered higher stress than the longer side. On the other hand, the stress distribution of small void edges remained uniform. After pressure release, large residual stress was observed at the contact area of the adjacent particles and the maximum stress was observed at the particles’ edges. Moreover, the residual stress did not proceed to the interior of the particles. Meanwhile, the stress of large void edges has been completely released but exhibited a nonuniform distribution. The smaller fraction of void filling resulted in a larger reduction of the released stress after pressure removal. Also, the particles closer to the upper die exhibited higher average equivalent von Mises stress inside the particles during compaction and pressure release processes.

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Franks, Shannon, James Storey, and Rajagopalan Rengarajan. "The New Landsat Collection-2 Digital Elevation Model." Remote Sensing 12, no. 23 (November 28, 2020): 3909. http://dx.doi.org/10.3390/rs12233909.

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Анотація:

The Landsat Collection-2 distribution introduces a new global Digital Elevation Model (DEM) for scene orthorectification. The new global DEM is a composite of the latest and most accurate freely available DEM sources and will include reprocessed Shuttle Radar Topographic Mission (SRTM) data (called NASADEM), high-resolution stereo optical data (ArcticDEM), a new National Elevation Dataset (NED) and various publicly available national datasets including the Canadian Digital Elevation Model (CDEM) and DEMs for Sweden, Norway and Finland (SNF). The new DEM will be available world-wide with few exceptions. It is anticipated that the transition from the Collection-1 DEM at 3 arcsecond to the new DEM will be seamless because processing methods to maintain a seamless transition were employed, void filling techniques were used, where persistent gaps were found, and the pixel spacing is the same between the two collections. Improvements to the vertical accuracy were realized by differencing accuracies of other elevation datasets to the new DEM. The greatest improvement occurred where ArcticDEM data were used, where an improvement of 35 m was measured. By using theses improved vertical values in a line of sight algorithm, horizontal improvements were noted in some of the most mountainous regions over multiple 30-m Landsat pixels. This new DEM will be used to process all of the scenes from Landsat 1-8 in Collection-2 processing and will be made available to the public by the end of 2020.

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Ghosh, Sohanjit, Ishan Sharma, and Deepak Dhingra. "Granular segregation on the rubble-pile asteroid Itokawa." EPJ Web of Conferences 249 (2021): 03042. http://dx.doi.org/10.1051/epjconf/202124903042.

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Анотація:

We investigate the dynamics of regolith on rubble-pile asteroids to explain granular processes observed in reality. In particular, we explain how the appearance of boulders on the surface of asteroid Itokawa could have resulted from a size sorting process in granular media called the Brazil Nut Effect (BNE). The Discrete Element Method (DEM) is implemented to perform numerical simulations of the BNE in a micro-gravity environment caused by inter-particle collisions during seismic vibrations. Firstly, we present the results of how the BNE depends on the magnitude of surface gravity. It is estimated that segregation processes on Itokawa occur over much longer time-scales (in the order of a few hundred years) than the same processes would require in the presence of a strong gravitational field, like on Earth. Secondly, we also find that the size sorting could also result from kinetic sieving encountered during granular avalanches. Finally, we discuss how the void-filling mechanism becomes more efficient when there is a higher relative size difference between the boulders and the surrounding grains. Our model has important implications in understanding the resurfacing of Itokawa by trying to explain one of the many complex geophysical processes that occur in such unique conditions.

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Більше джерел

Дисертації з теми "DEM void filling"

Shayegan, Amir. "Les matériaux d'obturation et de coiffage pulpaire des dents lactéales." Doctoral thesis, Universite Libre de Bruxelles, 2012. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209613.

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Анотація:

Les soins pulpaires en denture lactéale sont les soins fréquemment réalisés en odontologie pédiatrique et la réaction pulpaire ainsi que les mécanismes de réparation tissulaire lors d’un traumatisme ne sont pas très bien connus ou investigués. En se référant à la littérature dentaire, on constate que les recherches sont souvent focalisées sur la denture définitive.

Objectifs

1) Etudier l’influence du traitement avec divers matériaux sur la formation dentinaire, la structure tissulaire et le degré d’inflammation.

2) Acquérir des connaissances sur l’interaction à l’interface matériau/tissu.

3) Etudier le rôle de la voie de signalisation Notch (1 et 2) dans le processus de remaniement et réparation du tissu pulpaire en cas de traumatisme.

Matériels et méthodes

Deux types de soins pulpaires, la pulpotomie et le coiffage direct, sont réalisés sur les dents lactéales porcines et les résultats sont étudiés après 3 périodes :7 jours, 21-28 jours et 90 jours.

La pulpotomie :8 matériaux, 10 dents par matériau, 2 matériaux par cochon et par période pour un total de 240 dents.

Le coiffage direct :10 matériaux, 10 dents par matériau, 2 matériaux par cochon et par période pour un total de 300 dents.

Par la suite, différentes techniques ont été utilisées :

1. La microscopie optique :étude des coupes histologiques après coloration.

2. La microscopie électronique à transmission :observer les caractéristiques morphologiques du tissu pulpaire et analyser les structures cellulaires en détail. L’objectif principal de cette recherche était centré sur les biomatériaux pour la période de 21-28 jours afin d’examiner l’interaction entre le biomatériau et le complexe dentino-pulpaire après leur placement.

3. La voie de signalisation Notch (1 et 2): investiguer l’intervention éventuelle de cette voie dans le processus de régénération tissulaire par différenciation des cellules pour les deux périodes de 7 et 21-28 jours pour tous les matériaux utilisés.

Résultats

A. Microscopie optique :

1. La pulpotomie :les biomatériaux provoquent moins d’infiltrat de cellules inflammatoires au niveau du tissu pulpaire et favorisent la déposition dentinaire.

2. Le coiffage direct :on obtient les mêmes résultats que dans le cadre de la pulpotomie.

B. Microscopie électronique en transmission :

1. L’induction du tissu calcifié ou la formation de néo-dentine s’est seulement produite au niveau du site de l’exposition après le placement des biomatériaux dans les 2 types du traitement (pulpotomie & coiffage direct). En revanche aucune formation du tissu calcifié n’a été observée dans le parenchyme pulpaire à distance du site de l’exposition.

2. Cet examen montre que les cellules en contact des biomatériaux ou même à proximité de ces derniers présentent un réticulum endoplasmique élargi parallèle à la longueur de la cellule. Des mitochondries, plusieurs appareils de Golgi et des éléments denses sont observés dans le cytoplasme cellulaire.

3. Cet examen a également montré que le MTA et le WPC, considérés par la littérature dentaire comme non-résorbables, sont phagocytés par les cellules histiocytaires.

C. Voie de signalisation de Notch :

Nos recherches n’ont pas montré de marquage concluant pour aucun de matériaux sauf dans le cadre de coiffage direct pour les dents traitées au MTA et Ca(OH)2 pour la période de 7 jours. Par contre, aucun marquage n’est observé après 21-28 jours.
Doctorat en Sciences dentaires
info:eu-repo/semantics/nonPublished

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Bulusu, Mani Madhoolika. "Interpolation of Digital Elevation Models using Generative Adversarial Networks." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6058.

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Анотація:

A digital elevation model (DEM) is a three-dimensional representation of elevation data of a terrain such as a terrestrial terrain acquired by a reconnaissance aircraft or a lunar terrain acquired using a Chandrayaan rover. Terrestrial DEMs are used in hydrological modeling, geomorphology, and glaciology. Lunar DEMs can be used to locate natural resources and to identify prospective landing sites for exploratory missions. Hence, high quality, reliable DEMs are of great significance. DEMs are generally captured using LiDAR (Light Detection and Ranging), stereophotogrammetry, and time-of-flight cameras. However, a reliable DEM cannot be constructed if there are no adequate landmark points/features. This is the case with smooth terrains, occlusions, presence of multiple voids. The measurements are nonuniform in general. Hence, there is a need for interpolation and void-filling techniques that can estimate the elevation with a high accuracy. Inverse distance weighting (IDW), De Launay triangulation, and Kriging are some of the popular benchmark algorithms for interpolating scattered and nonuniformly spaced data. Manual parameter tuning, inability to recover high-frequency information, and high computational complexity are some of the issues that these traditional interpolation techniques suffer from. Deep Learning (DL) has proven to be effective in providing excellent results in the field of image processing and computer vision, specifically in the tasks of super resolution, image in-painting, extrapolation, and segmentation. With the massive success of DL in several image processing and computer vision applications, its applicability has been explored for solving the DEM interpolation problem as well. However, convolutions are not readily defined if the measurements are nonuniform. Hence, the recent DL based research on DEM interpolation has only focused only on regularly spaced data. We address the realistic problem of DEM interpolation from irregularly spaced measurements, with the density of measurements varying spatially. This is a new and unexplored direction in the deep learning setting. We propose a new and a robust DL architecture based on Generative Adversarial Networks (GANs) to perform interpolation and result in a uniform DEM with a user-specified resolution. The generator comprises three modules: Learnable Distance Weighting Module (LDW), DEM in-painting architecture, and Continuous Convolution (CC) modules. We design the novel LDW module as a learnable counterpart to the popular IDW algorithm that operates on the distances between the measurements and grid locations. This reduces the problem to that of inpainting post the LDW transformation. The proposed method is evaluated on synthetically generated data and on standard publicly available NASA (LOLA LRO) datasets using the mean relative error and PSNR as performance metrics. Extensive experiments justify the effectiveness and accuracy of the proposed approach in comparison with the benchmark techniques. We conclude the thesis by discussing possible future directions for DL based DEM interpolation.
ISRO - IISc Space Technology Cell

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Тези доповідей конференцій з теми "DEM void filling"

Bachmann, Markus, Carolina Gonzalez, Jose-Luis Bueso-Bello, Paola Rizzoli, and Manfred Zink. "TanDEM-X Edited DEM: Automated Global Void Filling and Water Flattening." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883327.

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Звіти організацій з теми "DEM void filling"

Baah, Prince. Implementing Epoxy Injection in Concrete Overlaid Bridge Decks. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317588.

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Анотація:

Concrete overlays have proven to be an effective maintenance treatment as they slow de-icing chemical and water penetration into the original deck surface. Typically, due to vibration, structural flexibility, and weak bond between the concrete overlay and deck concrete potentially due to poor construction, de-bonding develops at the boundary between the original deck and overlay. This de-bonding creates voids, providing reservoir for chloride laden water to fill after it penetrates cracks in the surface. Displacing this liquid solution and filling these cracks with epoxy helps reduce freeze/thaw cycling and reduces spalling. The epoxy material protects the bridge deck from moisture and helps support the overlay and prevent it from failing under traffic loads. This leads to an extension of the asset life span and life cycle cost benefit increase. Overall, the bridge infrastructure performance improves significantly with greater mobility and less traffic interruptions due to repairs to bridges on our roadway infrastructures. Epoxy injection helps extends the service life of bridge decks and reduces the need for emergency bridge deck patching and as a result improves safety of road users. Epoxy injection has been added to INDOT’s Maintenance Work Performance Standards during the course of this study.

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