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Openly available geodata served as the input for our framework. In this study, GeoAI represents the detection and mapping of common Ae aegypti breeding containers from satellite and street view imagery. We fitted a BSTM with an INLA to generate seasonal suitability maps for immature Ae aegypti covering the whole municipality of Rio de Janeiro at Aedes flight range resolution using suitability indicators and ovitrap counts. Entomological surveillance data from ovitraps and LIRAa were applied for evaluation. BI=Breteau index collected during LIRAa. BSTM=Bayesian spatiotemporal model. GeoAI=geospatial techniques of artificial intelligence. HI=House index collected during LIRAa. INLA=integrated nested Laplace approximation. LIRAa=Rapid Assay of the Larval Index for Ae aegypti. LOESS=locally weighted scatterplot smoothing. MET=mean egg per trap rate collected monthly via ovitraps. MLT=mean larva per trap rate collected monthly via ovitraps. NB-GLM=negative-binomial generalised linear regression model.

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