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+# fAIr
+
+## :open_book: History
+
+We recognized the Open Cities Challenge for building segmentation mid-2020, and then around the end of 2020, HOT conducted research in collaboration with the Netherland Red Cross. Last year, HOT contributed to an academic research project investigating the capability of UAV imagery to be used for AI-assisted mapping on refugees camps in Africa, which proved that the use of localised AI models produces higher prediction accuracy in comparison to wide trained models.
+
+In March 2022, we participated in an AI for Social good seminar in Frankfurt, Germany where data scientists and nonprofit organisations came together pursuing various social good goals. Around mid-2022, we (the hot_tech team) set our strategy and defined our direction. From the beginning of 2022, we have played an advisory role in the RAMP project, until its release in October 2022. Currently (November 2022), we have reached the midpoint on the Omdena-HOT innovation challenge and built the fAIr roadmap (read this for key takeaways) .
+
+
+
+
+## Glossary
+fAIr is the product name. How come?:
+
+
+f: for freedom and free and open-source software
+
+AI: for Artificial Intelligence
+
+r: for resilience and our responsibility for our communities and the role we play within humanitarian mapping
+
+AI models: AI is wide term and it includes lots of approaches and techniques. In our (mapping) context, we refer to computer vision techniques to detect objects from satellite imagery. These objects can be buildings, roads, water ways, trees, and potentially other objects. fAIr intersects with Machine Learning, Deep Learning, Computer Vision .
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+
+
+
+## What , How and for Whom?
+Unlike other AI data producers, fAIr is an intuitive, fair and open-source AI-assisted mapping tool where AI models are created and trained by the people living and working in the local communities. By working with the local communities (and getting constant feedback on the models), we strive to eliminate model biases as we ensure the models are relevant to the communities where the maps are being created to improve the conditions of the people living there.
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+fAIr uses AI models (built by humanitarian OSM mappers) to detect map features based on open-source satellite and UAV imagery from HOT’s OpenAerialMap (OAM) and suggest detected features to be added to OpenStreetMap (OSM). It is crucial that models will NOT produce mass features and NO mass import into OSM is planned. Unlike other AI data producers, fAIr is a free and open-source AI service that offers local communities accurate feedback loops through the efforts of OSM community mappers. This results in progressive intelligence of computer vision models. Whenever an OSM mapper uses the AI models for assisted mapping and completes corrections, fAIr can take those corrections as feedback to enhance the AI model’s accuracy.
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+fAIr is an AI-assisted mapping service currently being developed for humanitarian OSM mappers who want or need to map more efficiently. The goal is to provide humanitarian OSM mappers access to AI-assisted mapping across mobile and in-browser editors using “community-created AI models”. Yes, you read it right, an OSM community member will be able to create their own open-source AI model and use it for mapping in their region of interest and/or humanitarian need.
+
+**We suggest you to read Omran Najjar's [blog](https://www.hotosm.org/tech-blog/hot-tech-talks-fair/) on fAIr** .
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+# :hugs: Welcome To The fAIr Wiki :hugs:
+
+## _**What are the challenges that mappers experience while utilizing AI data on OpenStreetMap?**_
+
+- ### Quality of AI Data
+ One of the primary challenges faced by mappers when using AI data on OpenStreetMap is the quality of the data. The accuracy and completeness of AI data depends on the quality of the training data used to train the AI model. If the training data is biased or incomplete, the AI model will produce inaccurate results and this has happened in some cases especially with roads. Mappers must therefore carefully assess the quality of the AI data before adding it to OSM.
+- ### Data integration process.
+ Another challenge faced by mappers is the integration process. Foexample, when using using Mapwithai, one has to always pick potions of data from the AI layer if they are buildings, one picks a maximum of less that 100 buildings and pastes them in the OSM data layer and does the same for all the buildings in that particular area or tile or layer.
+- ### Technical Expertise
+ The use of AI technology in mapping requires technical expertise, which can be a significant challenge for mappers who may not have the necessary skills. For instance, beginer Mappers can not be recommended or trained to use these AI tools as they may mess the entire area. Therefore frequent trainings are needed for mappers to be equipped with adquet skills of handling such tasks.
+- ### Imagery offsets
+ The AI technologies for example AI data from Microsoft was and is generated using Bing Imagery which in several cases is older compared to the recent Imageries like Maxar Premium. The imposes a big challenge in aligning the AI data with the latest imageries.
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+
+
+### Here the HOT’s open AI-assisted mapping service: fAIr comes to rescue .
+The fAIr tool is also an open-source mapping tool with AI assistance, and the AI models it uses are developed and trained by people who reside in and work in nearby towns. To know more about fAIr check the **About** page.
+