Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model

TL;DR

This paper introduces an unsupervised Bayesian nonparametric model to predict phytoplankton hotspots in the ocean by analyzing co-occurrence patterns of taxa from imaging data, effectively handling noisy and sparse data.

Contribution

It presents a novel community modeling approach that estimates spatial distributions of phytoplankton hotspots without direct observations, outperforming traditional methods.

Findings

The model accurately predicts phytoplankton hotspots in simulated missions.

It outperforms nearest neighbor and k-means methods in spatial prediction.

The approach is robust to noisy and sparse data environments.

Abstract

Many interesting natural phenomena are sparsely distributed and discrete. Locating the hotspots of such sparsely distributed phenomena is often difficult because their density gradient is likely to be very noisy. We present a novel approach to this search problem, where we model the co-occurrence relations between a robot's observations with a Bayesian nonparametric topic model. This approach makes it possible to produce a robust estimate of the spatial distribution of the target, even in the absence of direct target observations. We apply the proposed approach to the problem of finding the spatial locations of the hotspots of a specific phytoplankton taxon in the ocean. We use classified image data from Imaging FlowCytobot (IFCB), which automatically measures individual microscopic cells and colonies of cells. Given these individual taxon-specific observations, we learn a phytoplankton community model that characterizes the co-occurrence relations between taxa. We present experiments with simulated robot missions drawn from real observation data collected during a research cruise traversing the US Atlantic coast. Our results show that the proposed approach outperforms nearest neighbor and k-means based methods for predicting the spatial distribution of hotspots from in-situ observations.