| Student: | F. Campomanes MSc |
|---|---|
| Timeline: | March 2023 - 1 March 2027 |
Numerous African nations are currently undergoing a rapid process of urban transition, resulting in a significant surge in the proliferation of slum-like communities. This unprecedented urbanization presents considerable challenges for these countries, as they grapple with limited capacity to effectively address the associated problems. Consequently, socio-economic disparities are widening, and an unprecedented number of individuals find themselves in vulnerable circumstances. Regrettably, the availability of data on slums and their social attributes remains limited, hampering targeted efforts in prioritizing development and aid interventions. In this context, the integration of earth observation (EO), deep learning (DL) and citizen science (CS) emerges as a potential solution to effectively map and capture the intricacies of slums and their defining characteristics at large scales.
The current paradigm of AI development has been model-centric, where the focus has been building new network architectures or loss functions or new ways to optimize hyperparameters. However, data-centric AI (DCAI), veers away from touching the models themselves and instead pushes for ensuring the quality of the input datasets in a systematic and semi- or fully-automated manner. We employ DCAI to potentially improve the spatial transferability of slum mapping models in a data-scarce environment. To achieve this, we will (1) develop a systematic data refinement method to reduce and "clean" original input data and then (2) train several models for different cities using synthetic imagery labelled using citizen science. From these models, we will (3) select the optimal model by quantifying the domain shift between all trained models and unseen datasets (from other cities). Lastly, throughout the research, we will, (4) with the local stakeholders and slum dwellers, co-develop communication and visualization strategies of the research outputs .
Meet the team
