["SentinelAI" PROJECT PROPOSAL]

## Project Request

"SentinelAI: Harnessing Satellite Vision for Rapid Disaster Response and Life-saving Insights"

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| Field | Computer Vision , DeepLearning |
| ------ | --------------------------------- |
| About | Predicting High-Impact Areas for Efficient Disaster Response using CNN, GAN, and SVM |
| Github | prithubanik |
| Email | prithubanik123@gmail.com |
| Label | Project Request |

https://github.com/prithubanik

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**Define You**

- [x] GSSOC Participant
- [ Prithu Banik ] Contributor

# Project Name

**SentinelAI**

## Description
**SentinelAI** is an innovative project that combines the power of Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Support Vector Machines (SVM) to predict and prioritize the most heavily impacted areas during disasters. By leveraging advanced machine learning techniques, SentinelAI aims to optimize rescue efforts and save lives by accurately identifying areas requiring urgent attention.

## Scope

[**Objectives:** The project aims to develop and implement a CNN-based model to analyze satellite imagery and predict heavily impacted areas during natural disasters, facilitating efficient rescue operations and resource allocation.

**Deliverables:** The project will deliver a trained CNN model capable of identifying and classifying areas with severe damage or potential hazards, along with an integrated GAN and SVM model for data augmentation and prioritization. A user-friendly interface or API will also be provided for real-time input and prediction.

**Constraints**: The project's constraints include limited availability of labeled satellite imagery for training, potential variations in disaster scenarios, and computational resource limitations for model training and inference. Time constraints and the need for continuous model updates to adapt to evolving disaster situations are also considered.]

## Timeline

[Start date : the assigned date
End date: August 10 2023]