<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling early identification, prediction, and description of environmental risks. The current work analyses the advances in deep learning for urban environmental hazard assessments and disaster studies to provide monitoring, management, and mitigation measures. It reports the improvement in self-supervised learning, transformer architectures, persistent learning, attention mechanisms, adversarial robustness, associated learning, meta-learning, and multimodal learning within the domain of urban environmental hazard analysis. These approaches allow the creation of robust models for handling vast data volumes, facilitating early detection, prediction, and characterisation of diverse environmental threats. This trends analysis for urban applications will bring insights for connecting deep-learning models for effective and proactive approaches to tackle urban environmental hazards and disasters.</p>
</abstract>

Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling early identification, prediction, and description of environmental risks. The current work analyses the advances in deep learning for urban environmental hazard assessments and disaster studies to provide monitoring, management, and mitigation measures. It reports the improvement in self-supervised learning, transformer architectures, persistent learning, attention mechanisms, adversarial robustness, associated learning, meta-learning, and multimodal learning within the domain of urban environmental hazard analysis. These approaches allow the creation of robust models for handling vast data volumes, facilitating early detection, prediction, and characterisation of diverse environmental threats. This trends analysis for urban applications will bring insights for connecting deep-learning models for effective and proactive approaches to tackle urban environmental hazards and disasters.

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

University at Albany (UAlbany), State University of New York (SUNY)

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling...

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Article Category: Review

Data publikacji: 23 maj 2024

Zakres stron: -

Otrzymano: 28 gru 2023

DOI: https://doi.org/10.2478/ijssis-2024-0014

Słowa kluczowe
Deep learning, environment, deep neural network, remote sensing, geospatial big data analytics

© 2024 Deepak Kumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Article Category: Review

Data publikacji: 23 maj 2024

Zakres stron: -

Otrzymano: 28 gru 2023

DOI: https://doi.org/10.2478/ijssis-2024-0014

Słowa kluczoweDeep learning, environment, deep neural network, remote sensing, geospatial big data analytics

© 2024 Deepak Kumar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Deep learning, environment, deep neural network, remote sensing, geospatial big data analytics