Minor updates

README.md

@@ -2,7 +2,9 @@
 
 ## Overview
 
-**SafeBridge** is a light-weight open-source tool for the Python programming language that calculates bridge damage indicators with low latency by combining remotely sensed and processed data (Persistent Scatterer InSAR time series) with algorithms to process topologies in the geospatial domain.
+**SafeBridge** is a light-weight open-source tool for the Python programming language that estimates bridge damage indicators with low latency by combining remotely sensed and processed data (Multi-Temporal InSAR time series) with algorithms to process topologies in the geospatial domain.
+
+For furher information and underlying research, you can check the paper from [here](https://doi.org/10.58286/29731)
 
 ## Installation
 
@@ -45,6 +47,10 @@ python tutorial.py
 
 If everything works correctly, you should see a folder called **`SafeBridgeDB`** inside the example folder. Inside that folder, you will find the generated **PDF** report for the entire dataset provided.
 
-## Acknowledgements & Funding
+## Disclaimer
+
+The toy MT-InSAR data presented in the repository and used for the tutorial is a synthetic one and has no relation to real data. It was generated with the intent to present how the `SafeBridge` is working.
+
+## Acknowledgements
 
 This work is supported by Vidi project InStruct, project number 18912, financed by the Dutch Research Council (NWO).

docs/source/index.rst

@@ -11,7 +11,7 @@ Welcome to SafeBridge's documentation!
    :align: left
    :alt: SafeBridge Logo
 
-**SafeBridge** is an open-source Python package to calculate the damage indicators for bridges, utilising Persistent Scatter InSAR (PSInSAR) 
+**SafeBridge** is an open-source Python package for estimating damage indicators for bridges, utilising Multi-Temporal InSAR (MT-InSAR) 
 time-series data and geospatial operations to leverage bridge topologies within a Geographical Information System (GIS) environment. 
 **SafeBridge** provides an efficient way to derive the damage indicators for bridges promptly in the era of big data.
 
@@ -37,9 +37,9 @@ Background & Motivation
 The growing use of satellite-based monitoring, particularly time-series data from MT-InSAR, has led to a significant increase in data volume and complexity, 
 posing challenges for researchers and engineers in terms of storage, processing, and analysis. Handling such large datasets often requires access to high-performance 
 computing (HPC) systems; however, access to these resources can be limited by institutional constraints, cost, or technical barriers. As an alternative, many professionals 
-rely on in-house computing resources—laptops, desktops, or workstations—which demand careful optimisation and the use of efficient, open-source tools to manage processing 
+rely on in-house computing resources such as laptops, desktops, or workstations, which demand careful optimisation and the use of efficient, open-source tools to manage processing 
 loads. Yet, selecting and effectively using these tools requires expertise across multiple domains, including geospatial analysis, remote sensing, and data science. This is 
-where `SafeBridge` offers a practical solution—by providing a streamlined, tailored approach for analysing Persistent Scatterer InSAR time-series data, it simplifies the complex 
+where `SafeBridge` offers a practical solution—by providing a streamlined, tailored approach for analysing Multi-Temporal InSAR time-series data, it simplifies the complex 
 workflow of bridge damage detection, enabling more users to extract meaningful insights from satellite observations without needing advanced computation infrastructures.