The document discusses the raster data model used in geographic information systems (GIS). It defines raster data as consisting of a matrix of grids made up of rows, columns, and cells that can each store a single value. Common examples of raster data include satellite imagery, digital elevation models, and scanned maps. Raster data has advantages for modeling continuous geographic variation and works well with raster output devices, but has limitations representing discrete features and may lose detail during conversion from vector data. Popular raster file formats include TIFF, JPEG, and GIF.
This document discusses how geographic features are represented in GIS data structures. Spatial data represents the location of features, while attribute data describes characteristics. Features can be represented using vector or raster data models. Vector models store location data as x,y coordinates and connect them to form lines and polygons. Raster models divide space into a grid of cells and store a single value for each cell. Relational databases are commonly used to organize spatial and attribute data for GIS analysis and mapping.
Spatial data defines a location using points, lines, polygons or pixels and includes location, shape, size and orientation. Non-spatial data relates to a specific location and includes statistical, text, image or multimedia data linked to spatial data defining the location. The document outlines key differences between spatial and non-spatial data, noting that spatial data is multi-dimensional and correlated while non-spatial data is one-dimensional and independent, with implications for conceptual, processing and storage issues.
Also known as geospatial data or geographic information it is the data or information that identifies the geographic location of features and boundaries on Earth, such as natural or constructed features, oceans, and more. Spatial data is usually stored as coordinates and topology, and is data that can be mapped.
Remote sensing and GIS techniques are useful tools for civil engineering projects. There are several models that can be used to represent the shape of the Earth, including flat, spherical, and ellipsoidal models. The ellipsoidal model is needed for accurate measurements over long distances. A geodetic datum defines the parameters of the reference ellipsoid and the orientation of the coordinate system grid. Common datums include NAD27 and NAD83, and transformations allow conversion between them. Map projections, such as Mercator and UTM, are used to represent the 3D Earth on a 2D surface, inevitably distorting some spatial properties like shape, area, or distance.
The Universal Transverse Mercator (UTM) system is a global coordinate system that divides the world into narrow longitudinal zones projected using the Transverse Mercator projection. This projection is conformal, meaning angles and shapes are accurately represented locally. Each zone is 6 degrees wide and numbered from 1 to 60, with grid coordinates measured in kilometers from the equator within each zone. The UTM system is used internationally and forms the basis of grid referencing on topographic maps.
This document discusses methods for calculating the heights of objects like trees and buildings from aerial photos. It describes the relief/radial displacement method, where the displacement between the top and bottom of an object seen in a single aerial photo is used along with the distance from the principal point to determine height. It explains that relief displacement occurs due to perspective projection and varies with object elevation relative to the datum. An example problem demonstrates using measured displacement and distance to calculate an object's height given the flying height.
This document discusses digital photogrammetry. It begins by explaining that digital photogrammetry uses digital images that are stored and processed on a computer, rather than hard copy photos. These digital images can come from satellites, airplanes, or cameras. The document then discusses some applications of digital photogrammetry like topographic mapping and creating orthophotos, digital elevation models (DEMs), and virtual landscapes. It also notes that nadir imagery and image overlap are needed to provide 3D information. Finally, the document lists some common products of digital photogrammetry such as maps, DEMs, and virtual landscapes.
Aerial photography and photogrammetry are techniques used in remote sensing. Aerial photography involves taking photographs from aircraft and has been used since the 1850s. Photogrammetry uses photographs to measure and obtain spatial information about the objects and terrain photographed. It allows for the creation of topographic maps, cadastral maps, and large-scale construction plans more quickly and economically than traditional ground-based surveying. While aerial photography and photogrammetry provide advantages over field surveys, some on-site control and verification is still needed.
This document discusses GIS data analysis techniques including raster to vector conversion and spatial analysis through vector overlay. It provides information on various data types and models in GIS. Key analysis techniques covered are raster and vector data overlays, terrain mapping and analysis, and spatial interpolation methods. Specific vector and raster overlay methods like point-in-polygon, line-in-polygon and polygon-on-polygon are described. Spatial data editing techniques involving digitization errors and topological/non-topological editing are also summarized.
Digital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEM is frequently used to refer to any digital representation of a topographic surface. DEM is the simplest form of digital representation of topography. GIS applications depend mainly on DEMs, today.
This document help you to prepare Triangulation Network (TIN), Hillshade Map, Slope map, interpolation and Digital Elevation Model (DEM) in a area and how to interpret them.
A Geographic Information System (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data.
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
The document discusses geographic coordinate systems and map projections. It defines key concepts like geoid, spheroid, datum, latitude and longitude, projections, and the UTM coordinate system. The UTM system divides the globe into 60 zones, each 6 degrees wide, and uses a Transverse Mercator projection within each zone. UTM coordinates express a point's easting and northing distances in meters from the central meridian and equator/south pole.
This document discusses stereoscopy and parallax measurement in aerial photography. Stereoscopy uses two photographs of the same ground area taken from separate positions to create a stereo pair that enables three-dimensional viewing. Parallax is the displacement of an object caused by a change in the point of observation. Stereoscopic parallax occurs when photographs are taken of the same object from different positions, allowing measurement of differences in elevation.
Principle of aerial photography and photogrammetry, types of aerial photographs, scale of aerial photograph.
This document discusses key concepts related to data in GIS systems. It describes the different types of spatial and attribute data as well as vector and raster data formats. It explains how data is organized into layers and how those layers can be queried and overlaid to integrate information from different sources and analyze spatial patterns in the data.
This document provides an overview of basic data models used in GIS, including vector data models and raster data models. It discusses how raster data models establish a grid pattern over a geographic area with cells defined by row and column indices. Each cell is assigned a value representing dominant features or multiple features in that area. It also lists common raster data formats used in GIS like TIFF, JPEG, and netCDF files.
GIS data models define real-world phenomena in a way that computers can interpret and analyze. There are two main data models: vector and raster. Vector data models use points, lines, and polygons defined by x,y coordinates to represent discrete geographic features. Raster data models use a grid of cells or pixels to represent continuous surfaces like elevation or rainfall. Scale affects how spatial entities are represented as points, lines, or polygons in a vector data model. Common vector data formats include shapefiles, coverages, and digital line graphs.
GIS models reality through abstraction using a mix of raster, vector, and attribute data tailored to specific functions. Topological vector models record shared geometries like points and lines only once, allowing features to be connected and ensuring integrity as changes propagate between related features. Object-oriented models represent real-world phenomena as interconnected objects with their own rules and relationships.
The document discusses raster data models in GIS. Raster data models represent geographic space as a grid of cells or pixels, with each cell storing numeric values representing attributes like elevation. Key points: - Raster models use a grid-based structure of rows and columns to store imagery and represent continuous surfaces. - Each cell holds a value like elevation and has a defined spatial resolution (size). - Raster data is used for things like satellite imagery, elevation maps, and representing variables that vary continuously over space.
The document discusses geographic data structures and models used in GIS. It explains that geographic data must be encoded digitally and organized in a database to be useful for GIS. It then contrasts conventional paper maps with digital geographic data, which allows for dynamic representation and interaction. The document goes on to describe common data models for representing geographic information, including raster, vector, field-based and object-based models. It provides details on raster and vector data models, focusing on how each represents points, lines and areas digitally.
Vector data stores individual map features with high precision and has a linked attribute table for storing metadata. It is well-suited for mapmaking but poorly suited for storing continuously varying surfaces like elevation. Raster data stores information as a grid of cells, each with a single value, making it ideal for representing continuously varying data but with less precision. Some types of analysis are faster with rasters due to more developed analysis tools, while others are faster with vectors.
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Spatial data can be represented using either a raster or vector data model. The raster model divides space into a grid of cells, with each cell storing an attribute value. The vector model represents features as points, lines, and polygons made up of x,y coordinates. Common spatial data structures include raster grids for storing raster data and point dictionaries and topological networks for vector data. The two models have different strengths, with raster better for overlays and modeling surfaces, and vector more accurate for features and cartography.
The document discusses various concepts related to remote sensing and geographic information systems (GIS). It defines key terms like digital and analog images, spatial and spectral resolution, data models, and data types used in GIS. It also describes common data formats, software, techniques for data input and management, and methods for spatial analysis and map making using GIS.
Raster data consists of grids of cells that store values representing features like elevation or pixel color. Each cell has a single value and raster data can represent images through pixel color values. Raster data is stored in file formats like TIFF or directly in databases. Vector data represents geographic features as points, lines, and polygons linked to attribute data. Points are zero-dimensional, lines are one-dimensional, and polygons are two-dimensional and can measure area. Vector data respects topological rules and can model continuously varying surfaces through techniques like contours and TINs. GIS tools allow users to perform tasks like buffering shapes, merging data, and clipping layers.
Raster data represents geographic information in a grid format of cells or pixels, where each cell contains a value. Vector data represents geographic features as points, lines, and polygons with x,y coordinates. The key differences are that raster data focuses on location rather than features, represents data as generalized cells, and is better for images and modeling, while vector data focuses on features, represents data as discrete objects, and is better for accuracy and topology. The appropriate model depends on the type of data, analysis needs, and required accuracy or detail.
This document discusses different types of data and data models used in geographic information systems (GIS). It describes spatial data, which refers to the location, shape and size of geographic features, and non-spatial data, which includes other attributes. The two main spatial data models are raster, which divides space into a grid, and vector, which represents features as points, lines and polygons. Common file formats for each type of data are also listed. The document outlines functions of GIS like data entry, storage and analysis including queries, overlays and networks. Different database models for storing attribute data are also summarized, including tabular, hierarchical, network and relational models.