Results 191 to 200 of about 4,629,474 (274)

Multi‐Spectral Gaussian Splatting with Neural Color Representation

Computer Graphics Forum, EarlyView.
Abstract 3D Gaussian Splatting (3DGS) [KKLD23] has transformed novel‐view synthesis from RGB images, yet remains restricted to the visible spectrum. Many applications, including agricultural monitoring, rely on multi‐spectral imaging, where spectral camera alignment and scalability pose major challenges.
Lukas Meyer, Josef Grün, Maximilian Weiherer, Bernhard Egger, Marc Stamminger, Linus Franke   +5 more
wiley   +1 more source

Investigating LST evolution and heatwave patterns using machine learning in the Beijing-Tianjin-Hebei Urban agglomeration. [PDF]

iScience
Liu C, Zhang M.
europepmc   +1 more source

A revised classification system of the marronoid clade of spiders (Araneae), based on dense sampling phylogenomics and comparative morphology

Cladistics, EarlyView.
Abstract The marronoid clade is a globally distributed, ecologically diverse group of spiders. It is an important faunal component in both natural and synanthropic environments, and even found in aquatic and extreme habitats such as desert salt flats. The systematics of the marronoid clade has been notoriously unstable due to the widespread character ...
Jonas O. Wolff, Michael B. J. Kelly, Franklyn Cala‐Riquelme, Jacob A. Gorneau, Katherine O. Montana, Helen M. Smith, Phil J. Sirvid, Cor J. Vink, Lauren A. Esposito, Martín J. Ramírez, Charles E. Griswold   +10 more
wiley   +1 more source

Interpretable machine learning reveals daytime and nighttime forest fire point drivers in Guizhou. [PDF]

iScience
Zhang Y, Li Z, Chen L.
europepmc   +1 more source

Monitoring wildlife health for diseases with visible signs by integrating camera traps with marked individuals

Conservation Biology, EarlyView.
Abstract Monitoring wildlife health is essential for conservation and management, wildlife and livestock welfare, and public health in a One Health framework. Yet, wildlife health monitoring often requires long‐term fieldwork and intensive sampling, which can be costly or logistically challenging, especially for remote, rare, or elusive populations. To
Jonathan Tichon, Roi Lapid, Roni King, Elyasaf Freiman, Orr Spiegel, Erez Baruchi, Aviam Atar, Shirli Bar‐David, David Saltz   +8 more
wiley   +1 more source

Spatial Patterns of Opioid Overdose Mortality Risk Across Urban, Transitional, and Rural Communities Using Satellite-Derived and Socioeconomic Indicators. [PDF]

J Urban Health
Barboza-Salerno G, Harrington T.
europepmc   +1 more source

Signal of carbon exchange in Qilian mountain based on eddy covariance and remote sensing data. [PDF]

iScience
Han C, Liu Y, Wang X, Liu Z, Liu G, Chen R.   +5 more
europepmc   +1 more source

Spatial distribution and associated factors of public healthcare infrastructure in tourism-oriented environments: evidence from Southwest China in the context of residential tourism. [PDF]

Front Public Health
Zhao H, Wei W, Yu S, Wang C.
europepmc   +1 more source

Does nature shape risk preferences? Evidence from Chile, Norway, and Tanzania

Economic Inquiry, Volume 63, Issue 2, Page 568-590, April 2025.
Abstract Does exposure to a more risky environment affect risk preferences? Going beyond single‐case study evidence, we report results from five surveys conducted in three countries and link this with administrative data to study whether a link between exposure and preferences is detectable and widespread. We find no evidence for endogenous preferences
Florian Diekert, Robbert‐Jan Schaap
wiley   +1 more source

Detecting gaps between urban expansion and lighting infrastructure growth using daytime and nighttime satellite imagery. [PDF]

Int J Appl Earth Obs Geoinf
Chen TK, Chen W, Stokes EC, Zhou Y.
europepmc   +1 more source