PICOGRAM - informing coral reef resilience-based management through prediction of individual coral organismal growth, recruitment, and mortality

Coral reef science and management are increasingly relying on imagery-based methods to expand monitoring capacity and improve the assessment of reef condition. However, scalable monitoring requires coral colony-scale segmentation from high-resolution underwater imagery and orthomosaic-derived survey products, while dense manual pixel-wise annotation remains costly, time-consuming, and inconsistent across sites and imaging conditions. Operational methods therefore need to generalize across sites, optics, and platforms while providing calibrated confidence with minimal quality-assurance effort. We introduce NASA/NOAA PICOGRAM, an open-source extension inspired by the NASA NeMO-Net ecosystem, as an image-only, user-promptable framework for automated coral colony detection, segmentation, and percent-cover estimation. The current study evaluates segmentation and cover estimation from underwater still images and orthomosaic-derived image tiles; repeated site imagery can subsequently support longitudinal analyses of colony change. PICOGRAM adapts a Segment Anything Model (SAM)-style encoder–decoder to underwater imagery while freezing the visual backbone and training only low-rank adapters, a lightweight prompt encoder for points and boxes, and a transformer mask decoder. PICOGRAM is supervised using Simple Linear Iterative Clustering (SLIC)-derived pseudolabels. Enhanced images are over-segmented into superpixels, scored with underwater-aware color, texture, and edge features, smoothed on a superpixel graph, and converted to masks using a Potts conditional random field (CRF). Mask-level non-maximum suppression and a two-round curriculum further tighten pseudo-label selection. A quality head predicts mask Intersection-overUnion (IoU) and is calibrated on validation data to support site-specific operating thresholds. Using NOAA National Coral Reef Monitoring Program benthic survey imagery, PICOGRAM is evaluated on a site-disjoint expert-labeled hold-out (n = 137). The method attains an IoU of 87.5% in-domain and 82.5% cross-site, with strong boundary accuracy (bIoU 82.1%) and favorable precision–recall behavior (93.0% precision; PR–AUC 94.5%). Quality scores are well calibrated, with an expected calibration error of 0.028, and ambiguous masks can be refined efficiently with an average of 1.8 clicks to reach 0.90 IoU. Percent-cover estimates closely match expert annotations (Pearson r = 0.98, mean absolute error of 1.2 percentage points; bias −0.3 ± 2.1 pp). Across matched operating points, PICOGRAM modestly but consistently outperforms strong baselines based on SAM 1, SAM 2, YOLOv11, and CoralScop. By reducing the need for manual pixel-wise training masks and enabling calibrated, low-click refinement, PICOGRAM provides deployable coral colony-scale segmentation and cover estimation from NOAA underwater imagery and orthomosaic-derived tiles. This capability can support higher spatial coverage in coral monitoring programs, improve quality control in large image collections, and facilitate rapid post disturbance assessment, while future longitudinal validation will extend the framework toward direct estimation of colony growth, recruitment, and mortality.