[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 …
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 …
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 …
[HTML][HTML] Learning spectral-indices-fused deep models for time-series land use and land cover map** in cloud-prone areas: The case of Pearl River Delta
Map** of highly dynamic changes in land use and land cover (LULC) can be hindered by
various cloudy conditions with optical satellite images. These conditions result in …
various cloudy conditions with optical satellite images. These conditions result in …
[HTML][HTML] 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 …
Deep learning for satellite image time-series analysis: A review
Earth observation (EO) satellite missions have been providing detailed images about the
state of Earth and its land cover for over 50 years. Long-term missions, such as those of …
state of Earth and its land cover for over 50 years. Long-term missions, such as those of …
[HTML][HTML] Large-scale land use/land cover extraction from Landsat imagery using feature relationships matrix based deep-shallow learning
Deep learning has demonstrated its effectiveness in capturing high-level features, with
convolutional neural networks (CNNs) excelling in remote sensing classification. However …
convolutional neural networks (CNNs) excelling in remote sensing classification. However …
Amfnet: Attention-guided multi-scale fusion network for bi-temporal change detection in remote sensing images
Z Zhan, H Ren, M ** 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 …