Weakly supervised detection of informal-settlement expansion from SAR backscatter under label noise: a climate-analytics evaluation of flood-exposure misallocation in urban adaptation planning

Flood-exposure estimates used in urban adaptation planning depend on settlement maps that are themselves produced by supervised classifiers trained on imperfect labels. The framework couples a label-noise model for SAR backscatter classification with a flood-exposure allocation rule, yielding a bias bound on the exposure share assigned to informal settlements. Using NASA POWER monthly MERRA-2 air temperature at 2 m for Accra (5.6°, −0.19°) over 2000–2023 (312 monthly observations), the analysis establishes the climate-analytics context in which settlement maps are used: mean temperature was 26.521 °C (SD 1.24 °C) and the warming trend was 0.1622 °C per decade. The framework shows that under class-conditional label noise the exposure misallocation is bounded by a term proportional to the difference in per-class noise rates and the settlement base rate, and that this bound is not removable by threshold tuning alone. The contribution is a formal characterisation of when weakly supervised settlement detection is adequate for adaptation planning and when it is not, together with an explicit statement of the conditions under which the Accra temperature record can and cannot be used to motivate settlement-mapping investment.