Drones and tweets for mapping the built environment

Abstract

Accurate documentation and mitigation of wildfires are significant in addressing the UN's Sustainable Development Goal Eleven (11) as well as the African Union's Agenda 2063 Goal One (1), which resonate an intention to preserve sustainable environments, communities, and cities for future generations. Poor and unscientific documentation of wildfires leads to error-prone and quickly outdated event curation, making it difficult to create comprehensive overviews of wildfire events. Although several geospatial studies have recorded advances in wildfire data curation, many of these rely on structured spatial datasets, which enjoy numerous advantages but can be prone to temporal or time-voids in data. Voids in data can be mitigated through interpolation techniques, but these are not without challenges for the modellers tasked with optimally filling the voids. On this backdrop, this paper therefore aims to test and extend strategies of wildfire reporting by investigating the strengths of fusing mainstream spatial data (including high-resolution unmanned aerial vehicle (UAV) footage) with unstructured Twitter (tweets) data in order to disrupt common practise and contribute to the global agenda. The novelty of this contribution lies in its unusual hybrid data approach used to statistically establishes long- and short-term pre-fire conditions tested on the African landscape. It proceeds to illustrate how social media data can create fascinating and yet accurate visual timelines of the events that surprisingly compared strongly to the results from structured sources. Finally, the study detects the extent of damage from the wildfire using supervised classification, burn indices and three-dimensional (3D) reconstruction. A 95% positive detection rate was reported, and it affirmed the place of unstructured data in mainstream scientific approaches such as wildfire documentation. The work presented therefore contributes towards meeting African Union's Agenda 2063 and the United Nations Sustainable Development Goals (2030) in curation, documentation and mitigation of sustainable communities and environments.

Paper

A fusion of structured and unstructured datasets in curating fire damage (2023)

How to cite: O-Sullivan Hewlett, D., Shoko, M., and Chamunorwa, B.: A fusion of structured and unstructured datasets in curating fire damage, Scientific African, Volume 20, 2023, e01656, ISSN 2468-2276, https://doi.org/10.1016/j.sciaf.2023.e01656.