Lecture

Modeling and Interpolation

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In course

ENV-140: Fundamentals of geomatics

Bases de la géomatique pour les ingénieur·e·s civil et en environnement. Présentation des méthodes d'acquisition, de gestion et de représentation des géodonnées. Apprentissage pratique avec des méthodes topométriques et d'imagerie du territoire. ...

Description

This lecture covers the modeling and interpolation techniques used in geomatics, such as digital elevation models, photogrammetry, and triangulated irregular networks. It explains how to input raw data, create surfaces, and interpolate measurements to generate accurate models.

In MOOC

Elements of Geomatics

This basic geomatics course introduces the concepts and methods of geodata acquisition, management and representation. It includes the basics of topometry, geodesy and cartography, with an emphasis on computation.

Instructors (2)

Pierre-Yves Gilliéron

He graduated in Surveying Engineering at the Swiss Federal Institute of Technology Lausanne in 1988. He started his professional career in photogrammetry and digital mapping. He joined the Geodetic Eng. Laboratory in 1997 where he worked as research scientist on various navigation and satellite positioning projects. Since 2018 he is deputy head of the section in environmental sciences and engineering at EPFL. -- He was member of the board of the swiss institute of navigation (ION-CH), member of the swiss geodetic committee (SGK) and expert in different committees of the swiss road association (VSS).

Devis Tuia

I come from Ticino and studied in Lausanne, between UNIL and EPFL. After my PhD at UNIL in remote sensing, I was postdoc in Valencia (Spain), Boulder (CO) and EPFL, working on model adaptation and prior knowledge integration in machine learning. In 2014 I became Research Assistant Professor at University of Zurich, where I started the 'multimodal remote sensing' group. In 2017, I joined Wageningen University (NL), where I was professor of the GeoInformation Science and Remote Sensing Laboratory. Since 2020, I joined EPFL Valais, to start the ECEO lab, working at the interface between Earth observation, machine learning and environmental sciences.

Official source

Related concepts (24)

Multivariate interpolation

In numerical analysis, multivariate interpolation is interpolation on functions of more than one variable (multivariate functions); when the variates are spatial coordinates, it is also known as spatial interpolation. The function to be interpolated is known at given points and the interpolation problem consists of yielding values at arbitrary points . Multivariate interpolation is particularly important in geostatistics, where it is used to create a digital elevation model from a set of points on the Earth's surface (for example, spot heights in a topographic survey or depths in a hydrographic survey).

Interpolation

In the mathematical field of numerical analysis, interpolation is a type of estimation, a method of constructing (finding) new data points based on the range of a discrete set of known data points. In engineering and science, one often has a number of data points, obtained by sampling or experimentation, which represent the values of a function for a limited number of values of the independent variable. It is often required to interpolate; that is, estimate the value of that function for an intermediate value of the independent variable.

Polynomial interpolation

In numerical analysis, polynomial interpolation is the interpolation of a given bivariate data set by the polynomial of lowest possible degree that passes through the points of the dataset. Given a set of n + 1 data points , with no two the same, a polynomial function is said to interpolate the data if for each . There is always a unique such polynomial, commonly given by two explicit formulas, the Lagrange polynomials and Newton polynomials.

Bilinear interpolation

In mathematics, bilinear interpolation is a method for interpolating functions of two variables (e.g., x and y) using repeated linear interpolation. It is usually applied to functions sampled on a 2D rectilinear grid, though it can be generalized to functions defined on the vertices of (a mesh of) arbitrary convex quadrilaterals. Bilinear interpolation is performed using linear interpolation first in one direction, and then again in another direction.

Nearest-neighbor interpolation

Nearest-neighbor interpolation (also known as proximal interpolation or, in some contexts, point sampling) is a simple method of multivariate interpolation in one or more dimensions. Interpolation is the problem of approximating the value of a function for a non-given point in some space when given the value of that function in points around (neighboring) that point. The nearest neighbor algorithm selects the value of the nearest point and does not consider the values of neighboring points at all, yielding a piecewise-constant interpolant.

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