[HTML][HTML] 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 …

[HTML][HTML] Finer-resolution map** of global land cover: Recent developments, consistency analysis, and prospects

L Liu, X Zhang, Y Gao, X Chen, X Shuai… - Journal of Remote …, 2021 - spj.science.org
Land-cover map** is one of the foundations of Earth science. As a result of the combined
efforts of many scientists, numerous global land-cover (GLC) products with a resolution of 30 …

[HTML][HTML] Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin

MG Selvaraj, A Vergara, F Montenegro, HA Ruiz… - ISPRS Journal of …, 2020 - Elsevier
Front-line remote sensing tools, coupled with machine learning (ML), have a significant role
in crop monitoring and disease surveillance. Crop type classification and a disease early …

Impacts of droughts and floods on croplands and crop production in Southeast Asia–An application of Google Earth Engine

M Venkatappa, N Sasaki, P Han, I Abe - Science of the Total Environment, 2021 - Elsevier
While droughts and floods have intensified in recent years, only a handful of studies have
assessed their impacts on croplands and production in Southeast Asia. Here, we used the …

Future scenarios of land use/land cover (LULC) based on a CA-markov simulation model: case of a mediterranean watershed in Morocco

M Beroho, H Briak, EK Cherif, I Boulahfa, A Ouallali… - Remote Sensing, 2023 - mdpi.com
Modeling of land use and land cover (LULC) is a very important tool, particularly in the
agricultural field: it allows us to know the potential changes in land area in the future and to …

Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network

F Waldner, FI Diakogiannis - Remote sensing of environment, 2020 - Elsevier
Applications of digital agricultural services often require either farmers or their advisers to
provide digital records of their field boundaries. Automatic extraction of field boundaries from …

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 …

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 …

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the …

MK Gumma, PS Thenkabail, PG Teluguntla… - GIScience & Remote …, 2020 - Taylor & Francis
ABSTRACT The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has
a staggering 900 million people (~ 43% of the population) who face food insecurity or severe …

Large-scale rice map** under different years based on time-series Sentinel-1 images using deep semantic segmentation model

P Wei, D Chai, T Lin, C Tang, M Du, J Huang - ISPRS journal of …, 2021 - Elsevier
Identifying spatial distribution of crop planting in large-scale is one of the most significant
applications of remote sensing imagery. As an active remote sensing system, synthetic …