Tag: deep learning

Deep Learning Applied to Satellite Imagery Reveals Untracked Ships

Published on by Jonathan Crowe
Maps showing registered and unregistered fishing vessels near Spain, Morocco, Sicily and Tunisia.
Excerpt from Fig. 2 of Paolo et al., “Satellite mapping reveals extensive industrial activity at sea,” Nature 625 (2024).

Speaking of AI-assisted global monitoring: researchers affiliated with Global Fishing Watch have revealed that the global fishing, transport and energy fleets are a lot bigger than expected. They were able to compare the locations of ships carrying AIS transponders with satellite imagery, to which deep learning was applied to classify ships. They conclude that something like three-quarters of industrial fishing vessels, and thirty percent of transport and energy vessels, go untracked. This isn’t necessarily so much about clandestine activity—in many regions ships, especially fishing boats, simply aren’t required to be tracked—but it can, among other things, reveal illegal fishing in protected areas. Results of the study were published in Nature last month. Global Fishing Watch also has an interactive map. [The Verge]

Google, EDF Partner to Build Map of Global Methane Emissions

Published on by Jonathan Crowe

Methane is a greenhouse gas, more powerful than CO2 but shorter-lived. Google is partnering with the Environmental Defense Fund to map global methane emissions, much of which result from leaks from fossil fuel infrastructure and are undercounted. The EDF’s MethaneSAT satellite (itself a partnership between the EDF and New Zealand’s space agency) launches next month: it’ll measure methane emissions at high resolution. Google’s bringing to the party algorithms and AI, the latter to build a global map of oil and gas infrastructure.

Once we have this complete infrastructure map, we can overlay the MethaneSAT data that shows where methane is coming from. When the two maps are lined up, we can see how emissions correspond to specific infrastructure and obtain a far better understanding of the types of sources that generally contribute most to methane leaks. This information is incredibly valuable to anticipate and mitigate emissions in oil and gas infrastructure that is generally most susceptible to leaks.

More at The Verge.

Previously: Mapping Methane Emissions.