Building Ffootprint Segmentation

Overview

This project is aimed at performing building footprint segmentation using semantic segmentation technique. The segmentation task involves identifying and segmenting building footprints from satellite or aerial imagery.

Table of Contents

1. Introduction
2. Installation
3. Technologies
4. Demo

Introduction

Building footprint segmentation can be utilized in various applications such as urban planning, disaster response, and environmental monitoring. This project utilizes the UNet architecture, a popular convolutional neural network (CNN) architecture for image segmentation tasks, combined with semantic segmentation techniques to accurately detect building footprints.

Installation

1. Normal Installation:

   git clone https://github.com/pranzalkhadkaBuilding_footprint_segmentation.git
   cd Building_footprint_segmentation
   python3 -m venv venv
   source venv/bin/activate
   pip install requirements.txt
   train.py
   uvicorn app:app --reload
   

2. Docker Installation:

   git clone https://github.com/pranzalkhadkaBuilding_footprint_segmentation.git
   cd Building_footprint_segmentation
   docker build -t some_name .
   docker run -p 8000:8000 name_you_used_above
   Access the application at http://localhost:8000 in your browser
   
   

Technologies

1. Python
2. Tensorflow
3. Keras
4. FastAPI
5. Docker
6. MLflow

Demo