Results 61 to 70 of about 22,166 (333)

Satellite-Based Crop Typology Mapping with Google Earth Engine

Engineering Proceedings
Crop classification plays a pivotal role in agricultural remote sensing, offering critical insights into planting areas, growth monitoring, and yield evaluation.
Alapati Renuka, Manne Suneetha, Prathipati Vasavi   +2 more
doaj   +1 more source

Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation

Remote Sensing, 2016
Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple ...
Adia Bey, Alfonso Sánchez-Paus Díaz, Danae Maniatis, Giulio Marchi, Danilo Mollicone, Stefano Ricci, Jean-François Bastin, Rebecca Moore, Sandro Federici, Marcelo Rezende, Chiara Patriarca, Ruth Turia, Gewa Gamoga, Hitofumi Abe, Elizabeth Kaidong, Gino Miceli   +15 more
doaj   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Evaluation of satellite precipitation products over Mexico using Google Earth Engine

Journal of Hydroinformatics, 2022
Satellite-based precipitation products and reanalysis precipitation products have the potential to overcome the lack of information in regions where there are no or insufficient rain gauges to achieve any hydrological study. The Google Earth Engine (GEE)
Pedro Rincón-Avalos, Abdou Khouakhi, Oliver Mendoza-Cano, Jesús López-De la Cruz, Karla Michelle Paredes-Bonilla   +4 more
doaj   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

RODEO: An algorithm and Google Earth Engine application for river discharge retrieval from Landsat

, 2021
A publicly available Google Earth Engine application for river discharge retrieval from ...
David, Cedric, Allen, George, Pan, Ming, Riggs, Ryan, Lin, Peirong, Yang, Xiao   +5 more
core   +1 more source

Development of a Google Earth Engine-Based Application for the Management of Shallow Coral Reefs Using Drone Imagery [PDF]

, 2023
Paula A. Zapata-Ramírez, Hernando Hernández-Hamón, Clare Fitzsimmons, Marcela Cano, J. M. Pajares García, Carlos A. Zuluaga, Rafael E. Vásquez   +6 more
openalex   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Automated avalanche susceptibility mapping in Google Earth engine

, 2022
LAUREA MAGISTRALELe mappe di pericolosità di valanghe sono strumenti importanti per aiutare i governi, i gestori del territorio e i turisti a rendersi conto dei possibili luoghi pericolosi nelle zone di montagna. Rispetto allo sviluppo dei metodi per la
Wang, Zhuowei
core  

Detection of agricultural fires in the state of Acre with GEE and QGIS

Biodiversidade Brasileira, 2020
Herein we aimed to test four supervised classifiers to map forest scars caused by agricultural burning activities, and also evaluate time-quality ratio accuracy.
Francisco Salatiel Clemente de Souza, Sonaira Souza da Silva, Liana Andreson, Philip Fearnside, Marcus Liesenfeld, Igor Oliveira, Thiago Morello, Adriele Karlokoski, Tiago Lucena da Silva, Marllus Rafael Almeida, Luiz Aragão, Foster Brown, Jessica Costa, Antonio Willian Melo, Paulo Maurício Graça, Paulo Monteiro Brando, Ane Alencar   +16 more
doaj   +3 more sources