Deep learning in environmental remote sensing: Achievements and challenges
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …
environmental remote sensing research. With an increasing amount of “big data” from earth …
Sensors, features, and machine learning for oil spill detection and monitoring: A review
Remote sensing technologies and machine learning (ML) algorithms play an increasingly
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …
Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model
Leaf area index (LAI) is a terrestrial essential climate variable that is required in a variety of
ecosystem and climate models. The Global LAnd Surface Satellite (GLASS) LAI product has …
ecosystem and climate models. The Global LAnd Surface Satellite (GLASS) LAI product has …
Land surface and air temperature dynamics: The role of urban form and seasonality
Due to the scarcity of air temperature (T a) observations, urban heat studies often rely on
satellite-derived Land Surface Temperature (LST) to characterise the near-surface thermal …
satellite-derived Land Surface Temperature (LST) to characterise the near-surface thermal …
Long-term trends of surface and canopy layer urban heat island intensity in 272 cities in the mainland of China
The canopy layer urban heat island (CLUHI) and surface urban heat island (SUHI) refer to
higher canopy layer and land surface temperatures in urban areas than in rural areas …
higher canopy layer and land surface temperatures in urban areas than in rural areas …
An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran
Traditional soil salinity studies are time-consuming and expensive, especially over large
areas. This study proposed an innovative deep learning convolutional neural network (DL …
areas. This study proposed an innovative deep learning convolutional neural network (DL …
A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning
Most algorithms for land surface temperature (LST) retrieval depend on acquiring prior
knowledge. To overcome this drawback, we propose a novel LST retrieval method based on …
knowledge. To overcome this drawback, we propose a novel LST retrieval method based on …
A deep learning method of water body extraction from high resolution remote sensing images with multisensors
M Li, P Wu, B Wang, H Park, H Yang… - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
Water body extraction from remote sensing images is an important task. Deep learning has
become a more popular method for extracting water bodies from remote sensing images …
become a more popular method for extracting water bodies from remote sensing images …
Remote sensing data fusion with generative adversarial networks: State-of-the-art methods and future research directions
In the past decades, remote sensing (RS) data fusion has always been an active research
community. A large number of algorithms and models have been developed. Generative …
community. A large number of algorithms and models have been developed. Generative …
A global dataset of daily near-surface air temperature at 1-km resolution (2003–2020)
Near-surface air temperature (Ta) is a key variable in global climate studies. A global
gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable …
gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable …