Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources
This paper reviews the most important information fusion data-driven algorithms based on
Machine Learning (ML) techniques for problems in Earth observation. Nowadays we …
Machine Learning (ML) techniques for problems in Earth observation. Nowadays we …
Multimodal classification: Current landscape, taxonomy and future directions
Multimodal classification research has been gaining popularity with new datasets in
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …
Classification of weather phenomenon from images by using deep convolutional neural network
H **ao, F Zhang, Z Shen, K Wu… - Earth and Space …, 2021 - Wiley Online Library
Weather phenomenon recognition notably affects many aspects of our daily lives, for
example, weather forecast, road condition monitoring, transportation, agriculture, forestry …
example, weather forecast, road condition monitoring, transportation, agriculture, forestry …
Meta-analysis of deep neural networks in remote sensing: A comparative study of mono-temporal classification to support vector machines
Deep learning methods have recently found widespread adoption for remote sensing tasks,
particularly in image or pixel classification. Their flexibility and versatility has enabled …
particularly in image or pixel classification. Their flexibility and versatility has enabled …
Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data
The distribution coefficient (K d) plays a crucial role in predicting the migration behavior of
radionuclides in the soil environment. However, K d depends on the complexities of …
radionuclides in the soil environment. However, K d depends on the complexities of …
Multimodal deep learning models incorporating the adsorption characteristics of the adsorbent for estimating the permeate flux in dynamic membranes
Dynamic membranes (DMs) can improve the overall efficiency and performance of water-
treatment processes. However, DM modeling studies are limited by the constraint of …
treatment processes. However, DM modeling studies are limited by the constraint of …
Development of an ontology for construction carbon emission tracking and evaluation
Significant carbon emissions from construction sites necessitate improved management
through integrating diverse data sources. Ontologies are widely employed for data …
through integrating diverse data sources. Ontologies are widely employed for data …
The development and application of machine learning in atmospheric environment studies
Machine learning (ML) plays an important role in atmospheric environment prediction,
having been widely applied in atmospheric science with significant progress in algorithms …
having been widely applied in atmospheric science with significant progress in algorithms …
A novel method for ground-based cloud image classification using transformer
X Li, B Qiu, G Cao, C Wu, L Zhang - Remote Sensing, 2022 - mdpi.com
In recent years, convolutional neural networks (CNNs) have achieved competitive
performance in the field of ground-based cloud image (GCI) classification. Proposed CNN …
performance in the field of ground-based cloud image (GCI) classification. Proposed CNN …
UATNet: U-shape attention-based transformer net for meteorological satellite cloud recognition
Cloud recognition is a basic task in ground meteorological observation. It is of great
significance to accurately identify cloud types from long-time-series satellite cloud images for …
significance to accurately identify cloud types from long-time-series satellite cloud images for …