Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
[HTML][HTML] Nighttime light remote sensing for urban applications: Progress, challenges, and prospects
Nighttime light (NTL) remote sensing data offer unique capabilities to characterize both the
extent and intensity of human activities and have been extensively used to understand …
extent and intensity of human activities and have been extensively used to understand …
Comparison of three machine learning algorithms using google earth engine for land use land cover classification
Abstract Google Earth Engine (GEE) is presently the most innovative international open-
source platform for the advanced-level analysis of geospatial big data. In this study, we used …
source platform for the advanced-level analysis of geospatial big data. In this study, we used …
Remote sensing image classification using an ensemble framework without multiple classifiers
Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an
effective method for improving remote sensing classification accuracy. Although these …
effective method for improving remote sensing classification accuracy. Although these …
A spectral-temporal constrained deep learning method for tree species mapping 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 mapping of tree species through remote …
expansion during the past few decades. Accurate mapping of tree species through remote …
Application of deep learning in multitemporal remote sensing image classification
X Cheng, Y Sun, W Zhang, Y Wang, X Cao, Y Wang - Remote Sensing, 2023 - mdpi.com
The rapid advancement of remote sensing technology has significantly enhanced the
temporal resolution of remote sensing data. Multitemporal remote sensing image …
temporal resolution of remote sensing data. Multitemporal remote sensing image …
Enhanced crop classification through integrated optical and SAR data: a deep learning approach for multi-source image fusion
Agricultural crop mapping has advanced over the last decades due to improved approaches
and the increased availability of image datasets at various spatial and temporal resolutions …
and the increased availability of image datasets at various spatial and temporal resolutions …
A novel fuzzy Harris hawks optimization-based supervised vegetation and bare soil prediction system for Javadi Hills, India
SN MohanRajan, A Loganathan - Arabian Journal of Geosciences, 2023 - Springer
For several decades, researchers throughout the world have been motivated and
contributed to the research on land use/land cover (LU/LC) change prediction analysis. This …
contributed to the research on land use/land cover (LU/LC) change prediction analysis. This …
Comparison of big-leaf and two-leaf light use efficiency models for GPP simulation after considering a radiation scalar
Light use efficiency (LUE) models, mainly including the big-leaf (BL) and two-leaf (TL)
categories, are efficient approaches to simulate gross primary productivity (GPP). Recently …
categories, are efficient approaches to simulate gross primary productivity (GPP). Recently …
Deep learning application for crop classification via multi-temporal remote sensing images
Q Li, J Tian, Q Tian - Agriculture, 2023 - mdpi.com
The combination of multi-temporal images and deep learning is an efficient way to obtain
accurate crop distributions and so has drawn increasing attention. However, few studies …
accurate crop distributions and so has drawn increasing attention. However, few studies …