The complete research work is distributed in 3 phases. First phase is responsible for spatial and non-spatial data collection and field survey using hand held GPS device. Second phase is related to image registration, data processing and geodatabase preparation and geometrical validation. In this phase Deep learning and python module have been integrated to the GIS environment for performing the automatic feature extraction from multi spectral images and creation of spatial features such as points, lines, polygons and annotations. In the third phase ArcGIS python API, ArcGIS online and ArcGIS Pro have been incorporated with the Anaconda Jupyter notebook (a cloud-based python IDE) for publishing, storing and retrieving spatial data from the cloud. A virtual organization has been created for performing the work related to second phase. All the spatial data and geodatabase prepared in the second phase using AI and manual techniques has been stored to the created organization. The whole of the data and module is being stored in the container of Docker (A cloud-based storage technique).

1. Development of Village level Geospatial Framework
for “Digital India”
Presented
By
Dinesh Kumar Azad
Reg. No. – 2014RGI01
(Ph.D. 10th Semester)
GIS Cell
Motilal Nehru National Institute of Technology
Supervised
By
Dr. A. K. Singh
Professor & Head CED,
Member GIS Cell
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2. Outlines
1. Development of Geospatial Framework
2. Methodology
3. Working with ArcGIS Python API
4. Result
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3. Geospatial Framework
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4. Methodology
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5. Sign Up for ArcGIS
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6. Creation of New User Account
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7. Set Up for User Organization
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8. Downloading and Installation of ArcGIS Apps and ArcGIS Pro
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9. Management of Licenses and Extensions
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10. Downloading and Installation of Anaconda
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11. Configuring Anaconda for ArcGIS Desktop 10.6
1. Add Anaconda to Windows system PATH
Control Panel - System and Security - System - Advanced system settings - Advanced Tab - Environment
Variables - System variables - Double click Path variable - New - Add C:Anaconda3Scripts to Path
variable - OK
2. Find the versions of numpy and matplotlib used by ArcGIS 10.6
o Start - ArcGIS - ArcMap - Python Windows
>>> import sys, numpy, matplotlib
>>> print(sys.version, numpy.__version__, matplotlib.__version__)
(‘2.7.14 (v2.7.14:84471935ed, Sep 6 2017, 20:19:30) [MSC v.1500 32 bit (Intel)]’, ‘1.9.3’, ‘1.5.2’)
3. Create an Anaconda environment that is compatible with ArcGIS 10.6
o Start - All apps – Anaconda3 (32-bit) - Anaconda Prompt - Run as Administrator
o conda create -n arc106 python=2.7.14 numpy=1.9.3 matplotlib=1.5.2 pyparsing xlrd xlwt
pandas scipy ipython ipython-notebook ipython-qtconsole
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12. Configuring ArcGIS 10.6 to work with Anaconda
1. Anaconda Python to ArcPy
o Copy the Desktop10.6.pth file to the Anaconda environment site-packages folder:
o From: C:Python27ArcGIS10.6Libsite-packagesDesktop10.6.pth
o To: C:Anaconda3envsarc106Libsite-packagesDesktop10.6.pth
2. Arcpy to Anaconda Python
o Create a zconda.pth (path) file with the content “C:Anacondaenvsarc104libsite-packages” in it.
o Then copy zconda.pth to C:Python27ArcGIS10.6Libsite-packages
3. Testing in ArcMap
o Start - All apps – Anaconda3 (32-bit) - Anaconda Prompt - Run as Administrator
o conda activate arcgis106
o pip install archook
o jupyter notebook , or
o Start - All apps - Anaconda3 (32-bit) - Jupyter Notebook (arcgis106)
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13. Configuring ArcGIS Python API to work with ArcGIS
1. Start - All apps – Anaconda3 (32-bit) - Anaconda Prompt - Run as Administrator
2. conda install -c esri arcgis
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14. Launching Jupyter Notebook and Publishing the Map
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15. Displaying the Layers
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16. Exporting Training Data or Deep Learning
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17. Exporting Training Data or Deep Learning
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18. Exported Training Data
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19. Exported Training Data: Ponds
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20. Thank you