Geomatics Engineering (GE)

Candidates appearing for the Graduate Aptitude Test in Engineering (GATE) 2022 Exam with  Geomatics Engineering (Code: GE) as their subject paper can refer to the detailed syllabus provided below. Geomatics Engineering is one of the 29 optional papers in the exam. The syllabus is divided into two parts  – Part A and Part B (Sections 1 and 2). 

PART A: Common

Engineering Mathematics – Surveying measurements, Accuracy, Precision, Most probable value,

Errors and their adjustments, Regression analysis, Correlation coefficient, Least square adjustment,

Statistical significant value, Chi square test.

Remote Sensing – Basic concept, Electromagnetic spectrum, Spectral signature, Resolutions-

Spectral. Spatial, Temporal and Radiometric, Platforms and Sensors, Remote Sensing Data

Products – PAN, Multispectral, Microwave, Thermal, Hyperspectral, Visual and digital interpretation

methods

GNSS – Principle used, Components of GNSS, Data collection methods, DGPS, Errors in

observations and corrections.

GIS – Introduction, Data Sources, Data Models and Data Structures, Algorithms, DBMS, Creation of

Databases (spatial and non-spatial), Spatial analysis – Interpolation, Buffer, Overlay, Terrain

Modeling and Network analysis.

PART B: Section I

Maps – Importance of maps to engineering projects, Types of maps, Scales and uses, Plotting

accuracy, Map sheet numbering, Coordinate systems- Cartesian and geographical, map projections,

map datum – MSL, Geoid, spheroid, WGS-84.

Land Surveying – Various Levels, Levelling methods, Compass, Theodolite and Total Station and

their uses, Tachometer, Trigonometric levelling, Traversing, Triangulation and Trilateration.

Aerial Photogrammetry – Types of photographs, Flying height and scale, Relief (height)

displacement, Stereoscopy, 3-D Model, Height determination using Parallax Bar, Digital Elevation

Model (DEM), Slope.
PART B: Section II

Data Quantization and Processing – Sampling and quantization theory, Principle of Linear System,

Convolution, Continuous and Discrete Fourier Transform.

Digital Image Processing – Digital image characteristics: image histogram and scattergram and

their significance, Variance-Covariance matrix, Correlation matrix and their significance.

Radiometric and Geometric Corrections – Registration and Resampling techniques.

Image Enhancement – Contrast Enhancement: Linear and Non-linear methods; Spatial Enhancement: Noise and Spatial filters

Image Transformation – Principal Component Analysis (PCA), Discriminant Analysis, Color

transformations (RGB – IHS, CMYK), Indices (Ratios, NDVI, NDWI).

Image Segmentation and Classification – Simple techniques.