The team observed the gentle giants two days in a row. Blue whales can be found in every ocean except the Arctic. Breakthroughs, discoveries, and DIY tips sent six days a week. As if soaring above the brilliant blue ocean isn't spectacular enough, the New England Aquarium's aerial survey team recently experienced two back-two-back sightings of blue whales --a little déjà blue, per the aquarium's clever social media post. The first sighting occurred on February 27, when scientists from the Aquarium's Anderson Cabot Center for Ocean Life spotted a blue whale ().

Early test batches contained 12 percent protein. Food scientists with the Arkansas Agricultural Experiment Station investigated proteins from three parts of a single rice cultivar for plant-based cheesemaking and discovered each source offered different qualities. Breakthroughs, discoveries, and DIY tips sent six days a week. There are a lot of non-dairy and vegan cheese alternatives on the market today. But while the tastes and textures of many of them almost pass for the real thing, they usually lack one major component: protein .

Stinknet is an invasive weed that can disperse thousands of seeds at a time. An invasive yellow weed called stinknet surrounding desert plants. Breakthroughs, discoveries, and DIY tips sent six days a week. Invasive plants can be just as destructive as animals --and often fly more under the radar until it's too late. The noxious yellow weed gives off more than just an offensive smell; it's also destroying native wildflowers critical to the ecosystem.

The agent pulls an arm and observes the corresponding return provided by the environment.

However, such interleaving methods struggle to produce final results that look like natural objects of interest (i.e., manifold feasibility)

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.

However, this assumption can possibly fail in real-world scenarios.

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.