Carrol Chan

Building and progressing a mapping community in the Pacific, what is the role of unseen bias?

"Drawing inspiration from Edafe Onerhime's insightful talk, we aim to expand the conversation and deepen our understanding of how our assumptions shape the OSM ecosystem, the mapping communities in the Pacific and examples of current practices. The term ""building=yes and other defaults"" pertains to the data we add as features in OSM as well as the various contributors who map these features and/or share the same characteristics. Our communities, projects, and guidelines often reflect a limited set of characteristics, therefore introducing limitations and biases into the data. This is particularly important across a region of more than 30,000 islands, exhibiting variances in socio-economic, political and cultural statuses and differing practices of ‘community’. This talk will explore these default aspects and common assumptions using the the OSM ecosystem, as a primary example. It is not meant to assign blame or induce guilt but to encourage awareness & inclusive discussions within a safe space. The talk is aimed to understand (and take action) on how our unseen biases affect data and the progression of the mapping community including OSM. Strategies for addressing biases, enhancing data quality, and cultivating a more inclusive and equitable community, will also be addressed."

In the Pacific Islands region, where there are over 30,000 islands vulnerable to invasion and isolated, geospatial data and remote sensing are suitable tools to detect, manage and monitor prolific invasive species that are causing environmental and socio-economic impact.

In this talk we highlight the utilisation of geospatial data to undertake niche similarity/equivalency test to fill in knowledge gaps to better understand the behaviour of invasives such as the African Tulip. We also highlight and provide an update on the progress on the detection techniques using available data to map the African Tulip, these include textural-based analysis using machine learning algorithms and spectral-based techniques focused on species phenology. We also highlight existing challenges and opportunities for RS for invasive species management in the Pacific Islands region.