Calibrating Remote Sensing for Climate-Risk Zones

Researchers are tackling the data gap in rapidly growing Sub-Saharan African cities by combining remote-sensing measurements, low-cost satellite imagery, and smarter machine-learning models. Their work focuses on validating and improving these tools—so models trained on U.S. suburbs can reliably map risks like flooding, heat, and transportation stress in places like Kigali. For the test & measurement community, it’s a story of making high-tech sensing and calibration methods robust where data is scarce—so climate-risk planning can work where it’s needed most.