Big Data-Driven Satellite Image Segmentation for Urban Planning and Environmental Analysis

This project focuses on leveraging Big Data and advanced image segmentation techniques to support efficient urban planning and environmental monitoring. With the rapid growth of cities, traditional methods of analyzing land use and environmental changes are often slow and less accurate. This project addresses these challenges by utilizing large-scale satellite data and intelligent algorithms.

The system collects high-resolution satellite imagery and processes it using data preprocessing techniques such as noise reduction, normalization, and enhancement. Then, deep learning models (such as Convolutional Neural Networks - CNNs) are applied to perform image segmentation, which divides the image into meaningful regions like buildings, roads, vegetation, and water bodies.

By integrating Big Data technologies, the system can handle massive datasets efficiently, enabling real-time or near real-time analysis. The segmented outputs are used to identify patterns such as urban expansion, deforestation, land-use changes, and environmental degradation.

The project provides valuable insights for:

Urban planners to design sustainable cities
Environmental analysts to monitor ecological changes
Government authorities for decision-making and policy planning

Overall, this project demonstrates how the combination of Big Data analytics and machine learning can significantly improve the accuracy, speed, and scalability of urban and environmental analysis systems.(23149)(22976)

Видео Big Data-Driven Satellite Image Segmentation for Urban Planning and Environmental Analysis канала Katikapally Dastagiramma