A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities

W Han, X Zhang, Y Wang, L Wang, X Huang… - ISPRS Journal of …, 2023 - Elsevier
Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …

Global and regional trends and drivers of fire under climate change

MW Jones, JT Abatzoglou, S Veraverbeke… - Reviews of …, 2022 - Wiley Online Library
Recent wildfire outbreaks around the world have prompted concern that climate change is
increasing fire incidence, threatening human livelihood and biodiversity, and perpetuating …

Combustion machine learning: Principles, progress and prospects

M Ihme, WT Chung, AA Mishra - Progress in Energy and Combustion …, 2022 - Elsevier
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

A review of practical ai for remote sensing in earth sciences

B Janga, GP Asamani, Z Sun, N Cristea - Remote Sensing, 2023 - mdpi.com
Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for
revolutionizing data analysis and applications in many domains of Earth sciences. This …

An improved forest fire detection method based on the detectron2 model and a deep learning approach

AB Abdusalomov, BMDS Islam, R Nasimov… - Sensors, 2023 - mdpi.com
With an increase in both global warming and the human population, forest fires have
become a major global concern. This can lead to climatic shifts and the greenhouse effect …

Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation

Z Amiri, A Heidari, NJ Navimipour - Energy, 2024 - Elsevier
With the gallo** progress of the changing climates all around the world, Machine Learning
(ML) approaches have been prevalently studied in many types of research in this area. ML is …

A review of earth artificial intelligence

Z Sun, L Sandoval, R Crystal-Ornelas… - Computers & …, 2022 - Elsevier
In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

SPH Boroujeni, A Razi, S Khoshdel, F Afghah… - Information …, 2024 - Elsevier
Wildfires have emerged as one of the most destructive natural disasters worldwide, causing
catastrophic losses. These losses have underscored the urgent need to improve public …

Flash-flood susceptibility map** based on XGBoost, random forest and boosted regression trees

R Abedi, R Costache… - Geocarto …, 2022 - Taylor & Francis
Historical exploration of flash flood events and producing flash-flood susceptibility maps are
crucial steps for decision makers in disaster management. In this article, classification and …

Machine learning for risk and resilience assessment in structural engineering: Progress and future trends

X Wang, RK Mazumder, B Salarieh… - Journal of Structural …, 2022 - ascelibrary.org
Population growth, economic development, and rapid urbanization in many areas have led
to increased exposure and vulnerability of structural and infrastructure systems to hazards …