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Conservation of Natural Resources

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Deepdive: Leveraging Pre-trained Deep Learning for Deep-Sea ROV Biota Identification in the Great Barrier Reef.

Scientific data
Understanding and preserving the deep sea ecosystems is paramount for marine conservation efforts. Automated object (deep-sea biota) classification can enable the creation of detailed habitat maps that not only aid in biodiversity assessments but als...

Mapping of temperate upland habitats using high-resolution satellite imagery and machine learning.

Environmental monitoring and assessment
Upland habitats provide vital ecological services, yet they are highly threatened by natural and anthropogenic stressors. Monitoring these vulnerable habitats is fundamental for conservation and involves determining information about their spatial lo...

A deep learning classification framework for research methods of marine protected area management.

Journal of environmental management
The latest emerging transdisciplinary marine protected area (MPA) research scheme requires efficient approaches for theoretically based and data-driven method integration. However, due to the rapid development and diversification of research methods,...

To save wildlife from fences, scientists turn to AI.

Science (New York, N.Y.)
The research uses aerial imagery to pinpoint structures that could block migratory pronghorn and other wildlife.

Coping with the tale of natural resources and environmental inequality: an application of the machine learning tools.

Environmental science and pollution research international
With the rising momentum according to the environmentalist voices seeking climate justice for more equity and the importance of encouraging environmental justice mechanisms and tools, in this perspective, the objective of this study is to analyze in ...

From expansion to efficiency: Machine learning-based forecasting of Japan's building material stocks under demographic declines.

The Science of the total environment
Japan's unique demographic trajectory, marked by population decline and aging, coupled with continued urbanization, presents distinct challenges for aligning built environment capacity with resource efficiency. This study aims to investigate the hist...

Early warning of trends in commercial wildlife trade through novel machine-learning analysis of patent filing.

Nature communications
Unsustainable wildlife trade imperils thousands of species, but efforts to identify and reduce these threats are hampered by rapidly evolving commercial markets. Businesses trading wildlife-derived products innovate to remain competitive, and the pat...

Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota.

Environmental management
The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in miti...

An investment decision framework for offshore CCUS project under interval-valued fermatean fuzzy environment.

Environmental technology
Carbon Capture, Utilization and Storage (CCUS) is an indispensable technology for achieving a net-zero emission society. The offshore CCUS project is still in its infancy. To promote its sustainable development, developing a comprehensive framework f...

Man versus machine: cost and carbon emission savings of 4G-connected Artificial Intelligence technology for classifying species in camera trap images.

Scientific reports
Timely and accurate detection and identification of species are crucial for monitoring wildlife for conservation and management. Technological advances, including connectivity of camera traps to mobile phone networks and artificial intelligence (AI) ...