  Aquaculture production -- the cultivation of aquatic plants and animals --
has grown rapidly since the 1990s, but sparse, self-reported and aggregate
production data limits the effective understanding and monitoring of the
industry's trends and potential risks. Building on a manual survey of
aquaculture production from remote sensing imagery, we train a computer vision
model to identify marine aquaculture cages from aerial and satellite imagery,
and generate a spatially explicit dataset of finfish production locations in
the French Mediterranean from 2000-2021 that includes 4,010 cages (69m2 average
cage area). We demonstrate the value of our method as an easily adaptable,
cost-effective approach that can improve the speed and reliability of
aquaculture surveys, and enables downstream analyses relevant to researchers
and regulators. We illustrate its use to compute independent estimates of
production, and develop a flexible framework to quantify uncertainty in these
estimates. Overall, our study presents an efficient, scalable and highly
adaptable method for monitoring aquaculture production from remote sensing
imagery.
