Raster Data

GISBox is a one-stop 3D GIS data editing, conversion and publishing platform that supports editing in multiple GIS formats such as OSGB/GEOTIFF/RVT, converting to 3DTiles/Terrain and publishing.

Introduction

Raster data is a commonly used data format in geographic information systems (GIS). Its essence is a two-dimensional or three-dimensional data grid composed of a collection of pixels (grid cells). Each pixel (or cell) stores specific geographic information, such as terrain elevation, land use type, temperature or precipitation, expressed in the form of a numerical value. In the field of GIS, satellite images, aerial photos, digital elevation models (DEMs), etc. are usually stored and used in the form of raster data.

File Structure

Raster data has a variety of file formats, and different formats have their own characteristics. The following are common raster data formats:

GeoTIFF (.tif, .tiff):

TIFF format images with geographic location information.
Widely used in the GIS field, supporting multi-band and large data sets.

JPEG2000 (.jp2):

Supports high compression ratio and can significantly reduce file size.
Suitable for storing large-scale remote sensing image data.

ECW (.ecw):

Raster format with high compression ratio and high-speed display performance.
Commonly used for online map services or large-scale image data.

IMG (.img):

Raster format dedicated to Erdas IMAGINE software.
Suitable for storing large satellite or aerial image data.

ASCII Grid (.asc):

Text-based raster data format.
Usually used to store simple terrain or spatial analysis data.

Pros

Suitable for storing and processing large-scale spatial data: Raster data can efficiently represent large-scale geographic information and is suitable for storing large-scale remote sensing images, DEM data, etc.
Strong continuity, suitable for expressing continuous variables: In terrain, climate or environmental monitoring, raster data can well represent continuously changing data, such as temperature distribution or precipitation changes.
Easy to perform spatial analysis and calculation: Raster data is widely used in geospatial analysis such as spatial overlay, buffer analysis, and surface slope calculation.
Intuitive visualization: Raster data exists in the form of images and is easy to display intuitively, such as map rendering or spatial data visualization.

Cons

Large data volume, heavy storage and processing burden: Raster data often takes up a large storage space, and requires high computing resources when processing large-scale high-resolution data.
**Resolution dependence, reduced accuracy after zooming in: **The resolution of raster data determines the accuracy. When the data is zoomed in, pixels will be blocked, affecting readability.
Position accuracy is not as good as vector data: Raster data stores spatial information in grid units and cannot accurately represent boundaries or points like vector data.
Limited attribute information storage capacity: Raster data mainly stores numerical information and cannot store complex attribute information like vector data, which has limitations in some analysis scenarios.

Application Scenario

In remote sensing image analysis, images taken by satellites or drones are usually stored in raster format. Raster data can be used for land classification, vegetation coverage analysis, or urban expansion monitoring. In digital elevation models (DEMs), raster data can be used to calculate slope, aspect, and terrain undulation, and is widely used in geological exploration and hydrological analysis. In meteorological and environmental monitoring, raster data can be used to visualize the distribution of temperature, precipitation, or air pollution. In addition, raster data also plays an important role in urban planning and disaster response.