Google Earth Engine and artificial intelligence (AI): a comprehensive review

L Yang, J Driscol, S Sarigai, Q Wu, H Chen, CD Lippitt - Remote Sensing, 2022 - mdpi.com
Remote sensing (RS) plays an important role gathering data in many critical domains (eg,
global climate change, risk assessment and vulnerability reduction of natural hazards …

Challenges to use machine learning in agricultural big data: a systematic literature review

A Cravero, S Pardo, S Sepúlveda, L Muñoz - Agronomy, 2022 - mdpi.com
Agricultural Big Data is a set of technologies that allows responding to the challenges of the
new data era. In conjunction with machine learning, farmers can use data to address …

Map** of cropland, crop** patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest

A Tariq, J Yan, AS Gagnon, M Riaz Khan… - Geo-Spatial …, 2023 - Taylor & Francis
Map** and monitoring the distribution of croplands and crop types support policymakers
and international organizations by reducing the risks to food security, notably from climate …

[HTML][HTML] Current and near-term advances in Earth observation for ecological applications

SL Ustin, EM Middleton - Ecological Processes, 2021 - Springer
There is an unprecedented array of new satellite technologies with capabilities for
advancing our understanding of ecological processes and the changing composition of the …

Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia

Y Kang, H Choi, J Im, S Park, M Shin, CK Song… - Environmental …, 2021 - Elsevier
Abstract In East Asia, air quality has been recognized as an important public health problem.
In particular, the surface concentrations of air pollutants are closely related to human life …

Map** croplands of Europe, middle east, russia, and central asia using landsat, random forest, and google earth engine

AR Phalke, M Özdoğan, PS Thenkabail… - ISPRS Journal of …, 2020 - Elsevier
Accurate and timely information on croplands is important for environmental, food security,
and policy studies. Spatially explicit cropland datasets are also required to derive …

Google Earth Engine for large-scale land use and land cover map**: An object-based classification approach using spectral, textural and topographical factors

H Shafizadeh-Moghadam, M Khazaei… - GIScience & Remote …, 2021 - Taylor & Francis
Map** the distribution and type of land use and land cover (LULC) is essential for
watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle …

[HTML][HTML] Forecasting disruptions in global food value chains to tackle food insecurity: The role of AI and big data analytics–A bibliometric and scientometric analysis

P Tamasiga, H Onyeaka, M Bakwena… - Journal of Agriculture …, 2023 - Elsevier
Globalization and interconnected supply chains have led to complex disruptions in global
value chains, caused by various factors such as natural disasters, climate events …

A review of regional and Global scale Land Use/Land Cover (LULC) map** products generated from satellite remote sensing

Y Wang, Y Sun, X Cao, Y Wang, W Zhang… - ISPRS Journal of …, 2023 - Elsevier
Abstract Land Use and Land Cover (LULC) map** products are essential for various
environmental studies, including ecological environmental assessments, resource …

Use and adaptations of machine learning in big data—Applications in real cases in agriculture

A Cravero, S Sepúlveda - Electronics, 2021 - mdpi.com
The data generated in modern agricultural operations are provided by diverse elements,
which allow a better understanding of the dynamic conditions of the crop, soil and climate …