Improving the Accuracy of Global DEM of Differences (DoD) in Google Earth Engine for 3-D Change Detection Analysis

Capolupo, Alessandra

doi:10.1109/jstars.2021.3130063

Cited by 7 publications

(3 citation statements)

References 48 publications

(72 reference statements)

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“…Furthermore, other studies evaluated the vertical accuracy of open-source DEM, i.e., SRTM and ASTER (Yap et al (2019), Miliaresis and Paraschou (2005), Suwandana et al (2012), Mirza et al (2011), Mukherjee et al (2013), Falorni et al (2005, and Shaikh et al (2021)). To improve the accuracy of the DEM, Yamazaki et al (2017) produced a highaccurate map of global terrain elevations, Capolupo (2021) enhanced the accuracy of GDEM of differences in the Google Earth Engine for 3D change detection analysis, Muhadi, Mohd Kassim, and Abdullah (2019) Improved DEM using data fusion technique for oil palm replanting phase. In addition, Xu and Zhou (2016) suggested recovering distorted DEMs of regular terrain while Deilami et al (2012) recommended reducing pit and flat error for improving DEM accuracy.…”

Section: Background and Related Workmentioning

confidence: 99%

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Alemam,

YONG,

Sani-Mohammed

2023

Artificial Satellites

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The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.

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Section: Background and Related Workmentioning

confidence: 99%

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Alemam,

YONG,

Sani-Mohammed

2023

Artificial Satellites

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“…In addition, the direct differential operation between two digital elevation models (DEMs) of the same place acquired at different times, known as the DEM of difference (DOD), is employed to characterize the differences of multi-temporal surface morphology. DOD can significantly reduce the impact of outliers and high surface roughness, making it suitable for rapidly measuring surface morphology differences under low surface roughness and is therefore widely applied [37], [38], [39], [40]. However, in most cases, high-precision DEMs may not have been obtained prior to the occurrence of landslides because the location of landslides is usually unknown in advance.…”

Section: Introductionmentioning

confidence: 99%

Robust Estimation of Landslide Displacement From Multitemporal UAV Photogrammetry-Derived Point Clouds

He,

Ming,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Existing algorithms based on remote sensing for landslide displacement estimation, such as C2C, C2M, DOD, and M3C2, are sensitive to errors generated in data processing, and further improving their accuracy is difficult. To address this issue, given that redundant observations may occur in landslide monitoring, we proposed a robust estimation method of landslide displacement from multi-temporal unmanned aerial vehicle (UAV) photogrammetry-derived point clouds. The proposed method first establishes the relevant graph to manage the trajectory of error propagation for landslide displacement estimation for all possible paths. Two modules, namely, intraand inter-estimates, are explored to reduce the impact of outliers and high surface roughness in point clouds derived by UAV photogrammetry. Individually, the intra-estimate operation is used to calculate landslide displacement between two temporal point clouds by robust estimation considering outlier constraint, and the inter-estimate operation is used to obtain the optimal calculation of landslide displacement by minimizing a given objective function using IGG robust estimation proposed by the Institute of Geodesy and Geophysics at the Chinese Academy of Sciences. Experimental results show that the proposed method is significantly superior to conventional methods such as C2C, C2M, and M3C2, with an accuracy improvement of at least 8%.

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“…Version 4.1 of the SRTM DEM is distributed by the Consortium for Spatial Information (CGIAR-CSI) [45]. It is important to note that global DEMs are generally less accurate than local DEMs and may contain errors, the main sources of which are the post-processing or generation techniques used [46]. The area chosen for this study is largely flat, so negligible errors in altitude can be accepted.…”

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confidence: 99%

Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support

Delogu,

Caputi,

Perretta

et al. 2023

Sustainability

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Hyperspectral satellite missions, such as PRISMA of the Italian Space Agency (ASI), have opened up new research opportunities. Using PRISMA data in land cover classification has yet to be fully explored, and it is the main focus of this paper. Historically, the main purposes of remote sensing have been to identify land cover types, to detect changes, and to determine the vegetation status of forest canopies or agricultural crops. The ability to achieve these goals can be improved by increasing spectral resolution. At the same time, improved AI algorithms open up new classification possibilities. This paper compares three supervised classification techniques for agricultural crop recognition using PRISMA data: random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). The study was carried out over an area of 900 km2 in the province of Caserta, Italy. The PRISMA HDF5 file, pre-processed by the ASI at the reflectance level (L2d), was converted to GeoTiff using a custom Python script to facilitate its management in Qgis. The Qgis plugin AVHYAS was used for classification tests. The results show that CNN gives better results in terms of overall accuracy (0.973), K coefficient (0.968), and F1 score (0.842).

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Improving the Accuracy of Global DEM of Differences (DoD) in Google Earth Engine for 3-D Change Detection Analysis

Cited by 7 publications

References 48 publications

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Robust Estimation of Landslide Displacement From Multitemporal UAV Photogrammetry-Derived Point Clouds

Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support

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