scholarly journals Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

2015 ◽  
Vol 7 (12) ◽  
pp. 16398-16421 ◽  
Author(s):  
Iftikhar Ali ◽  
Felix Greifeneder ◽  
Jelena Stamenkovic ◽  
Maxim Neumann ◽  
Claudia Notarnicola
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Remote Sensing Data ◽  
Learning Approaches ◽  
Sensing Data

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

scholarly journals A review of machine learning in processing remote sensing data for mineral exploration

2022 ◽  
Vol 268 ◽  
pp. 112750
Author(s):  
Hojat Shirmard ◽  
Ehsan Farahbakhsh ◽  
R. Dietmar Müller ◽  
Rohitash Chandra
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Mineral Exploration ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

2017 ◽  
Vol 8 (2) ◽  
pp. 1080-1102 ◽  
Author(s):  
Hossein Mojaddadi ◽  
Biswajeet Pradhan ◽  
Haleh Nampak ◽  
Noordin Ahmad ◽  
Abdul Halim bin Ghazali
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Risk Assessment ◽  
Flood Risk ◽  
Remote Sensing Data ◽  
Flood Risk Assessment ◽  
Sensing Data ◽  
Ensemble Machine Learning

Improving forest detection with machine learning in remote sensing data

2021 ◽  
pp. 100654
Author(s):  
Gabriel D. Caffaratti ◽  
Martín G. Marchetta ◽  
Leonardo D. Euillades ◽  
Pablo A. Euillades ◽  
Raymundo Q. Forradellas
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data
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