Update FAQ.md

docs/FAQ.md

@@ -9,13 +9,13 @@ A : fAIr is an open-source toolkit developed by the Humanitarian OpenStreetMap T
 
 **Q : How can I use fAIr in my mapping projects?**
 
-A : To use fAIr, you can follow the installation instructions provided in the documentation. Once installed, you can utilize the pre-trained AI models for tasks such as road network extraction, building footprint detection, or land use classification. The documentation offers examples and guidelines to help you get started.
+A : Building for now but to use fAIr, you can follow the installation instructions provided in the documentation. Once installed, you can utilize the pre-trained AI models for tasks such as road network extraction, building footprint detection, or land use classification. The documentation offers examples and guidelines to help you get started.
 
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 **Q : What types of data can I use with fAIr?**
 
-A : fAIr primarily works with satellite imagery and aerial photographs. You can use various types of imagery sources, including public satellite imagery datasets, to perform mapping tasks using the AI models provided. The documentation provides information on the compatible data formats and recommended data sources.
+A : fAIr primarily works with satellite imagery and aerial photographs. You can use various types of imagery sources which compiles with opensource imagery license to perform mapping tasks using the AI models provided. The documentation provides information on the compatible data formats and recommended data sources.
 
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@@ -39,7 +39,7 @@ A : You can contribute to the improvement of fAIr by providing feedback, reporti
 
 **Q : Can I integrate fAIr with other mapping tools or platforms?**
 
-A : Yes, fAIr is designed to be modular and can be integrated into existing mapping workflows and platforms. It provides interoperability with tools like OpenStreetMap, QGIS, and ArcGIS. You can leverage the AI models from fAIr within these tools or use the outputs of fAIr in combination with other mapping datasets and tools.
+A : Yes, fAIr is designed to be modular and can be integrated into existing mapping workflows and platforms. It provides modular pip packages/ API endpoints that can be used in other scripts and can be integrated as plugins on different platforms such as Qgis, Josm etc. You can leverage the AI models from fAIr within these tools or use the outputs of fAIr in combination with other mapping datasets and tools.
 
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@@ -53,7 +53,7 @@ A : You can contribute to fAIr's development by following the guidelines provide
 
 **Q : What programming languages and frameworks are used in fAIr?**
 
-A : fAIr frontend is made with React and backend with Django.fAIr is primarily developed using Python, which is widely used in machine learning and AI. It leverages popular frameworks such as PyTorch and TensorFlow for model development and training. Familiarity with Python and these frameworks will be beneficial if you plan to work on fAIr's development.
+A : fAIr frontend is made with React and backend with Django.fAIr is primarily developed using Python, which is widely used in machine learning and AI. It leverages popular framework TensorFlow for model development and training. Familiarity with Python and these frameworks will be beneficial if you plan to work on fAIr's development.
 
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@@ -65,7 +65,7 @@ A : Yes, fAIr follows coding standards and guidelines outlined in the project's
 
 **Q : Can I propose new AI models or enhancements to existing models?**
 
-A : Absolutely! The fAIr project welcomes contributions related to new AI models or enhancements to existing models. If you have developed a new model that aligns with the project's goals or if you have ideas to improve the current models, you can propose them through the GitHub repository and engage in discussions with the community or you can create them at [here](https://fair-dev.hotosm.org/ai-models) .
+A : Absolutely! The fAIr project welcomes contributions related to new AI models or enhancements to existing models. If you have developed a new model that aligns with the project's goals or if you have ideas to improve the current models, you can propose them through the GitHub repository and engage in discussions with the community . Once approved community will be able go use it . 
 
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