geoanalytics 2026.1.19.1

This software is being developed at the University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

Click here for more information

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Table of Contents

• Introduction
• Recent updates
• Features
• Maintenance
• Try your first geoAnalytics program
• License
• Documentation
• Getting Help
• Discussion and Development
• Contribution to geoAnalytics
• Real-World Case Studies

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Introduction

geoAnalytics is an open-source Python-based Machine Learning library developed to discover various forms of useful information hidden in the raster data. The algorithms provided in this library cover a wide-spectrum of machine learning tasks, such as imputation, image fusion, clustering, classification, one class classification, and pattern mining. This library being platform independent can run any operating system.

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Recent Updates

• Version 2025.7.2: In this latest version, the following updates have been made:
  • Included new algorithms in patternMining, CorrelatedPatternMining, CoveragePatternMining, FaultTolerantFrequentPatternMining, FrequentPatternMining, LocalPeriodicPatternMining, PartialPeriodicFrequentPatternMining, PartialPeriodicPatternMining, PartialPeriodicPatternInMultipleTimeSeries, PeriodicCorrelatedPatternMining, RecurringPatternMining, RelativeFrequentPatternMining, and PeriodicFrequentPatternMining for identifying the patterns.
  • Included new algorithms in normalization.
  • Test cases are implemented using geoanalytics package.

Total number of algorithms: 50+

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Features

• ✅ Tested to the best of our possibility
• 🔋 Highly optimized to our best effort, light-weight, and energy-efficient
• 👀 Proper code documentation
• 🍼 Sample examples of using various algorithms at ./tests folder
• 🤖 Works with AI libraries such as TensorFlow, PyTorch, and sklearn.
• ⚡️ Supports Cuda
• 🖥️ Operating System Independence
• 🔬 Knowledge discovery in static data and streams
• 🐎 Snappy
• 🐻 Ease of use

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Maintenance

Operation	Command
Installation	pip install geoAnalytics
Upgradation	pip install --upgrade geoAnalytics
Uninstallation	pip uninstall geoAnalytics
Information	pip show geoAnalytics

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Try your first geoAnalytics program

$ python

Install the Necessary Ubuntu Packages

$ !apt update

$ !apt install -y nco cdo gdal-bin

$ !which ncrename

Output: /usr/bin/ncrename

$ !which cdo

Output: /usr/bin/cdo

$ !which gdal_translate

Output: /usr/bin/gdal_translate

Install the geoAnalytics package

!pip install -U geoanalytics

Knowing the information of geoAnalytics package

pip show geoanalytics

Output: 
        Name: geoanalytics
        Version: 2025.6.10.3
        Summary: This software is being developed at the University of Aizu, Aizu-Wakamatsu, Fukushima, Japan
        Home-page: https://github.com/UdayLab/geoanalytics
        Author: 
        Author-email: Rage Uday Kiran <[e-mail]>
        License: GPLv3
        Location: /usr/local/lib/python3.11/dist-packages
        Requires: deprecated, discord.py, fastparquet, matplotlib, mplcursors, networkx, numba, numpy, pandas, Pillow, plotly, psutil, psycopg2-binary, resource, scikit-learn, shapely, sphinx, sphinx-rtd-theme, tqdm, urllib3, validators
        Required-by: 

Download any dataset with extension of both .img and .lbl from the below link

Link --> https://data.darts.isas.jaxa.jp/pub/pds3/sln-l-mi-5-map-v3.0/

Example Direction : --> https://data.darts.isas.jaxa.jp/pub/pds3/sln-l-mi-5-map-v3.0/lon042/data/

Upload the .lbl file .img file

Operatioin 1: Raster to CSV

Step1: Import RasterToTSV package from geoAnalytics.conversion

from geoanalytics.conversion import Raster2CSV

Step2: Pass the lbl file as input and give desired outputFile name also specify the inputBand value as well as ouputBand value

converter = Raster2CSV.Raster2CSV(inputFile='MI_MAP_03_S16E035S17E036SC.lbl', outputFile='Moon.csv', startBand=1, endBand=9)

Step3: Convert the raster file into a CSV file

converter.run()

Output: 
        Processing: MI_MAP_03_S16E035S17E036SC.lbl
        Done. Output saved to: Moon.csv

Operation 2 : Clustering the Data

Step 1.1: Read the obtained CSV into a dataframe

import pandas as pd
df = pd.read_csv('Moon.csv', sep='\t')
df

x	y	1	2	3	4	5	6	7	8	9
1061317.265	-485173.607	1928	3446	3859	3913	4026	3999	4236	5245	6513
1061332.071	-485173.607	1924	3480	3876	3930	4059	3996	4243	5234	6518
1061346.877	-485173.607	1904	3476	3834	3923	4047	3992	4238	5222	6523
1061361.684	-485173.607	1874	3452	3801	3897	3959	3988	4228	5210	6518
1061376.490	-485173.607	1907	3464	3777	3868	3974	3983	4218	5198	6504
...	...	...	...	...	...	...	...	...	...	...
1091566.583	-515482.151	2090	3719	4007	3996	4117	4056	4310	5374	6633
1091581.390	-515482.151	2098	3734	4038	4020	4177	4083	4327	5389	6659
1091596.196	-515482.151	2114	3767	4046	4040	4213	4110	4332	5393	6685
1091611.002	-515482.151	2123	3813	4019	4056	4214	4136	4331	5397	6711
1091625.809	-515482.151	2125	3784	4011	4051	4184	4136	4329	5402	6737
4194304 rows × 11 columns

Step2: Import any clustering algorithm from geoanalytics.clustering

!pip install fuzzy-c-means
from geoanalytics.clustering import FuzzyCMeans
obj = FuzzyCMeans.FuzzyCMeans(dataframe=df)

Step3: Execute the run() method to cluster the data

labels, centers = obj.run(n_clusters=4)

Step4: Print the labels

labels

x	y	labels
1061317.265	-485173.607	2
1061332.071	-485173.607	2
1061346.877	-485173.607	2
1061361.684	-485173.607	2
1061376.490	-485173.607	2
...	...	...
1091566.583	-515482.151	2
1091581.390	-515482.151	0
1091596.196	-515482.151	0
1091611.002	-515482.151	0
1091625.809	-515482.151	0
4194304 rows × 3 columns

Step5: Print the centers

centers

Output: 
      array([[2098.51620472, 3762.45416178, 4099.00372289, 4146.23025903,
              4295.80971825, 4229.78337628, 4443.6879668 , 5471.85298977,
              6790.96720993],
             [2274.54605523, 4040.75198398, 4374.89922669, 4395.57058102,
              4538.50885801, 4454.45212911, 4677.95182781, 5739.96854105,
              7095.06428559],
             [1984.82196498, 3578.0340764 , 3908.79548852, 3961.66894715,
              4108.90409919, 4049.30456217, 4255.81099407, 5258.24055464,
              6549.13133772],
             [1896.71176664, 3421.42972113, 3741.56611478, 3802.88625245,
              3946.94784255, 3896.3217178 , 4093.18671711, 5066.07543066,
              6327.35005223]])

Step6: To know the run time and memory consumption of Algorithm, print the statistical values

obj.getRuntime()
obj.getMemoryRSS()
obj.getMemoryUSS()

Output: 
      Total Execution time of proposed Algorithm: 407.7022657394409 seconds
      Memory (RSS) of proposed Algorithm in KB: 1655512.0
      Memory (USS) of proposed Algorithm in KB: 1634900.0

Step7: Save the labels and centers

obj.save(outputFileLabels='FuzzyCMeansLabels.csv', outputFileCenters='FuzzyCMeansCenters.csv')

Output: 
      Labels saved to: FuzzyCMeansLabels.csv
      Cluster centers saved to: FuzzyCMeansCenters.csv

Operation3: CSV to Raster

Step1: Import CSV2Raster from geoanalytics.conversion

from geoanalytics.conversion import CSV2Raster as CSV2Raster

Step2: Pass the dataFrame and give desired outputFile name

process = CSV2Raster.CSV2Raster(dataframe=labels,outputFile='FuzzyCMeans.tiff')

Step3: Execute the run() method

process.run()

Output: 
      (0, '')
      (139, 'Segmentation fault (core dumped)')
      (0, '')
      (0, 'Input file size is 2048, 2048\n0...10...20...30...40...50...60...70...80...90...100 - done.')
      (0, '')

Operation 4: Visualization

Step1: Import TiffViewer from geoanalytics.visualization

pip install rasterio
from geoanalytics.visualization import TiffViewer

Step2: Input the RasterFile or pass it into the parameter

viewer = TiffViewer.TiffViewer(inputFile='FuzzyCMeans.tiff')

Step3: Display the image with desired scaling map

1. gray scale

viewer.run(cmap='gray', title='TIFF Image')

2. Jet Scale

viewer.run(cmap='jet', title='TIFF Image')

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License

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Documentation

The official documentation is hosted on geoAnalytics.

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Getting Help

For any queries, the best place to go to is Github Issues Github Issues.

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Discussion and Development

In our GitHub repository, the primary platform for discussing development-related matters is the university lab. We encourage our team members and contributors to utilize this platform for a wide range of discussions, including bug reports, feature requests, design decisions, and implementation details.

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Contribution to geoAnalytics

We invite and encourage all community members to contribute, report bugs, fix bugs, enhance documentation, propose improvements, and share their creative ideas.

Real World Case Studies

1. Lunar data analytics

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