Deep Learning for Resource and Environmental Earth Observations
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: closed (25 January 2025) | Viewed by 374
Special Issue Editors
Interests: implicit modeling; 3D prospectivity modeling; machine learning; 3D GIS
Special Issues, Collections and Topics in MDPI journals
Interests: remote imagery processing; environmental monitoring; deep learning
Interests: 3D prospectivity modelling; spatial analysis; numerical simulation; economic geology; mineral system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The transformation in resource exploration and environmental monitoring has been driven by advancements in sensing technologies, ranging from remote sensing to deep drilling data, resulting in a significant shift from being a data-poor to a data-rich field. The increasing availability of extensive Earth observation data presents immense potential for deep learning. Deep learning has reached a transformative level of maturity in Earth resource and environmental remote sensing. Its ability to automatically learn complex patterns from vast amounts of Earth observation data, combined with advances in computational power, has led to breakthroughs with numerous applications currently in active development worldwide. As this rapid advancement continues, novel deep learning techniques, from convolutional neural networks to transformer networks, are emerging, marking a new era in data-driven resource exploration and environmental Earth observation.
This Special Issue focuses on the latest advancements in Earth resource and environmental remote sensing. We invite contributions across all areas of deep learning for resource and environmental Earth observations, including deep learning models, algorithms, and innovative applications that leverage Earth observation data for resource exploration and environmental monitoring. Topics of interest include, but are not limited to, the following:
- Deep learning models for inverting Earth observation data related to resource and environmental systems.
- Deep learning models for fusing multimodal resource and environmental data.
- Deep learning models for exploring geological and resource systems.
- Deep learning-driven resource potential modeling and exploration.
- Deep learning-empowered environmental modeling, data assimilation, and parameter estimation.
- Deep learning methods for environmental monitoring.
- Deep mining of Earth observation data on resource and environmental systems.
- New insights gained through deep learning into resource and environmental systems.
- New services and applications that integrate deep neural networks with resource and environmental data.
Dr. Hao Deng
Dr. Yuebin Wang
Dr. Zhankun Liu
Guest Editors
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Keywords
- deep learning models
- resource and environmental system
- resource exploration
- resource potential modeling
- environmental monitoring
- deep mining of earth observation data
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