Mostafa Kiani Shahvandi


Last Name

Kiani Shahvandi

First Name

Mostafa

ORCID

0000-0001-5705-7014

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2019 - 201912020 - 202569
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Publications 1 - 10 of 70
  • Identification and Classification of Phenomena in Multispectral Satellite Imagery Using a New Image Smoother Method and its Applications in Environmental Remote Sensing
    Item type: Other Conference Item
    Kiani Shahvandi, Mostafa (2020)
    In this paper a new method of image smoothing for satellite imagery and its applications in environmental remote sensing are presented. This method is based on the global gradient minimization over the whole image. With respect to the image discrete identity, the continuous minimization problem is discretized. Using the finite difference numerical method of differentiation, a simple yet efficient 5 × 5-pixel template is derived. Convolution of the derived template with the image in different bands results in the discrimination of various image elements. This method is extremely fast, besides being highly precise. A case study is presented for the northern Iran, covering parts of the Caspian Sea. Comparison of the method with the usual Laplacian template reveals that it is more capable of distinguishing phenomena in the image.
  • Geophysically Informed Machine Learning for Improving Rapid Estimation and Short‐Term Prediction of Earth Orientation Parameters
    Item type: Journal Article
    Kiani Shahvandi, Mostafa; Dill, Robert; Dobslaw, Henryk; et al. (2023)
    Journal of Geophysical Research: Solid Earth
    Rapid provision of Earth orientation parameters (EOPs, here polar motion and dUT1) is indispensable in many geodetic applications and also for spacecraft navigation. There are, however, discrepancies between the rapid EOPs and the final EOPs that have a higher latency but the highest accuracy. To reduce these discrepancies, we focus on a data-driven approach, present a novel method named ResLearner, and use it in the context of deep ensemble learning. Furthermore, we introduce a geophysically constrained approach for ResLearner. We show that the most important geophysical information to improve the rapid EOPs is the effective angular momentum functions of atmosphere, ocean, land hydrology, and sea level. In addition, semidiurnal, diurnal, and long-period tides coupled with prograde and retrograde tidal excitations are important features. The influence of some climatic indices on the prediction accuracy of dUT1 is discussed, and El Niño Southern Oscillation is found to be influential. We developed an operational framework, providing the improved EOPs on a daily basis with a prediction window of 63 days to fully cover the latency of final EOPs. We show that under the operational conditions and using the rapid EOPs of the International Earth Rotation and Reference Systems Service (IERS), we achieve improvements as high as 60%, thus significantly reducing the differences between rapid and final EOPs. Furthermore, we discuss how the new final series IERS 20 C04 is preferred over 14 C04. Finally, we compare against EOP hindcast experiments of the European Space Agency, on which ResLearner presents comparable improvements.
  • Improving the Accuracy of GNSS Orbit Predictions using Machine Learning Approaches
    Item type: Other Conference Item
    Gou, Junyang; Rösch, Christine; Shehaj, Endrit; et al. (2022)
    EGUsphere
    Precise orbit determination is vital for the increasingly vast number of space objects around the Earth. Moreover, accurate orbit prediction of GNSS satellites is essential for many real-time geodetic applications, including real-time navigation. The typical way to obtain accurate orbit predictions is using physics-based orbit propagators. However, the prediction errors accumulate with time because of insufficient modeling of the changing perturbing forces. Motivated by the rapid expansion of computing power and the considerable data volume of satellite orbits available in recent years, we can apply machine learning (ML) and deep learning (DL) algorithms to assess if they can be used to further reduce orbit errors. In this study, we focus on the orbit prediction of GNSS constellations. We investigate the potential of using different ML and DL algorithms for improving the accuracy of the ultra-rapid products from IGS. As ground truth we consider the IGS final products, and the differences between the ultra-rapid and final products are computed and serve as targets for the ML/DL methods. In this context, we combine the advantages of physics-based and data-driven ML/DL methods. Since the major errors of GNSS orbits are expected to be caused by the deficiency of solar radiation pressure models, we consider different related parameters as additional features to implicitly model the solar impact, such as the C0,0 terms of global ionosphere maps. In order to accurately model the effect of solar radiation pressure on the radial, along-track and cross-track components of the satellite orbit system, the geometric relation between the Sun, the satellite and the Earth are also considered. Furthermore, the performances of different ML/DL algorithms are compared and discussed. Due to the temporal characteristics of the problem, certain sequential modeling algorithms, such as Long Short-Term Memory and Gated Recurrent Unit, show superiority. Our approach shows promising results with average improvements of over 40% in 3D RMS within the 24-hours prediction interval of the ultra-rapid products.
  • Finslerian metrics in geodesy and geophysics
    Item type: Other Publication
    Kiani Shahvandi, Mostafa (2020)
  • Machine learning-based prediction of geodetic variables at the Geodetic Prediction Center of ETH Zurich
    Item type: Other Conference Item
    Soja, Benedikt; Crocetti, Laura; Gou, Junyang; et al. (2024)
  • Spheroidal spline interpolation and its application in geodesy
    Item type: Journal Article
    Kiani Shahvandi, Mostafa; Chegini, Nabi; Safari, Abdolreza; et al. (2020)
    Geodesy and Cartography
    The aim of this paper is to study the spline interpolation problem in spheroidal geometry. We follow the minimization of the norm of the iterated Beltrami-Laplace and consecutive iterated Helmholtz operators for all functions belonging to an appropriate Hilbert space defined on the spheroid. By exploiting surface Green’s functions, reproducing kernels for discrete Dirichlet and Neumann conditions are constructed in the spheroidal geometry. According to a complete system of surface spheroidal harmonics, generalized Green’s functions are also defined. Based on the minimization problem and corresponding reproducing kernel, spline interpolant which minimizes the desired norm and satisfies the given discrete conditions is defined on the spheroidal surface. The application of the results in Geodesy is explained in the gravity data interpolation over the globe.
  • Machine learning-based GRACE accelerometer transplant
    Item type: Other Conference Item
    Behzadpour, Saniya; Gou, Junyang; Kiani Shahvandi, Mostafa; et al. (2023)
  • Ellipsoidal spline functions for gravity data interpolation and smoothing
    Item type: Journal Article
    Kiani Shahvandi, Mostafa; Chegini, Nabi (2019)
    Earth Observation and Geomatics Engineering
    The aim of this paper is to study the theory of spline interpolation and smoothing problems on the surface of a triaxial ellipsoid for the Consecutive Iterated Helmholtz operator and a set of linearly independent evaluation functionals. Spline functions were introduced based on the minimization of a semi-norm in the context of a semi-Hilbert space whose domain was the surface of the ellipsoid. The semi-Hilbert space was decomposed into two different subspaces, a particular Hilbert space and the null space of the desired operator. Using surface Green’s functions for the Consecutive Iterated Helmholtz operator, the reproducing kernel for the Hilbert subspace was constructed. Spline and smoothing functions were explicitly represented based on the reproducing kernel and the evaluation functionals. An approximation formula was derived to facilitate the potential use in Earth’s gravity field data interpolation and smoothing. An application of this technique was presented to show the interpolation of potential fields over Iran. Ellipsoidal and spherical splines were compared as well. It revealed the ellipsoidal splines to be more accurate than the spherical counterparts.
  • On spline interpolation in ellipsoidal geometry and its application in geodesy
    Item type: Working Paper
    Kiani Shahvandi, Mostafa (2020)
Publications 1 - 10 of 70