We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active wildfires will spread at a resolution of 24 hours. The dataset consists of 13 607 images across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a full time series of daily observations, containing detected active fires and variables related to fuel, topography and weather conditions. The dataset is challenging due to: a) its inputs being multi-temporal, b) the high number of 23 multi-modal input channels, c) highly imbalanced labels and d) noisy labels, due to smoke, clouds, and inaccuracies in the active fire detection.

The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fields. Despite the increasing popularity of deep learning and remote sensing, the majority of researchers still use decision trees and random forests for Landsat image analysis due to the prevalence of small labeled datasets and lack of foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for Earth O bservation for the Landsat family of satellites (including 3 sensors and 2 product levels) and the largest Landsat dataset in history (5M image patches). Additionally, we modernize and re-release the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML benchmark datasets for Landsats 4-5 TM and Landsat 7 ETM+ SR. Finally, we pre-train the first foundation models for Landsat imagery using SSL4EO-L and evaluate their performance on multiple semantic segmentation tasks.

Snowpack levels across a wide swath of western US states are among the lowest seen in decades, even as regulators struggle to negotiate water rights in the region. States across the western US are facing record low snowpack levels in the middle of the winter season. The snowpack crisis, which could mean a drier, more wildfire -prone summer, is coming as states are racing unsuccessfully against a deadline to agree on terms to share water in the Colorado River Basin, the source of water for 40 million people across seven states in the West. "Barring a genuinely miraculous turnaround" in the remainder of the winter, says Daniel Swain, a climate scientist at the University of California Agriculture and Natural Resources, the low snowpack "has the potential to worsen both the ecological and political crisis on the Colorado Basin, and then also produce really adverse wildfire conditions in some parts of the West." Data provided by the US Department of Agriculture show that as of February 12, snowpack was at less than half its normal level in areas across nine Western states--some of the lowest levels seen in decades.

The Chincha Kingdom likely used seabird guano to fertilize their corn. Breakthroughs, discoveries, and DIY tips sent six days a week. When it comes to the success of ancient civilizations, the first things that come to mind are typically their military strength, roads, and trade. New research, however, highlights a potential key to the strength of a pre-Incan society that is both surprising and slightly disgusting: seabird guano, also known as bird poop. The successful power in question is the Chincha Kingdom (1000 - 1400 CE), a coastal society that ruled over the Chincha Valley in present-day southern Peru.

We consider the problem of regret minimization in non-parametric stochastic bandits. When the rewards are known to be bounded from above, there exists asymptotically optimal algorithms, with asymptotic regret depending on an infi-mum of Kullback-Leibler divergences (KL).

The specialized crops could save farmers millions. A swarm of desert locusts fly after an aircraft sprayed pesticide in Meru, Kenya in 2021. Breakthroughs, discoveries, and DIY tips sent six days a week. Swarms of locusts devouring a farmer's livelihood might sound apocalyptic, but major locust infestations are a regular problem in agricultural communities around the world. These locust swarms--dense, droning packs of certain grasshopper species--can cover hundreds of square miles, and the insects consume vast amounts of vegetation and threaten global agriculture.

Weather conditions can drastically alter the state of crops and rangelands, and in turn, impact the incomes and food security of individuals worldwide. Satellite-based remote sensing offers an effective way to monitor vegetation and climate variables on regional and global scales. The annual peak Normalized Difference Vegetation Index (NDVI), derived from satellite observations, is closely associated with crop development, rangeland biomass, and vegetation growth. Although various machine learning methods have been developed to forecast NDVI over short time ranges, such as one-month-ahead predictions, long-term forecasting approaches, such as one-year-ahead predictions of vegetation conditions, are not yet available. To fill this gap, we develop a two-phase machine learning model to forecast the one-year-ahead peak NDVI over high-resolution grids, using the Four Corners region of the Southwestern United States as a testbed. In phase one, we identify informative climate attributes, including precipitation and maximum vapor pressure deficit, and develop the generalized parallel Gaussian process that captures the relationship between climate attributes and NDVI. In phase two, we forecast these climate attributes using historical data at least one year before the NDVI prediction month, which then serve as inputs to forecast the peak NDVI at each spatial grid. We developed open-source tools that outperform alternative methods for both gross NDVI and grid-based NDVI one-year forecasts, providing information that can help farmers and ranchers make actionable plans a year in advance.

Global sensitivity analysis of complex numerical simulators is often limited by the small number of model evaluations that can be afforded. In such settings, surrogate models built from a limited set of simulations can substantially reduce the computational burden, provided that the design of computer experiments is enriched efficiently. In this context, we propose an active learning approach that, for a fixed evaluation budget, targets the most informative regions of the input space to improve sensitivity analysis accuracy. More specifically, our method builds on recent advances in active learning for sensitivity analysis (Sobol' indices and derivative-based global sensitivity measures, DGSM) that exploit derivatives obtained from a Gaussian process (GP) surrogate. By leveraging the joint posterior distribution of the GP gradient, we develop acquisition functions that better account for correlations between partial derivatives and their impact on the response surface, leading to a more comprehensive and robust methodology than existing DGSM-oriented criteria. The proposed approach is first compared to state-of-the-art methods on standard benchmark functions, and is then applied to a real environmental model of pesticide transfers.

The 13-year-old bovine is crushing stereotypes of bovine intelligence. Breakthroughs, discoveries, and DIY tips sent six days a week. The smart animal club continues to add new members, and the newest might surprise you. A pet cow in Austria named Veronika picks up sticks with her mouth and uses them to scratch herself--which a team at University of Veterinary Medicine, Vienna in Austria believes is tool use. Veronika and her ground-breaking scratching are detailed in a study published today in . "The findings highlight how assumptions about livestock intelligence may reflect gaps in observation rather than genuine cognitive limits," Alice Auersperg, a study co-author and cognitive biologist at the university, said in a statement .

Do quantum computers offer a way to vastly improve agriculture? As quantum computers continue to advance, identifying problems they can solve faster than the world's best conventional computers is becoming increasingly important - but it turns out that a key task held up as a future goal by quantum proponents may not need a quantum computer at all. The task in question involves a molecule called FeMoco, which plays a vital role in making life on Earth possible. That is because it is part of the process of nitrogen fixation, in which microbes convert atmospheric nitrogen into ammonia, making it biologically accessible to most other living organisms. How exactly FeMoco works during this process is complicated and not fully understood, but if we could crack it and replicate it on an industrial scale, it could drastically cut the energy involved in producing fertilisers, potentially leading to a boost in crop yields.