rohanmistry231/Geodata-Processing-using-Python-and-Machine-Learning
A Python-based project for processing and analyzing geospatial data using machine learning techniques, leveraging libraries like GeoPandas, Scikit-learn, and Folium. Includes examples for spatial analysis, visualization, and predictive modeling with real-world geographic datasets.
This project helps environmental scientists, urban planners, or geographers analyze and visualize satellite imagery and other geospatial data to understand geographical patterns. You input raw satellite images, land use/land cover data, or other geographical datasets, and it outputs maps, classifications (like water bodies or land use), and predictive models of spatial trends. Anyone who needs to extract insights from location-based information will find this useful.
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Use this if you are a GIS analyst or researcher looking for a practical, code-based approach to perform spatial analysis, create sophisticated maps, or build predictive models from various geographic data types.
Not ideal if you prefer using commercial GIS software with a graphical user interface and do not want to write code.
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MIT
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May 23, 2025
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