We apply deep metric learning for the first time to the prob-lem of classifying planktic foraminifer shells on microscopic images. This species recognition task is an important information source and scientific pillar for reconstructing past climates. All foraminifer CNN recognition pipelines in the literature produce black-box classifiers that lack visualisation options for human experts and cannot be applied to open set problems. Here, we benchmark metric learning against these pipelines, produce the first scientific visualisation of the phenotypic planktic foraminifer morphology space, and demonstrate that metric learning can be used to cluster species unseen during training. We show that metric learning out-performs all published CNN-based state-of-the-art benchmarks in this domain. We evaluate our approach on the 34,640 expert-annotated images of the Endless Forams public library of 35 modern planktic foraminifera species. Our results on this data show leading 92% accuracy (at 0.84 F1-score) in reproducing expert labels on withheld test data, and 66.5% accuracy (at 0.70 F1-score) when clustering species never encountered in training. We conclude that metric learning is highly effective for this domain and serves as an important tool towards expert-in-the-loop automation of microfossil identification. Key code, network weights, and data splits are published with this paper for full reproducibility.

Deep streams of data from Earth-imaging satellites arrive in databases every day, but advanced technology and expertise are required to access and analyze the data. Now a new system, developed in research based at the University of California, Berkeley, uses machine learning to drive low-cost, easy-to-use technology that one person could run on a laptop, without advanced training, to address their local problems. Berkeley-based project could support action worldwide on climate, health, and poverty. More than 700 imaging satellites are orbiting the earth, and every day they beam vast oceans of information -- including data that reflects climate change, health, and poverty -- to databases on the ground. There's just one problem: While the geospatial data could help researchers and policymakers address critical challenges, only those with considerable wealth and expertise can access it.

Water scarcity – an increasingly important topic of conversation as countries across the Middle and East and Africa grapple with rising concerns around future water shortages, which not surprisingly often results in civil unrest. Currently the Middle East and North Africa is the most water-scarce region in the world, according to the United Nations. With at least 17 countries well below the water line, water is being consumed faster than it can be replenished. Only a year ago, the City of Cape Town was in the headlines with a forecasted Day Zero where the city's taps would be turned off in order to preserve the remaining water supply (13.5% of normal) for critical services. Fortunately, the city avoided Day Zero through a combination of water saving measures and well-timed rain.