Integrating Deep Learning and Spatial Statistics in Marine Ecosystem Monitoring

TL;DR

This paper combines deep learning object detection with spatial statistical modeling to accurately estimate species distribution in marine ecosystems, correcting for detection biases in large-scale photogrammetric surveys.

Contribution

It introduces a novel approach integrating deep learning and thinned LGCP models to address detection uncertainty in spatial ecological data.

Findings

Automatic detection improves species distribution estimates.

The model effectively corrects detection bias.

Enhanced accuracy over traditional methods.

Abstract

In ecology, photogrammetry is a crucial method for efficiently collecting non-destructive samples of natural environments. When estimating the spatial distribution of animals, detecting objects in large-scale images becomes crucial. Object detection models enable large-scale analysis but introduce uncertainty because detection probability depends on various factors. To address detection bias, we model the distribution of a species of benthic animals (holothurians) in an area of the Italian Tyrrhenian coast near Giglio Island using a Thinned Log-Gaussian Cox Process (LGCP). We assume that a "true" intensity function accurately describes the distribution, while the observed process, resulting from independent thinning, is represented by a degraded intensity. The detection function controls the thinning mechanism, influenced by the object's location and other detection-related features. We use manual identification of holothurians as our benchmark. We compare automatic detection with this benchmark, an unthinned LGCP, and the thinned model to highlight the improvements gained from the proposed approach.Our method allows researchers to use photogrammetry, automatically identify objects of interest, and correct biases and approximations caused by the observation process.