Brief

Researchers have developed StruMPL, a novel multi-task dense regression model designed to estimate forest aboveground biomass (AGB) using disparate data sources. The model integrates satellite lidar data, which provides structural information but lacks biomass estimates, with ground-based plot data that offers biomass figures but is subject to bias and missingness. StruMPL addresses these challenges by employing a shared encoder with regression, imputation, and propensity heads for spatial correction, alongside a learnable physics module to enforce known allometric laws between variables. AI

A new research paper introduces an enhanced image simulator to generate realistic Earth Observation (EO) imagery degraded by atmospheric turbulence and satellite pointing errors. The study evaluates the performance of YOLOv8 and RetinaNet models on vessel detection tasks using this simulated data. Results indicate that YOLOv8's recall significantly drops under degraded conditions, while RetinaNet shows greater robustness, maintaining higher recall. AI