Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture
Application of Machine-Learning Algorithms on Multispectral Data
Bibliographic Data
| ID | 22033262 |
|---|---|
| Authors | Muhammad Fawad Akbar Khan (0000-0001-9921-9405, University of Engineering and Technology Peshawar), Khan Muhammad (0000-0002-0005-8945, University of Engineering and Technology Peshawar, corresponding author), Shahid Bashir (0000-0002-4740-7567, University of Engineering and Technology Peshawar), Shahab Ud Din (0000-0002-3659-239X, University of Engineering and Technology Peshawar), Muhammad Hanif (0000-0002-0417-4628, University of Peshawar) |
| Year | 2021 |
| Volume | 10 |
| Issue | 2 |
| Pages | 58 |
| Publication date | 2021-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10020058 |
| OpenAlex | W3128752144 |
| Language | EN |
| References cited | 46 |
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
Algorithm · Geochemistry · Geologic map · Geomorphology · Lithology · Multispectral image · Naive Bayes classifier · Random forest · Remote sensing · Support vector machine · Computer Science · Geochemistry and Geologic Mapping · Remote-Sensing Image Classification · Soil Geostatistics and Mapping · Artificial Intelligence · Geology
| Citation velocity | historical |
|---|---|
| Highly cited | No |