Results 181 to 190 of about 91,559 (240)

Bogong moths use a stellar compass for long-distance navigation at night. [PDF]

Nature
Dreyer D, Adden A, Chen H, Frost B, Mouritsen H, Xu J, Green K, Whitehouse M, Chahl J, Wallace J, Hu G, Foster J, Heinze S, Warrant E.   +13 more
europepmc   +1 more source

Redefining Optimal Coverage Path Planning for FLS‐Equipped AUVs With Deep Reinforcement Learning

Journal of Field Robotics, EarlyView.
ABSTRACT Autonomous Underwater Vehicles (AUVs) have emerged as indispensable tools for a variety of subsea tasks, from habitat monitoring and seabed mapping to infrastructure inspection and mine countermeasures. A fundamental challenge in this field is Coverage Path Planning (CPP), the problem of ensuring complete and efficient area coverage.
Lorenzo Cecchi, Alberto Topini, Alessandro Bucci, Alessandro Ridolfi   +3 more
wiley   +1 more source

Relationship Between Darkness and Healing of Night Sky in Planetarium. [PDF]

Int J Environ Res Public Health
Tanaka M, Otani K, Horiuchi T.
europepmc   +1 more source

From 0D to 3D liquid crystal elastomers architectures: Fabrications and applications

Responsive Materials, EarlyView.
This review presents a dimension‐oriented overview of liquid crystal elastomers (LCEs), highlighting their molecular alignment, fabrication strategies, and programmable deformations from 0D particles to 3D architectures. Emphasis is placed on how dimensionality guides design principles, enables diverse actuation modes, and bridges fundamental ...
Mingxuan Liu, Zongxuan Wu, Jinquan Zheng, Yidan Zhu, Yanjun Liu, Lingling Ma, Wei Hu, Yan‐Qing Lu, Dan Luo   +8 more
wiley   +1 more source

A multi-angle reflectance dataset of wheat and peach trees with unmanned aerial vehicle imagery. [PDF]

Data Brief
Guo Y, Mu X, Lyu X, Fan D, Lei C, Wu R, Xie D, Yan G.   +7 more
europepmc   +1 more source

Comparing convolutional neural network and random forest for benthic habitat mapping in Apollo Marine Park

Remote Sensing in Ecology and Conservation, EarlyView.
A comparison of Convolutional Neural Network (CNN) and Random Forest (RF) model predictions of benthic habitats within Apollo Marine Park. The CNN (left) and RF (right) classification maps show the spatial distribution of three habitat types: high energy circalittoral rock with seabed‐covering sponges, low complexity circalittoral rock with non‐crowded
Henry Simmons, Dang Nguyen, Benjamin Misiuk, Daniel Ierodiaconou, Sunil Gupta, Oli Dalby, Mary Young   +6 more
wiley   +1 more source

Advancements in Satellite Observations of Inland and Coastal Waters: Building Towards a Global Validation Network. [PDF]

Remote Sens (Basel)
Avouris DM, Maciel F, Sharp SL, Craig SE, Dekker AG, Di Vittorio CA, Gardner JR, Goldsmith E, Gossn JI, Greb SR, Grunert BK, Gurlin D, Jampani M, Khan RM, Lowin B, McKinna L, Mouw CB, Ogashawara I, Calle SR, Salls W, Sánchez-Cabeza JA, Schaeffer B, Seegers BN, Silander J, Smail EA, Wang M, Werdell J.   +26 more
europepmc   +1 more source

Using phenology to improve invasive plant detection in fine‐scale hyperspectral drone‐based images

Remote Sensing in Ecology and Conservation, EarlyView.
Using drone‐based hyperspectral images of mixed temperate successional forests collected over a growing season, detection algorithms were produced for three invasive species of interest, which are not only invasive in Virginia but also much of the U.S.: Ailanthus altissima (tree of heaven), Elaeagnus umbellata (autumn olive), and Rhamnus davurica ...
Kelsey S. Huelsman, Howard E. Epstein, Xi Yang, Roderick Walker   +3 more
wiley   +1 more source

Assessment of Noise Exposure in United States Urban Public Parks: A Scoping Review. [PDF]

Int J Environ Res Public Health
Nwanaji-Enwerem U, Mwenda KM, Dunsiger S, Grigsby-Toussaint D.   +3 more
europepmc   +1 more source

Lacking data? No worries! How synthetic images can alleviate image scarcity in wildlife surveys: A case study with muskox (Ovibos moschatus)

Remote Sensing in Ecology and Conservation, EarlyView.
This study investigates the integration of synthetic imagery, created with diffusion‐based models, to supplement limited training data and improve muskox (Ovibos moschatus) detection in zero‐shot (ZS) and few‐shot (FS) settings. ZS models detected more than 80% of muskoxen in real images, confirming the potential of synthetic data as a substitute for ...
Simon Durand, Samuel Foucher, Alexandre Delplanque, Joëlle Taillon, Jérôme Théau   +4 more
wiley   +1 more source