GeoTIFF Reader

Read GeoTIFF raster files (.tif, .tiff) - the most common geospatial raster format.

Format Name

gtiff_gdal

Overview

This is a named GDAL Reader that presets the driver option to "GTiff". GeoTIFF is the de facto standard for geospatial raster data, combining TIFF image format with geospatial metadata.

Key Features:

• Industry standard format for geospatial rasters
• Supports multiple bands (RGB, multispectral, etc.)
• Embedded spatial reference and geotransform
• Compression options (LZW, DEFLATE, JPEG, etc.)
• Cloud-optimized variant (COG) support

Basic Usage

Python

# Read GeoTIFF file (sample-data Volumes path)
df = spark.read.format("gtiff_gdal").load("/Volumes/main/default/geobrix_samples/geobrix-examples/nyc/sentinel2/nyc_sentinel2_red.tif")
df.show()

Example output

+--------------------------------------------------+-----+
|path                                              |tile |
+--------------------------------------------------+-----+
|/Volumes/.../nyc_sentinel2_red.tif                |{...}|
+--------------------------------------------------+-----+

Scala

val df = spark.read.format("gtiff_gdal").load("/Volumes/main/default/geobrix_samples/geobrix-examples/nyc/sentinel2/nyc_sentinel2_red.tif")

Example output

+--------------------------------------------------+-----+
|path                                              |tile |
+--------------------------------------------------+-----+
|/Volumes/.../nyc_sentinel2_red.tif                 |{...}|
+--------------------------------------------------+-----+

SQL

-- Read GeoTIFF in SQL (sample-data Volumes path)
SELECT * FROM gtiff_gdal.`/Volumes/main/default/geobrix_samples/geobrix-examples/nyc/sentinel2/nyc_sentinel2_red.tif` LIMIT 10;

Example output

+--------------------------------------------------+-----+
|path                                              |tile |
+--------------------------------------------------+-----+
|/Volumes/.../nyc_sentinel2_red.tif                |{...}|
+--------------------------------------------------+-----+

Options

The GeoTIFF reader inherits all GDAL reader options. Common options include:

Option	Default	Description
readSubdatasets	"false"	Read subdatasets if present
rasterAsGrid	"false"	Read as grid instead of tiles
retile	"false"	Retile rasters for optimal processing
tileSize	"256"	Tile size in pixels (if retiling)

Example with Options

# Read GeoTIFF with options (sample-data Volumes path)
df = spark.read.format("gtiff_gdal") \
    .option("readSubdatasets", "false") \
    .load("/Volumes/main/default/geobrix_samples/geobrix-examples/nyc/sentinel2/nyc_sentinel2_red.tif")
df.show()

Example output

+--------------------------------------------------+-----+
|path                                              |tile |
+--------------------------------------------------+-----+
|/Volumes/.../nyc_sentinel2_red.tif                |{...}|
+--------------------------------------------------+-----+

Output Schema

root
 |-- tile: struct (GeoBrix raster tile structure)
     |-- cellid: bigint (grid cell ID, nullable)
     |-- raster: binary (raster file content)
     |-- metadata: map<string,string> (driver, extension, etc.)

The tile column contains the complete raster data structure. See Tile Structure for detailed field descriptions.

Common Use Cases

Satellite Imagery

GeoTIFF is the primary format for satellite imagery:

• Sentinel-2: Multispectral Earth observation
• Landsat: Long-term Earth monitoring
• Planet: High-resolution daily imagery

Digital Elevation Models (DEMs)

Many elevation datasets are distributed as GeoTIFF:

• SRTM: 30m/90m global coverage
• ASTER GDEM: 30m global digital elevation
• LiDAR-derived: High-resolution terrain models

Aerial Photography

Orthophotos and aerial surveys commonly use GeoTIFF:

• RGB imagery
• Near-infrared (NIR) bands
• Thermal imagery

GeoTIFF vs GDAL Reader

When to use each (sample-data Volumes path):

# GeoTIFF reader (recommended for .tif files)
df = spark.read.format("gtiff_gdal").load("/Volumes/main/default/geobrix_samples/geobrix-examples/nyc/sentinel2/nyc_sentinel2_red.tif")

# GDAL reader (same result, explicit driver)
df = spark.read.format("gdal").option("driver", "GTiff").load("/Volumes/main/default/geobrix_samples/geobrix-examples/nyc/sentinel2/nyc_sentinel2_red.tif")

Use gtiff_gdal when:

• ✅ Working primarily with GeoTIFF files
• ✅ Want cleaner, more readable code
• ✅ Following GeoBrix naming conventions

Use gdal when:

• ✅ Working with multiple raster formats
• ✅ Need format-specific driver options
• ✅ Format is not a common one with named reader

Cloud-Optimized GeoTIFF (COG)

Cloud-Optimized GeoTIFFs work seamlessly with the GeoTIFF reader. Example uses sample-data path; for cloud storage use an s3://, abfs://, or gs:// path.

# COG files read like regular GeoTIFFs (sample-data path for local)
cog_df = spark.read.format("gtiff_gdal").load("/Volumes/main/default/geobrix_samples/geobrix-examples/nyc/sentinel2/nyc_sentinel2_red.tif")
# For cloud: spark.read.format("gtiff_gdal").load("s3://bucket/cog-file.tif")

Cloud-Optimized GeoTIFF

For cloud storage (S3, Azure Blob, GCS), use Cloud-Optimized GeoTIFF (COG) format for best performance. COGs enable efficient partial reads without downloading the entire file.

Compression Formats

GeoTIFF supports various compression options:

Compression	Use Case	Pros	Cons
None	Quick access	Fast read/write	Large files
LZW	General purpose	Good compression, lossless	Moderate speed
DEFLATE	General purpose	Better compression, lossless	Slower than LZW
JPEG	RGB imagery	High compression	Lossy
JPEG2000	High-quality	Very high compression	Slower

Compression

GeoBrix reads compressed GeoTIFFs transparently. The compression format is automatically detected and handled by GDAL.

Next Steps

• GDAL Reader - Generic raster reader for all GDAL formats
• RasterX Functions - Raster processing operations
• Quick Start - Get started with GeoBrix