Raster Data
Raster data represents geographic information as a grid of cells or pixels, where each cell holds a value representing a property such as elevation, temperature, or land cover. It is fundamental to remote sensing, terrain modeling, and continuous surface analysis in GIS.
Raster data is one of the two primary data models in GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation..., representing geographic phenomena as a regular grid of cells (pixels) where each cell stores a numeric value corresponding to a measured attribute. This grid-based structure makes raster data ideally suited for representing continuous surfaces and phenomena that vary gradually across space, such as elevation, temperature, precipitation, soil moisture, and spectral reflectance captured by satellite sensors.
Structure and Characteristics
A raster dataset is defined by its spatial extent (geographic coverage), resolution (cell size), number of bands (layers of values per cell), and bit depth (range of possible values per cell). Cell size determines the level of spatial detail: smaller cells capture finer features but require more storage and processing power. Single-band rasters store one value per cell, suitable for elevation models or temperature surfaces. Multi-band rasters store multiple values per cell, as in satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... where each band captures a different wavelength of electromagnetic radiation. Common raster formats include GeoTIFFGeoTIFFGeoTIFF is a public domain metadata standard that embeds georeferencing information within TIFF image files, enabling..., JPEG2000, MrSID, and cloud-optimized formats like Cloud Optimized GeoTIFF (COG).
Applications
Raster data is central to many geospatial disciplines. Remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... relies entirely on raster imagery from satellites and aerial platforms for land cover classificationLand Cover ClassificationLand cover classification is the process of categorizing Earth's surface into distinct classes such as forest, cropla..., vegetation health monitoring, and change detectionChange DetectionChange detection uses geospatial data and imagery to track and analyze alterations in landscapes, infrastructure, or .... Digital elevation models (DEMs) stored as raster grids enable terrain analysisTerrain AnalysisTerrain analysis derives quantitative measurements and descriptive information about the Earth's land surface from di... including slope, aspect, hillshadeHillshadeHillshade simulates the illumination of a terrain surface by a hypothetical light source, creating a shaded relief im..., and watershed delineation. Climate and weather modeling produces raster outputs of temperature, precipitation, and wind patterns. Land cover and land use mapping classifies satellite raster imagery into categories like forest, urban, and agricultural land. Continuous surface modeling uses raster interpolation to estimate values at unmeasured locations from sample points.
Advantages
Raster data excels at representing continuous phenomena that vary across space, which is difficult to capture with discrete vector features. Map algebraMap AlgebraMap algebra is a set of operations for performing cell-by-cell computations on raster datasets, enabling mathematical... enables powerful cell-by-cell mathematical operations between raster layers, supporting complex environmental modeling. Raster analysis is computationally straightforward due to the regular grid structure, and modern GPUs can accelerate raster processing dramatically. The data model aligns naturally with satellite and aerial sensor output.
Challenges
Raster datasets can be extremely large, particularly at high resolutions or with many spectral bands, creating storage and processing challenges. The fixed cell size means that sharp boundaries and small features may be poorly represented compared to vector dataVector DataVector data represents geographic features as discrete points, lines, and polygons with associated attribute informat.... Mixed pixels, where a single cell spans multiple land cover types, introduce classification uncertainty. Resampling rasters to different resolutions or projections can introduce interpolation artifacts.
Emerging Trends
Cloud-optimized raster formats like COG and Zarr are enabling efficient streaming and partial reading of massive raster datasets. Analysis-ready data (ARD) pipelines pre-process satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... into standardized, immediately usable raster products. Deep learning is revolutionizing raster image classificationImage ClassificationImage classification is the process of categorizing pixels in remote sensing imagery into land cover or land use clas... and feature extraction. Data cubes that organize multi-temporal raster data enable efficient time-series analysis of environmental change.
Code-Beispiele
import rasterio
import numpy as np
# Read a GeoTIFF raster file
with rasterio.open("elevation.tif") as src:
elevation = src.read(1) # Band 1
transform = src.transform
crs = src.crs
print(f"Shape: {elevation.shape}")
print(f"CRS: {crs}")
print(f"Min: {np.nanmin(elevation):.1f}m")
print(f"Max: {np.nanmax(elevation):.1f}m")Verwandte Mapular-Lösungen
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