A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications
The globe's population is increasing day by day, which causes the severe problem of
organic food for everyone. Farmers are becoming progressively conscious of the need to …
organic food for everyone. Farmers are becoming progressively conscious of the need to …
Recent applications of Landsat 8/OLI and Sentinel-2/MSI for land use and land cover map**: A systematic review
Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2
MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land …
MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land …
Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data
While the application of machine-learning algorithms has been highly simplified in the last
years due to their well-documented integration in commonly used statistical programming …
years due to their well-documented integration in commonly used statistical programming …
Detailed dynamic land cover map** of Chile: Accuracy improvement by integrating multi-temporal data
Stretching over 4300 km north to south, Chile is a special country with complicated
landscapes and rich biodiversity. Accurate and timely updated land cover map of Chile in …
landscapes and rich biodiversity. Accurate and timely updated land cover map of Chile in …
Crop classification using multi-temporal Sentinel-2 data in the Shiyang River Basin of China
Z Yi, L Jia, Q Chen - Remote Sensing, 2020 - mdpi.com
Timely and accurate crop classification is of enormous significance for agriculture
management. The Shiyang River Basin, an inland river basin, is one of the most prominent …
management. The Shiyang River Basin, an inland river basin, is one of the most prominent …
Spatial machine learning: new opportunities for regional science
K Kopczewska - The Annals of Regional Science, 2022 - Springer
This paper is a methodological guide to using machine learning in the spatial context. It
provides an overview of the existing spatial toolbox proposed in the literature: unsupervised …
provides an overview of the existing spatial toolbox proposed in the literature: unsupervised …
A spectral-temporal constrained deep learning method for tree species map** of plantation forests using time series Sentinel-2 imagery
Plantation forests provide critical ecosystem services and have experienced worldwide
expansion during the past few decades. Accurate map** of tree species through remote …
expansion during the past few decades. Accurate map** of tree species through remote …
Comparison of random forest, support vector machines, and neural networks for post-disaster forest species map** of the krkonoše/karkonosze transboundary …
Mountain forests are exposed to extreme conditions (eg, strong winds and intense solar
radiation) and various types of damage by insects such as bark beetles, which makes them …
radiation) and various types of damage by insects such as bark beetles, which makes them …
Exploring the potential of multi-source unsupervised domain adaptation in crop map** using Sentinel-2 images
Accurate crop map** is critical for agricultural applications. Although studies have
combined deep learning methods and time-series satellite images to crop classification with …
combined deep learning methods and time-series satellite images to crop classification with …
High resolution landslide susceptibility map** using ensemble machine learning and geospatial big data
Landslide susceptibility represents the potential of slope failure for given geo-environmental
conditions. The existing landslide susceptibility maps suffer from several limitations, such as …
conditions. The existing landslide susceptibility maps suffer from several limitations, such as …