"""Home page for Streamlit app.""" import streamlit as st from src.config_parameters import config from src.utils_layout import add_about, add_logo, toggle_menu_button # Page configuration st.set_page_config(layout="wide", page_title=config["browser_title"]) # If app is deployed hide menu button toggle_menu_button() # Create sidebar add_logo("app/img/MA-logo.png") add_about() # Set fontisize text st.markdown( """ """ % config["docs_fontsize"], unsafe_allow_html=True, ) # Page title st.markdown("# Home") # First section st.markdown("## Introduction") st.markdown( """ This tool allows to estimate flood extent using Sentinel-1 synthetic-aperture radar SAR data.

The methodology is based on a recommended practice published by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER) and it uses several satellite imagery datasets to produce the final output. The datasets are retrieved from Google Earth Engine which is a powerful web-platform for cloud-based processing of remote sensing data on large scales. More information on the methodology is given in the Description.

This analysis provides a comprehensive overview of a flooding event, across different areas of interest, from settlements to countries. However, as mentioned in the UN-SPIDER website, the methodology is meant for broad information provision in a global context, and contains inherent uncertainties. Therefore, it is important that the tool is not used as the only source of information for rescue response planning. """ % ( config["url_sentinel_esa"], config["url_unspider_tutorial"], config["url_gee"], ), unsafe_allow_html=True, ) # Second section st.markdown("## How to use the tool") st.markdown( """

In case you get errors, follow the intructions. If you have doubts, feel free to contact the Data Science team.

""", unsafe_allow_html=True, )