Merge branch 'feature/feedback_v1' of https://github.com/hotosm/fAIr …

…into feature/feedback_v1

Readme.md

@@ -28,5 +28,4 @@ See below a suggested product roadmap [subject to change] that provides high-lev
 
 ## Local Installation [DEV]
 
-Checkout Docker Installation [docs](./docs/Docker-installation.md)## Local Installation [DEV]
-
+Checkout Docker Installation [docs](./docs/Docker-installation.md)

docs/Docker-installation.md

@@ -56,6 +56,13 @@ Docker Compose is created with redis , worker , postgis database ,  api and fron
         ```
         eg : export RAMP_HOME=/home/kshitij/ramp
 
+    - Export ```TRAINING_WORKSPACE``` Env
+        Training workspace is the folder where fAIr will store its training files 
+        for eg :
+        ```
+        export TRAINING_WORKSPACE=/home/kshitij/hotosm/fAIr/trainings
+        ```
+
 5. Register your Local setup to OSM 
 
     - Go to [OpenStreetMap](https://www.openstreetmap.org/) , Login/Create Account
@@ -73,12 +80,7 @@ Docker Compose is created with redis , worker , postgis database ,  api and fron
     - Fill out the details of ```OSM_CLIENT_ID``` &```OSM_CLIENT_SECRET``` in .env file and generate a unique key & paste it to ```OSM_SECRET_KEY``` (It can be random for dev setup)
     
         Leave rest of the items as it is unless you know what you are doing
-    - Export ```TRAINING_WORKSPACE``` Env
-        Training workspace is the folder where fAIr will store its training files 
-        for eg :
-        ```
-        export TRAINING_WORKSPACE=/home/kshitij/hotosm/fAIr/trainings
-        ```
+
     - Create ```.env``` in /frontend
         ```
         cp .env_sample .env
@@ -110,7 +112,7 @@ Docker Compose is created with redis , worker , postgis database ,  api and fron
 
         python manage.py makemigrations
         python manage.py makemigrations login
-        python manage.py makemigrations login
+        python manage.py makemigrations core
         python manage.py migrate
 
 9. Play and Develop 

docs/FAQ.md

@@ -0,0 +1,74 @@
+# Frequently Asked Questions (FAQs)
+
+## Users
+**Q : What is fAIr?**
+
+A : fAIr is an open-source toolkit developed by the Humanitarian OpenStreetMap Team (HOT) that enables the integration of artificial intelligence (AI) into humanitarian mapping workflows. It provides AI models and tools to automate mapping tasks, improving efficiency and accuracy.
+
+<hr>
+
+**Q : How can I use fAIr in my mapping projects?**
+
+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.
+
+<hr>
+
+**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 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.
+
+<hr>
+
+**Q : Can I customize the AI models in fAIr for my specific needs?**
+
+A : Yes, fAIr is designed to be customizable. The toolkit provides instructions and guidelines on how to train and fine-tune models with your own datasets. This allows you to adapt the models to suit your specific mapping requirements and improve their performance on your target areas or features of interest.
+
+<hr>
+
+**Q : Are there any limitations to using fAIr?**
+
+A : While fAIr offers powerful AI models, it's important to note that the accuracy of the results may vary depending on the quality and resolution of the input imagery and the complexity of the mapping task. Some tasks may require additional manual editing or validation. It's recommended to review the documentation and understand the limitations of each model before use.
+
+<hr>
+
+**Q : How can I contribute feedback or report issues with fAIr?**
+
+A : You can contribute to the improvement of fAIr by providing feedback, reporting issues, or suggesting new features through the project's GitHub repository. By creating an issue, you can share your observations or difficulties faced while using fAIr, allowing the developers to address them and enhance the toolkit.
+
+<hr>
+
+**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 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.
+
+<hr>
+
+## Developers
+
+**Q : How can I contribute to the development of fAIr?**
+
+A : You can contribute to fAIr's development by following the guidelines provided in the project's GitHub repository. You can contribute code, suggest new features, or help improve documentation. By submitting pull requests, you can propose changes and collaborate with the project maintainers and the community.
+
+<hr>
+
+**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 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.
+
+<hr>
+
+**Q : Are there any specific coding standards or guidelines for contributing to fAIr?**
+
+A : Yes, fAIr follows coding standards and guidelines outlined in the project's repository. These guidelines cover aspects such as code formatting, documentation, and testing. Adhering to these standards helps maintain code consistency and facilitates collaboration among contributors.
+
+<hr>
+
+**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 at [fAIr-utilities](https://github.com/hotosm/fAIr-utilities) and engage in discussions with the community . Once approved community will be able go use it .
+
+<hr>
+
+**Q : What kind of support can I expect as a developer contributing to fAIr?**
+
+A : As a developer contributing to fAIr, you can receive support and guidance from the project maintainers and the community. The maintainers can help address technical questions, review your contributions, and provide feedback. The community also offers a collaborative environment where you can interact with other contributors and share knowledge.