This paper describes Fields2Cover, a novel open source library for coverage path planning (CPP) for agricultural vehicles. While there are several CPP solutions nowadays, there have been limited efforts to unify them into an open source library and provide benchmarking tools to compare their performance. Fields2Cover provides a framework for planning coverage paths, developing novel techniques, and benchmarking state-of-the-art algorithms. The library features a modular and extensible architecture that supports various vehicles and can be used for a variety of applications, including farms. Its core modules are: a headland generator, a swath generator, a route planner and a path planner. An interface to the Robot Operating System (ROS) is also supplied as an add-on. In this paper, the functionalities of the library for planning a coverage path in agriculture are demonstrated using 8 state-of-the-art methods and 7 objective functions in simulation and field experiments.

Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics. For several decades, the mapping of SSH products at regional and global scales has relied on nadir satellite altimeters, which provide one-dimensional-only along-track satellite observations of the SSH. The Surface Water and Ocean Topography (SWOT) mission deploys a new sensor that acquires for the first time wide-swath two-dimensional observations of the SSH. This provides new means to observe the ocean at previously unresolved spatial scales. A critical challenge for the exploiting of SWOT data is the separation of the SSH from other signals present in the observations. In this paper, we propose a novel learning-based approach for this SWOT calibration problem. It benefits from calibrated nadir altimetry products and a scale-space decomposition adapted to SWOT swath geometry and the structure of the different processes in play. In a supervised setting, our method reaches the state-of-the-art residual error of ~1.4cm while proposing a correction on the entire spectral from 10km to 1000k

NOAA is tracking Hurricane Fiona by drone, as the storm moves through the Caribbean. In partnership with NOAA, Saildrone Inc. is deploying seven ocean drones to collect data from hurricanes during the 2022 hurricane season. The National Oceanic and Atmospheric Administration has released the first image from its NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. The recently-launched satellite captured a stunning panoramic view of the Earth, created from swaths of data captured throughout the full globe over a period of 24 hours between Dec. 5 and Dec. 6. Polar-orbiting satellites observe the entire planet twice each day, unlike geostationary satellites.

One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system. A key first step in reducing this uncertainty is to accurately classify cloud types at high spatial and temporal resolution. In this paper, we introduce Cumulo, a benchmark dataset for training and evaluating global cloud classification models. It consists of one year of 1km resolution MODIS hyperspectral imagery merged with pixel-width 'tracks' of CloudSat cloud labels. Bringing these complementary datasets together is a crucial first step, enabling the Machine-Learning community to develop innovative new techniques which could greatly benefit the Climate community. To showcase Cumulo, we provide baseline performance analysis using an invertible flow generative model (IResNet), which further allows us to discover new sub-classes for a given cloud class by exploring the latent space. To compare methods, we introduce a set of evaluation criteria, to identify models that are not only accurate, but also physically-realistic.

As the public becomes increasingly aware of the dangers of AI algorithmic bias and concerned over surveillance and militaristic applications of deep learning, there have been a growing number of calls for AI regulation. Whether new laws governing AI fairness or policies constraining the use of autonomous weapons systems, the challenge confronting policymakers is that AI is very much like encryption: it is not a single controlled algorithm that can be regulated, it is a portfolio of techniques that no single country controls and which are being advanced every day by researchers all across the world. The almost unimaginably rapid progression of deep learning over the past half-decade into every corner of modern life has ushered in profoundly existential questions about how to ensure accurate, fair and beneficial use of this rapidly evolving technology. When it comes to biased algorithms, the fundamental fairness of current AI systems has been largely left to market forces. In turn, basic economics has ensured that free but heavily biased data wins over costly but minimally biased data.

That was Siri's programmed response to a user saying, "You're a slut." And really, there couldn't be a more perfect example to illustrate the arguments in a new paper from UNESCO about the social cost of having new digital technologies dreamt up and implemented by teams dominated by men. Who but men could have scripted such a response, which seems intended to please a harasser who sees aggression as foreplay? Siri is forced to enact the role of a woman to be objectified while apologizing for not being human enough to register embarrassment. Apple has since rewritten the code for responding to the word slut to the more neutral "I don't know how to respond to that."

Geoglyphs in the southern Amazon are evidence of a once-thriving population. Before Spanish invaders conquered South America, sparse groups of nomadic people clustered around the Amazon River, leaving the surrounding rain forest pristine and untouched. New research suggests a very different story--an Amazonian region peppered with rain forest villages, ceremonial earthworks, and a much larger population than previously thought. The research, funded in part by the National Geographic Society and published today in the journal Nature Communications, challenges a common perception of the pre-Columbian Amazon rain forest as sparsely populated. That perception has endured despite 16th-century accounts of large, interconnected villages that go against modern assumptions.

The Modified Antarctic Mapping Mission (MAMM) was conducted from September to November 2000 onboard RADARSAT. The mission plan consisted of more than 2400 synthetic aperture radar data acquisitions of Antarctica that achieved the scientific objectives and obeyed RADARSAT's resource and operational constraints. Mission planning is a time-and knowledge-intensive effort. It required over a workyear to manually develop a comparable plan for AMM-1, the precursor mission to MAMM. This article describes the design and use of the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks and enabled rapid generation of what-if scenarios for evaluating alternative mission designs.

When Donald Trump met with technology leaders in December to tell them he wanted them to create jobs in the US, their heads probably tilted to the side, as if you tried to explain physics to your dog and she just watched your lips moving and wondered when, among all those unfamiliar sounds, she was going to hear the word "treat". They're in business to help us do more with less. They like innovation and disruption and software eating the world but people – er, not so much. In his own Chance-the-gardener way, the President-elect might be on to something. His victory was a middle-finger salute from those who feeling left out by technology and globalisation.

When Donald Trump met with technology leaders in December to tell them he wanted them to create jobs in the U.S., their heads probably tilted to the side, as if you tried to explain physics to your dog and she just watched your lips moving and wondered when, among all those unfamiliar sounds, she was going to hear the word treat. They're in business to help us do more with less. They like innovation and disruption and software eating the world. In his own Chance-the-gardener way, the president-elect might be onto something. His victory was a middle-finger salute from those who feel left out by technology and globalization.