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- Introduction
- Recent updates
- Features
- Maintenance
- Try your first geoAnalytics program
- License
- Documentation
- Getting Help
- Discussion and Development
- Contribution to geoAnalytics
- Real-World Case Studies
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.
- 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+
- ✅ 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
| Operation | Command |
|---|---|
| Installation | pip install geoAnalytics |
| Upgradation | pip install --upgrade geoAnalytics |
| Uninstallation | pip uninstall geoAnalytics |
| Information | pip show geoAnalytics |
$ python$ !apt update$ !apt install -y nco cdo gdal-bin$ !which ncrenameOutput: /usr/bin/ncrename
$ !which cdoOutput: /usr/bin/cdo
$ !which gdal_translateOutput: /usr/bin/gdal_translate
!pip install -U geoanalyticspip show geoanalyticsOutput:
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 <[email protected]>
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:
Example Direction : --> https://data.darts.isas.jaxa.jp/pub/pds3/sln-l-mi-5-map-v3.0/lon042/data/
from geoanalytics.conversion import Raster2CSVStep2: 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)converter.run()Output:
Processing: MI_MAP_03_S16E035S17E036SC.lbl
Done. Output saved to: Moon.csv
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 |
!pip install fuzzy-c-means
from geoanalytics.clustering import FuzzyCMeans
obj = FuzzyCMeans.FuzzyCMeans(dataframe=df)labels, centers = obj.run(n_clusters=4)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 |
centersOutput:
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]])
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
obj.save(outputFileLabels='FuzzyCMeansLabels.csv', outputFileCenters='FuzzyCMeansCenters.csv')Output:
Labels saved to: FuzzyCMeansLabels.csv
Cluster centers saved to: FuzzyCMeansCenters.csv
from geoanalytics.conversion import CSV2Raster as CSV2Rasterprocess = CSV2Raster.CSV2Raster(dataframe=labels,outputFile='FuzzyCMeans.tiff')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, '')
pip install rasterio
from geoanalytics.visualization import TiffViewerviewer = TiffViewer.TiffViewer(inputFile='FuzzyCMeans.tiff')viewer.run(cmap='gray', title='TIFF Image')
viewer.run(cmap='jet', title='TIFF Image')
The official documentation is hosted on geoAnalytics.
For any queries, the best place to go to is Github Issues Github Issues.
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.
We invite and encourage all community members to contribute, report bugs, fix bugs, enhance documentation, propose improvements, and share their creative ideas.
- Lunar data analytics
