Deep learning classification of land cover and crop types using remote sensing data
Deep learning (DL) is a powerful state-of-the-art technique for image processing including
remote sensing (RS) images. This letter describes a multilevel DL architecture that targets …
remote sensing (RS) images. This letter describes a multilevel DL architecture that targets …
SAR image analysis techniques for flood area mapping-literature survey
R Manavalan - Earth Science Informatics, 2017 - Springer
Flood area mapping is an integral part of disaster management operation which gets value
when the details about inundated region has been made available in real time mode as well …
when the details about inundated region has been made available in real time mode as well …
Geospatial sensor web: A cyber-physical infrastructure for geoscience research and application
In the last half-century, geoscience research has advanced due to multidisciplinary
technologies, among which Information and Communication Technology (ICT) has played a …
technologies, among which Information and Communication Technology (ICT) has played a …
Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model
Abstract Knowledge on geographical location and distribution of crops at global, national
and regional scales is an extremely valuable source of information for many applications …
and regional scales is an extremely valuable source of information for many applications …
Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine
Ukraine is one of the most developed agricultural countries in the world. For many
applications, it is extremely important to provide reliable crop maps taking into account …
applications, it is extremely important to provide reliable crop maps taking into account …
Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models
Ukraine is one of the most developed agriculture countries and one of the biggest crop
producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level …
producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level …
[HTML][HTML] Enhancing crop yield prediction utilizing machine learning on satellite-based vegetation health indices
Accurate crop yield forecasting is essential in the food industry's decision-making process,
where vegetation condition index (VCI) and thermal condition index (TCI) coupled with …
where vegetation condition index (VCI) and thermal condition index (TCI) coupled with …
Flood hazard and flood risk assessment using a time series of satellite images: A case study in Namibia
In this article, the use of time series of satellite imagery to flood hazard mapping and flood
risk assessment is presented. Flooded areas are extracted from satellite images for the flood …
risk assessment is presented. Flooded areas are extracted from satellite images for the flood …
Regional scale crop mapping using multi-temporal satellite imagery
One of the problems in dealing with optical images for large territories (more than 10,000 sq.
km) is the presence of clouds and shadows that result in having missing values in data sets …
km) is the presence of clouds and shadows that result in having missing values in data sets …
[PDF][PDF] Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine
Winter wheat crop yield forecasting at national, regional and local scales is an extremely
important task. This paper aims at assessing the efficiency (in terms of prediction error …
important task. This paper aims at assessing the efficiency (in terms of prediction error …