Shamilah Nassozi

During the 2019-2020 pilot supported by Microsoft, 18 million building footprints were automatically extracted from satellite imagery for all of Tanzania and Uganda. HOT found that on average, mappers working without AI assistance could map between 1000-1500 buildings per working day. For areas with high-quality AI output, providing mappers with AI-generated building footprint suggestions increased this rate to up to 2500-3000 buildings per day approximately doubling the rate at which building data could be added to OpenStreetMap, which is the crucial link for making it available to the humanitarian information management community.

While doubling mapping efficiencies (100% efficiency gain) are promising, one of the greatest challenges is making sure data is converted from AI/ML to OpenStreetMap in a rapid yet responsible community-centric way (respecting existing data contributions already in OpenStreetMap). This project enabled us to take the ‘next step’ after receiving the building predictions from Microsoft by building better tools for data conflation. This is expected to dramatically reduce manual intervention. By doing this, human mappers can focus their time and skills on value-added activities: ground-truthing and validating predictions and adding local knowledge to the map not visible from satellite imagery (such as place names, location of key lifeline infrastructure, etc. We’ll describe our mapping workflow and progress updates for Kenya and Nigeria, and the specific challenges met with this dataset extracted through machine learning will be explained. Considering the growing availability of such AI-related datasets, we’ll review common errors and how we adapted to them and to other issues such as imagery offsets, heterogeneity of existing data and other context specific challenges. Eventually we’ll propose recommendations regarding this type of editing and aspects to consider before starting such imports and how to get the community involved.

Most field program managers have their go-to field preparation checklist - this often includes a data model, their preferred data collection tools, field survey timeline, to name a few. We are often cautioned about the importance of community entry, and it is right, you will not be able to just enter the community and start mapping as people will get curious, ask questions and possibly become suspicious or hesitant to accept your data collection activities. At HOT, we employe participatory mapping methods and encourage local people to map their communities. However, sometimes with even all the correct steps followed, your activities can be hindered due to factors outside of your control. In this session, we will explore one of HOT’s field mapping projects implemented in Kampala in collaboration with the Kampala Capital City Authority that aimed to map community-level flood risk in a local suburb along the Nakamiro Channel catchment area. Despite all the correct steps taken, community entry in a specific jurisdiction felt impossible and field mapping could not be carried out. In this session, our aim is to first discuss what went wrong and how our field team approached this situation and later invite participants/attendees to share similar challenges experienced in the field and how these situations were overcome or addressed.

Most field program managers have their go-to field preparation checklist - this often includes a data model, their preferred data collection tools, field survey timeline, to name a few. We are often cautioned about the importance of community entry, and it is right, you will not be able to just enter the community and start mapping as people will get curious, ask questions and possibly become suspicious or hesitant to accept your data collection activities. At HOT, we employe participatory mapping methods and encourage local people to map their communities. However, sometimes with even all the correct steps followed, your activities can be hindered due to factors outside of your control. In this session, we will explore one of HOT’s field mapping projects implemented in Kampala in collaboration with the Kampala Capital City Authority that aimed to map community-level flood risk in a local suburb along the Nakamiro Channel catchment area. Despite all the correct steps taken, community entry in a specific jurisdiction felt impossible and field mapping could not be carried out. In this session, our aim is to first discuss what went wrong and how our field team approached this situation and later invite participants/attendees to share similar challenges experienced in the field and how these situations were overcome or addressed.