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[HTML][HTML] Google Earth Engine and artificial intelligence (AI): a comprehensive review
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 …
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
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 …
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
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 …
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
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 …
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
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 …
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
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 …
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
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 …
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
Accurate and timely information on croplands is important for environmental, food security,
and policy studies. Spatially explicit cropland datasets are also required to derive …
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 …
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 …
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
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 …
applications of remote sensing imagery. As an active remote sensing system, synthetic …