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Language Integration in Remote Sensing: Tasks, datasets, and future directions
The emerging field of vision–language models, which combines computer vision and natural
language processing (NLP), has gained significant interest and exploration. This integration …
language processing (NLP), has gained significant interest and exploration. This integration …
Deep learning-based methods for natural hazard named entity recognition
J Sun, Y Liu, J Cui, H He - Scientific reports, 2022 - nature.com
Natural hazard named entity recognition is a technique used to recognize natural hazard
entities from a large number of texts. The method of natural hazard named entity recognition …
entities from a large number of texts. The method of natural hazard named entity recognition …
Advances in deep learning recognition of landslides based on remote sensing images
Against the backdrop of global warming and increased rainfall, the hazards and potential
risks of landslides are increasing. The rapid generation of a landslide inventory is of great …
risks of landslides are increasing. The rapid generation of a landslide inventory is of great …
Shared blocks-based ensemble deep learning for shallow landslide susceptibility map**
Natural disaster impact assessment is of the utmost significance for post-disaster recovery,
environmental protection, and hazard mitigation plans. With their recent usage in landslide …
environmental protection, and hazard mitigation plans. With their recent usage in landslide …
[HTML][HTML] Automated landslide-risk prediction using web gis and machine learning models
Spatial susceptible landslide prediction is the one of the most challenging research areas
which essentially concerns the safety of inhabitants. The novel geographic information web …
which essentially concerns the safety of inhabitants. The novel geographic information web …
[HTML][HTML] A Review of Deep Learning-Based Remote Sensing Image Caption: Methods, Models, Comparisons and Future Directions
K Zhang, P Li, J Wang - Remote Sensing, 2024 - mdpi.com
Remote sensing images contain a wealth of Earth-observation information. Efficient
extraction and application of hidden knowledge from these images will greatly promote the …
extraction and application of hidden knowledge from these images will greatly promote the …
Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model
Landslide probability prediction plays an important role in understanding landslide
information in advance and taking preventive measures. Many factors can influence the …
information in advance and taking preventive measures. Many factors can influence the …
BS-LSTM: an ensemble recurrent approach to forecasting soil movements in the real world
Machine learning (ML) proposes an extensive range of techniques, which could be applied
to forecasting soil movements using historical soil movements and other variables. For …
to forecasting soil movements using historical soil movements and other variables. For …
[PDF][PDF] A hybridized deep learning method for Bengali image captioning
An omnipresent challenging research topic in computer vision is the generation of captions
from an input image. Previously, numerous experiments have been conducted on image …
from an input image. Previously, numerous experiments have been conducted on image …
Prediction of real-world slope movements via recurrent and non-recurrent neural network algorithms: a case study of the Tangni landslide
Abstract The Tangni landslide in Chamoli, India, has experienced several landslide
incidents in the recent past. Due to the fatalities and injuries caused, it is essential to predict …
incidents in the recent past. Due to the fatalities and injuries caused, it is essential to predict …