rasteric 2.2.0

A Python geospatial library for raster preprocessing, analysis, visualization, and modeling

Rasteric

Rasteric is a Python geospatial library designed for raster preprocessing, analysis, visualization, and modeling. It simplifies common GIS workflows with easy-to-use functions built on top of Rasterio and GeoPandas.

Installation

pip install rasteric

Dependencies are installed automatically:

• rasterio, geopandas, shapely
• numpy, pandas, matplotlib, scipy
• scikit-learn, scikit-image
• rasterstats, Pillow

Requirements

• Python >= 3.8

Supported Data Formats

• Raster: GeoTIFF, TIFF
• Vector: Shapefiles, GeoJSON
• Tabular: CSV with spatial attributes (latitude/longitude)

Typical Use Cases

• Satellite image preprocessing (Sentinel-2, Landsat)
• NDVI and vegetation analysis
• Land-use / land-cover mapping
• Agricultural monitoring
• Raster-vector data extraction
• Geospatial machine learning data preparation
• Time-series crop monitoring
• Change detection
• Pan-sharpening (spatial resolution enhancement)

Quick Start

from rasteric import (plot, clip, ndvi, evi, stack, extract, mosaic, calc,
                      change_detect, timeseries, cloudmask, classify, sample)

# Visualize
plot('sentinel2.tif', bands=(4, 3, 2), brightness_factor=4)

# Vegetation indices
ndvi('sentinel2.tif', 'ndvi.tif', red_band=3, nir_band=4)
evi('sentinel2.tif', 'evi.tif', nir_band=4, red_band=3, blue_band=2)

# Mosaic tiles
mosaic('tiles/', 'merged.tif')

# Band math
calc('(B8 - B4) / (B8 + B4)', inputs={'B8': 'nir.tif', 'B4': 'red.tif'}, output='ndvi.tif')

# Change detection
result = change_detect('ndvi_jan.tif', 'ndvi_jul.tif', 'change.tif', threshold=0.2)

# Pan-sharpen: boost RGB from 5m to 1m using panchromatic band
rasteric.pansharpen('rgb_5m.tif', 'pan_1m.tif', 'rgb_1m.tif', method='gram-schmidt')

# Time-series
df = timeseries('ndvi_folder/', output_csv='ndvi_ts.csv')

# Cloud masking (Sentinel-2)
cloudmask('sentinel2.tif', 'cloud_free.tif', method='scl')

# Classification
classify('image.tif', 'classes.tif', method='kmeans', n_clusters=5)

# ML training data
df = sample('sentinel2.tif', 'training.shp', 'samples.csv', label_column='class')

Functions

Vegetation Indices

Function	Description
ndvi(raster, out, red, nir)	Normalized Difference Vegetation Index
evi(raster, out, nir, red, blue)	Enhanced Vegetation Index
savi(raster, out, nir, red, L)	Soil-Adjusted Vegetation Index
ndwi(raster, out, green, swir)	Normalized Difference Water Index
gndvi(raster, out, nir, green)	Green NDVI (chlorophyll sensitivity)
ndmi(raster, out, nir, swir)	Normalized Difference Moisture Index

Data Handling

Function	Description
stack(folder, out)	Stack multiple rasters into multi-band file
mosaic(folder, out, method)	Merge raster tiles into one raster
mergecsv(path, out)	Merge CSV files in a directory
clip(raster, shp, out)	Clip raster by shapefile
reproject(raster, out, crs)	Reproject to different CRS
resample(raster, out, factor)	Resample to new resolution

Raster Calculator

Function	Description
calc(expr, inputs, out)	Band math with named inputs
bandmath(raster, equation, out)	Band math with B1, B2, ...

Analysis

Function	Description
zonalstats(raster, vector, stats)	Zonal statistics for polygons
stats(raster)	Basic statistics (min, max, mean, std)
fillnodata(raster, out)	Fill NoData via interpolation

Time-Series & Change Detection

Function	Description
timeseries(folder, out_csv, band, stat)	Analyze raster time-series
change_detect(img1, img2, out, method, threshold)	Detect changes between dates

Cloud Masking

Function	Description
cloudmask(raster, out, method)	Sentinel-2 cloud masking (QA60 or SCL)

Image Classification

Function	Description
classify(raster, out, method, n_clusters)	K-Means or Random Forest classification
texture(raster, out, band, properties)	GLCM texture features

ML Preparation

Function	Description
sample(raster, shp, out_csv, label)	Extract training samples from raster
train_test_split(csv, test_size)	Split samples into train/test sets

Extraction

Function	Description
extract(data, shp, out_csv)	Extract raster values at vector locations
align_to_shp(tif, shp, out)	Reproject raster to match shapefile CRS

Visualization

Function	Description
plot(file, bands, cmap, title, ax, brightness)	Display raster with band selection
contour(file)	Overlay contour lines
hist(file, bin, title)	Histogram of pixel values
thumbnail(raster, out_png, bands, size)	Quick PNG thumbnail

Conversion

Function	Description
convras(raster, out_shp)	Raster to vector polygons

Pan-sharpening

Function	Description
pansharpen(ms_raster, pan_raster, output, method)	Increase MS resolution using pan band

import rasteric

# Pan-sharpen RGB (5m) using panchromatic (1m) → output 1m
rasteric.pansharpen('rgb_5m.tif', 'pan_1m.tif', 'rgb_1m.tif', method='gram-schmidt')

Methods:

• brovey — Brovey Transform, sharpest spatial detail
• ihs — IHS (Intensity-Hue-Saturation), good color preservation
• gram-schmidt — best spectral integrity (recommended for classification)
• simple_mean — simple average fallback

Utilities

Function	Description
convpath(path)	Cross-platform path conversion
bandnames(raster, names)	Update band descriptions
haversine(lon1, lat1, lon2, lat2)	Great-circle distance

Sentinel-2

from rasteric import process_sen2

# Full pipeline: JP2→TIFF, resample, stack, cloud removal
process_sen2('source/', 'output/')

Contributing

• Repository: github.com/tnmthai/rasteric
• Issues: github.com/tnmthai/rasteric/issues
• PyPI: pypi.org/project/rasteric

License

MIT License