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 …

Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review

S Peyghambari, Y Zhang - Journal of Applied Remote Sensing, 2021 - spiedigitallibrary.org
Hyperspectral imaging has been used in a variety of geological applications since its advent
in the 1970s. In the last few decades, different techniques have been developed by …

A hyperspectral evaluation approach for quantifying salt-induced weathering of sandstone

H Yang, C Chen, J Ni, S Karekal - Science of The Total Environment, 2023 - Elsevier
Salt-induced weathering is a common phenomenon in stone relics, and its traditional
artificial evaluation of severity is greatly affected by subjective consciousness and lacks …

A review on advancements in lithological mapping utilizing machine learning algorithms and remote sensing data

MA El-Omairi, A El Garouani - Heliyon, 2023 - cell.com
Lithological mapping is a fundamental undertaking in geological research, mineral resource
exploration, and environmental management. However, conventional methods for …

An ensemble approach of feature selection and machine learning models for regional landslide susceptibility mapping in the arid mountainous terrain of Southern …

C Kumar, G Walton, P Santi, C Luza - Remote Sensing, 2023 - mdpi.com
This study evaluates the utility of the ensemble framework of feature selection and machine
learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic …

Multi-stage corn yield prediction using high-resolution UAV multispectral data and machine learning models

C Kumar, P Mubvumba, Y Huang, J Dhillon, K Reddy - Agronomy, 2023 - mdpi.com
Timely and cost-effective crop yield prediction is vital in crop management decision-making.
This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation …

Effective delineation of rare metal-bearing granites from remote sensing data using machine learning methods: A case study from the Umm Naggat Area, Central …

MA Abdelkader, Y Watanabe, A Shebl… - Ore Geology …, 2022 - Elsevier
Albitized granite (ABG) is considered as one of the most significant hosts of rare metals
(RMs). Consequently, adequate recognition of ABG through proper lithological …

[HTML][HTML] PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert: Evaluating the support vector machine, random forest, and XG boost …

A Shebl, D Abriha, AS Fahil, HA El-Dokouny… - Ore Geology …, 2023 - Elsevier
In essence, targeting mineralization necessitates exact structural delineation and thorough
lithological mapping. The latter is still a challenge for geologists and its lack hinders …

Comparison of high-resolution NAIP and unmanned aerial vehicle (UAV) imagery for natural vegetation communities classification using machine learning …

P Bhatt, AL Maclean - GIScience & Remote Sensing, 2023 - Taylor & Francis
To map and manage forest vegetation including wetland communities, remote sensing
technology has been shown to be a valid and widely employed technology. In this paper …

[HTML][HTML] Lithological mapping enhancement by integrating Sentinel 2 and gamma-ray data utilizing support vector machine: A case study from Egypt

A Shebl, M Abdellatif, M Hissen, MI Abdelaziz… - International Journal of …, 2021 - Elsevier
Hybrid data fusion mostly gives a better diagnosis to lithological units compared to single-
source mapping techniques. Rock unit discrimination depends mainly on variations in the …