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 Food & Agriculture


WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction

Neural Information Processing Systems

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.



SSL4EO-L: Datasets and Foundation Models for Landsat Imagery Adam J. Stewart

Neural Information Processing Systems

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.



Record Low Snow in the West Will Mean Less Water, More Fire, and Political Chaos

WIRED

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.


Bird poop powered this pre-Hispanic kingdom

Popular Science

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.



Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits

Neural Information Processing Systems

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 lobstermen teaming up with scientists to save endangered whales

Popular Science

In a game of scientific telephone, if you find the food, you find the whales--and sound the alarm. North Atlantic right whales sometimes gather at Jeffrey's Ledge, a 62-mile-long underwater ridge about 25 miles off the coast of Portsmouth, New Hampshire. Breakthroughs, discoveries, and DIY tips sent six days a week. It was a cold and windy week last January, when a group of Maine lobstermen couldn't haul in their traps from Jeffrey's Ledge. The reason why surprised everyone.


Termites are swarming Florida even faster than predicted

Popular Science

Most of the state may be fighting the invasive species by 2050. Breakthroughs, discoveries, and DIY tips sent six days a week. Termites have plagued southern states like Florida for decades, but a new study indicates that the problem is even worse than researchers previously believed. After reviewing over 30 years of monitoring data, entomologists at the University of Florida (UF) now say both the Formosan and Asian subterranean termites ( and) are expanding their range of destruction. They've already traveled farther north than scientists initially predicted.