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Geo-spatial utilities for Remote Sensing and GIS application

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Geospatial Toolset

Flowers

Welcome to my repository of geospatial data science projects! This collection houses a variety of my data science and geospatial notebooks and python scripts, which serve as a testament to my proficiency and knowledge in this field. Each project showcases various facets of data analysis, machine learning, and visualization techniques.

tags: python, data-science, jupyter-notebook, ipython-notebook, exploratory-analysis, geospatial, machine-learning, deep-learning

Projects

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It uses Yahoo Finance stock data to forecast stock prices using ARIMA and SARIMAX model. The EDA analysis and step-by-step implementation of these two models presented in Jupyter Notebook

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Several Python scripts written using arcpy module for the purpose of learning. It helps us the use of arcpy module. An add-in button example also added to this repositary.

geoutils-rsg

Geospatial Utilities for Remote Sensing and GIS Application.

This repository has several scripts written in Python for Remote Sensing and GIS data analysis and workflow automation. This is an early development toolset, and is suitable for geospatial data analysis. Features and implementation are subject to change.

Introduction

The geoutils-rsg Python package aims to simplify geospatial tasks and automate geospatial application development. This library provides a comprehensive set of tools for Remote Sensing and GIS. By addressing the gaps in geospatial data processing tools and offering automation capabilities, this package proves to be an essential resource. Many of the tools are derived from scientific research articles, while others consist of efficient algorithms that enable streamlined processing with minimal code. With these tools, users or researchers can focus on analyzing their application results rather than spending time on coding or starting from scratch.

Features

Raster Functionalities

Functions or Classes Descriptions
clip_raster_by_extent() Clip Raster by Extent
raster_to_point() Convert Rater Pixel to Point Shapefile
dn_to_radiance() Convert Pixel DN Values to Radiance
extract_lulc_class() Export Individual LULC Class
get_border_pixel_values() Extract Border Pixel Values
group_raster_pixels() Group Homogeneous Pixel Values
find_sinknflat_dem() Identify Sink/Flat Pixels in DEM
radiance2degree_celsious_temperature() Convert Radiance to Degree-Celsious Temperature
regular_shift_raster() Shift Raster in Different Direction
mosaic_raster() Mosaic GeoTIFF Tiles

Vector Functionalities

Functions or Classes Descriptions
get_cumulative_drainage_area() Calculate Cumulative Drainage Area
generate_river_xscl() Create Cross-Section Line of River
generate_grid_boundary() Generate Grid Boundary from Point
get_nearest_point() Find Closest Point
LineDirectionError() Check River Network's Line Direction
GenerateHydroID() Generate HydroID of River Network
CreateGroupID() Generate GroupID of River Network
CreateObjectID() Generate ObjectID of River Network
FnTnodeID() Generate From-Node and To-Node ID of River Network
wkt_to_gdf() Convert WKT to Geo-DataFrame
extract_overlap_polygon() Extract Overlap Polygon Geometry
merge_shapefiles() Merge Vector Files

Tools for Application

Functions or Classes Descriptions
generate_shoreline_raster() Shoreline Extraction
generate_morphometric_parameters() Morphometric Analysis for Prioritizing Sub-watershed and Management Using Geospatial Technique
shoreline_change_analysis() Digital Shoreline Change-Rate Analysis (example: notebooks/shoreline_change_rate.ipynb)

Installation

pip install git+https://github.com/dghorai/geoutils-rsg

Contributing

We welcome contributions! If you wish to contribute to this repository, kindly adhere to the guidelines provided below. We greatly appreciate any enhancements, bug fixes, or additional projects.

  • Fork the repository to your GitHub account
  • Establish a fresh branch for your modifications or contributions
  • Implement your alterations, improvements, or repairs in your branch
  • Verify your modifications to guarantee they do not cause any problems
  • Record your modifications with a concise and detailed commit message
  • Upload/Push your modifications to your forked repository
  • Finally, submit a pull request to the main repository

Reference Data Science Projects

Other References