Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers Article Swipe
Imane Serbouti
,
Mohammed Raji
,
Mustapha Hakdaoui
,
Fouad El Kamel
,
Biswajeet Pradhan
,
Shilpa Gite
,
Abdullah Alamri
,
Khairul Nizam Abdul Maulud
,
Abhirup Dikshit
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14215498
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14215498
In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14215498
- https://www.mdpi.com/2072-4292/14/21/5498/pdf?version=1667888335
- OA Status
- gold
- Cited By
- 18
- References
- 121
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307989706
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307989706Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs14215498Digital Object Identifier
- Title
-
Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-ClassifiersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-31Full publication date if available
- Authors
-
Imane Serbouti, Mohammed Raji, Mustapha Hakdaoui, Fouad El Kamel, Biswajeet Pradhan, Shilpa Gite, Abdullah Alamri, Khairul Nizam Abdul Maulud, Abhirup DikshitList of authors in order
- Landing page
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https://doi.org/10.3390/rs14215498Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/14/21/5498/pdf?version=1667888335Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/14/21/5498/pdf?version=1667888335Direct OA link when available
- Concepts
-
Artificial intelligence, Naive Bayes classifier, Random forest, Support vector machine, Computer science, Cohen's kappa, Machine learning, Land cover, Geology, Remote sensing, Pattern recognition (psychology), Data mining, Land use, Engineering, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 6, 2023: 6Per-year citation counts (last 5 years)
- References (count)
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121Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.quantitatively | 168 |
| abstract_inverted_index.machine-learning | 17 |
| abstract_inverted_index.Dempster–Shafer | 134 |
| abstract_inverted_index.Earth-observation | 23 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5059040421 |
| countries_distinct_count | 5 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I114017466, https://openalex.org/I885383172 |
| citation_normalized_percentile.value | 0.91196684 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |