Computing the Planet: Integrating Machine Learning, Remote Sensing, and Sensor Data Fusion for Environmental Insights

Abstract

Indeed, a range of systems in the environment requires timely, spatially explicit, and credible information to support its environmental decision-making, but no one observing system can give the complete and reliable measures of the Earth system across scales. This review summarizes how the realization of the Compute the Planet is underway in the form of machine learning, remote sensing, and sensor data fusion to generate decision-ready environmental insights. We use the application-first approach, which considers remote sensing, in situ and Internet of Things (IoT) sensing, and physics-based models as complementary streams of evidence with similar strengths and failures. We look critically at how an integrated system can convert heterogeneous observations to action products across three high impact application areas: atmosphere and air quality, water–land–ecosystem dynamics, and hazards. Rapid-response situational awareness, ecosystem condition metrics, drought and flood indicators, exposure maps, and hazard/extreme indicators are key products. The integrated systems to environment interface in three high impact application areas: atmosphere and air quality, water-land-ecosystem dynamics, and hazard Examine Our operational requirements can often determine real-life value such as latency, time stability, smooth degradation in the presence of missing or degraded inputs, and calibrated uncertainty usable in threshold-based decisions. These pitfalls are common across fields: mismatch in the scale between a point sensor and a gridded product, objectives on proxies in remotely sensed measurements, domain shift in the extremes and changing baselines, and evaluation aspects, which overestimate generalization because of spatiotemporal autocorrelation. Based on these lessons, we present cross-domain proposals for strong validation, uncertainty quantification, provenance, and versioning, as well as fair performance evaluation. We conclude that the next era of environmental intelligence will see a reduction in average accuracy improvement and an increase in terms of robustness, transparency, and operational responsibility, thus allowing the integrated environmental intelligence system to be deployed, which may be relied on to monitor human health, resource allocation, and survival in a more climate-adapted world.