Article describes how there is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network.

This article presents a comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results.