In this repository, we will learn about GIS analysis, visualization, data management, and more, including QGIS, ArcGIS, Python, R, etc.
- Introduction
- Technologies Used
- Getting Started
- Project Structure
- Tutorials and Examples
- Datasets
- GIS Analysis
- Visualization
- Remote Sensing
- Contributing
- License
- Contact
Welcome to the ALL in One GIS Project repository! This repository aims to provide a comprehensive resource for GIS enthusiasts and professionals. Whether you're new to GIS or looking to expand your skills, you'll find valuable resources here.
- QGIS: An open-source Geographic Information System.
- ArcGIS: A proprietary GIS software developed by Esri.
- Python: Programming language for automation and data analysis.
- R: A programming language for statistical computing and graphics.
Before you begin, ensure you have the following software installed:
- QGIS
- ArcGIS
- Python (with necessary libraries such as geopandas, matplotlib, etc.)
- R (with necessary packages such as sf, ggplot2, etc.)
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Clone the repository:
git clone https://github.com/Laudarisd/GIS-Project.git
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Navigate to the project directory:
cd GIS-Project -
Install Python dependencies:
pip install -r requirements.txt
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Install R dependencies:
Follow the instructions in the
R/README.mdfile.
- Basic GIS operations
- Spatial analysis
- Customizing QGIS
- Getting started with ArcGIS
- Advanced GIS techniques
- Using ModelBuilder
- Automating GIS tasks
- Spatial data analysis with Geopandas
- Visualization with Matplotlib
- Spatial data manipulation
- Advanced statistical analysis
- Plotting maps with ggplot2
- List of datasets used in this project
- How to contribute new datasets
- Dataset - UAVs
Detailed documentation and examples of various GIS analysis techniques.
Examples and tutorials on visualizing GIS data.
In this section, we cover remote sensing techniques and applications, including:
- Introduction to remote sensing
- Satellite image processing
- Applications of remote sensing in GIS
- Tools and software for remote sensing analysis
Visualizing, processing, analyzing, and managing imagery and raster data are an important part of GIS. Information contained in typical land base maps and GIS layers is derived from imagery and raster data. Existing maps and layers are revised based on updated imagery.
ArcGIS includes capabilities for visualizing, managing, processing, and analyzing imagery and raster data. These imagery and remote sensing capabilities are extended in the more advanced image analysis capabilities in the ArcGIS Image Analyst extension.
Images are raster data that represent measurements of reflected or emitted electromagnetic energy captured by a sensor on a drone, airplane, or satellite. Other types of imagery and raster data include scientific measurements of a location's properties, such as temperature and salinity at different water depths, elevation models, and seismic surveys.
Remote sensing extracts meaningful information from imagery. It applies image processing techniques to identify and extract various types of information about features of interest, such as vegetation type and health, type of urban development, and identifying and analyzing trends in detected objects and phenomena. Proximity and neighborhood analysis, seasonality, physical and cultural geography, vegetation growth phenomenology, climate, and other physical and environmental factors are considered when defining the type and timing of image acquisition. Remote sensing is the embodiment of The Science of Where™.
The tools, functions, and capabilities in ArcGIS extract spatial and spectral information from imagery using scientific and image processing methodologies. While the processes are standardized and optimized using advanced image processing techniques, workflows and results are often unique based on scientific remote sensing techniques. For example, the relative health of specific vegetation species can be monitored and analyzed over a growth cycle.
Imagery and remote sensing provide functions, tools, and capabilities to perform the following:
- Correct, calibrate, and standardize imagery for integration into workflows
- Photogrammetric correction of remotely sensed imagery from drones, aircraft, and satellites
- Image interpretation, processing, and exploitation
- Multispectral and multidimensional raster analysis and processing
- Create information products from imagery
- Advanced raster and image analysis workflows for machine learning and feature extraction
ArcGIS Pro provides a collection of tools to manage and serve large collections of imagery. The core components of image management are mosaic datasets and raster products. Traditionally, a mosaic is created when you combine a collection of images to create one seamless image. The mosaic dataset does that, but it also uses image attributes, such as acquisition date, cloud cover, or spatial resolution, to help you manage a collection of imagery, even one composed of overlapping images or images with varying spatial resolutions. Instead of creating files when you want to change which images are displayed, or enhance the images, the mosaic dataset processes the imagery in-memory, allowing it to quickly display an image of interest, regardless of the size of the collection.
This explanation is from ArcGIS page. Source
We welcome contributions! Please read our contributing guidelines for more details.
This project is licensed under the MIT License - see the LICENSE file for details.
If you have any questions, feel free to reach out:
- Name:
- Email:
- GitHub: