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001-es BibID:	BIBFORM131970
035-os BibID:	(Scopus)105013652193
Első szerző:	Abdelmajeed, Adam Elrasheed Ali (Geologist)
Cím:	Spatial expansion of artisanal and small-scale gold mining nearby the Nile River, Sudan and its potential environmental impacts: Insights from Planetscope data and machine learning / Abdelmajeed A. Elrasheed, Yousif Y. Obaid, Szilárd Szabó
Dátum:	2025
ISSN:	2667-0100
Megjegyzések:	Artisanal and small-scale gold mining (ASGM) has dramatically expanded along the Nile River, North Sudan; however, the rates and environmental impacts were not sufficiently assessed. We aimed to use PlanetScope data to detect and map ASGM and highlight its environmental impacts around the Nile River, North Sudan, using the random forest (RF) classifier in three steps. First, a visual inspection and analysis were performed to evaluate how distinguishable ASGM sites are from rock units/geological features in color composites; then, reference data were collected from processed images for training and testing, and supervised classification was conducted using binary and multiclass RF classifiers. RF and PlanetScope data were efficient in discriminating ASGM sites with high overall accuracy (0.84-0.92). The binary approach ensured higher accuracy over the multiclass approach, but the latter helped to understand the spatial distribution of illegal mining. Our findings showed that ASGM areas significantly expanded from 50 ha (2016) to 90 ha (2021) and 125 ha (2024). Additionally, we highlighted the environmental risks associated with the development of ASGM in the region. The results can help decision makers and stakeholders to obtain better information on the environment, and the methodology helps to monitor ASGM activities.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Remote sensing PlanetScope Random forest Environmental impacts
Megjelenés:	Environmental Challenges. - 20 (2025), p. 1-12. -
További szerzők:	Obaid, Yousif Y. Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	K 138079 NKFIH
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM125617
Első szerző:	Abdelmajeed, Adam Elrasheed Ali (Geologist)
Cím:	Spectra Analysis of PRISMA for Detecting Iron Alteration Zones Associated with Gold Mineralization in Red Sea Hills N-E Sudan / Abdelmajeed A. Elrasheed, Mohammadreza Ojani, Zeynab Kougir Chegini, Abazar M. A. Daoud, Musa. M. M. Mina, Szabó Szilárd
Dátum:	2024
Megjegyzések:	This work presents a comprehensive study utilizing Precursore Iperspettrale della Missione Applicativa (PRISMA) hyperspectral imagery in detecting and mapping iron alteration zones associated with gold mineralization in the Red Sea Hills of North-eastern Sudan. The application of hyperspectral color composite and bands rationing techniques aids in enhancing spectral signatures indicative of mineral alterations, thereby facilitating accurate mapping of potential gold mineralization zones. Our study employs a systematic approaches (colour composite and band ratio) integrating field data collection and advanced spectral analysis techniques to characterize and map alteration zones. Band ratios band 30/ band 20 (0.637 ? 0.551 ?m) and band 20 / band 9 (0.551 ? 0.468 ?m) obtained best result. The results demonstrate the effectiveness of PRISMA hyperspectral data for delineating gold mineralization-related iron alteration zones, thus offering valuable insights for mineral exploration in the region.
ISBN:	978-963-490-619-3
Tárgyszavak:	Természettudományok Földtudományok előadáskivonat könyvrészlet PRISMA Iron alteration Band ratio Red Sea Hills
Megjelenés:	Az elmélet és a gyakorlat találkozása a térinformatikában = Theory meets practice in GIS : Debreceni Egyetem Térinformatikai Konferencia és Szakkiállítás / szerk. Abriha-Molnár Vanda Éva. - p. 99-106. -
További szerzők:	Ojani, Mohammadreza (1990-) Zeynab Kougir Chegini Daoud Abazar Mohamed Ahmed (1991-) (földtudományi kutató, geológus, geográfus) Musa. M. M. Mina Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	NKFI K138079 Other
Internet cím:	Szerző által megadott URL Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM125614
Első szerző:	Abdelmajeed, Adam Elrasheed Ali (Geologist)
Cím:	Comparing the Capability of Multi- and Hyperspectral Remote Sensing Data in Lithological Mapping Using Machine Learning Algorithms: A Case Study from Sudan / Abdelmajeed A. Elrasheed, Szilard Szabo
Dátum:	2024
Megjegyzések:	Lithological mapping is vital approach in a variety of geological applications such as mineral exploration, study and understand the origin and tectonic setting for the area under investigation. However, it's challenging to conduct this task in the traditional way mainly in remote areas characterised by rugged topography such as Red Sea. Recently, the integration of remote sensing and machine learning provide an effective quick and low-cost approach in lithological mapping. The aim of this research was to compare the potentiality of Landsat 9 multi-spectral and PRISMA hyperspectral remote sensing data in lithological mapping in Red Sea Area, N-E Sudan. We employed Random Forest (RF) and Naïve Bayes (NB) machine learning algorithms. The study area is covered mainly by; Ophiolite, Meta-volcanic, Marble, Granitoids, Altered rocks and superficial deposits. The results showed that, PRISMA hyperspectral data obtained better classification result compare to the Landsat 9 multi-spectral data using both classifiers. Also, our finding proved that, RF out performance NB in the multi- and hyperspectral datasets. E.g. NB classifier gave Kappa 0.90 and 0.80 while RF provided 0.95 and 0.90 for PRISMA and Landsat 9 respectively. Moreover, the OA was 0.96 and 0.92 for PRISMA and 0.92 and 0.83 for Landsat 9. We firmly recommend this approach as an effective method for mapping lithology in the area where the rocks are cropped out and free vegetation cover regions.
Tárgyszavak:	Természettudományok Földtudományok előadáskivonat könyvrészlet
Megjelenés:	EGU General Assembly 2024. - p. EGU24-5443
További szerzők:	Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	NKFI K138079 Other
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM132954
Első szerző:	Szabó Szilárd (geográfus)
Cím:	Lithological mapping with pseudo-labeling: promise or overestimation in data-scarce settings? / Szilárd Szabó, Abdelmajeed A. Elrasheed, Lilla Kovács, Imre J. Holb, Szilárd Likó, Dávid Abriha
Dátum:	2025
ISSN:	2064-5031 2064-5147
Megjegyzések:	Reference data are the most crucial points in model building. In geoscience, a scarcity of sufficient reference data is common. Pseudo-labelling (PL), i.e., incorporating high-probability data in the model building process, offers a potential solution. We aimed to reveal the efficiency of PL in lithological mapping in a vegetation-free arid region of Sudan. Random Forest (RF) and Multiple Adaptive Regression Splines (MARS) were used to classify a Landsat 9 image. Reference data were collected during field work and visual interpretation. Image processing yielded classified maps with associated probability layers, from which 1000 additional traditional samples (PL data) were extracted at a 95% probability. A detailed accuracy assessment was conducted, and accuracy measures were evaluated using statistical analysis and visual inspection. MARS was found to be an ambiguous classifier because the probability was too optimistic related to the overall accuracy (OA) (81% of samples had above 99% probability, OA=98.2%) compared to RF (21% above 99%, OA=98.1%); that is, despite the high probability, the accuracy improvement was only 0.1%. At the class level, the correlation between probability and the F1-score was low (0.21%). The original and PL-based models resulted in different maps with improved accuracy, although the new model version showed lower probability values for both the classifiers. Visual inspection proved essential for better insights into the spatial patterns: expert knowledge is crucial for controlling the occurrence of rock types and identifying false classifications. The main finding is that probability should be handled carefully, as it does not guarantee high model performance in classification, although the PL approach can lead to more reliable maps
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény hazai lapban folyóiratcikk Random Forest Multiple Adaptive Regression Splines self-training probability data augmentation
Megjelenés:	Hungarian Geographical Bulletin. - "Accepted by Publisher" (2025), p. 1-29. -
További szerzők:	Abdelmajeed, Adam Elrasheed Ali (1988-) (Geologist) Kovács Lilla (Msc hallgató) Holb Imre (1973-) (agrármérnök) Likó Szilárd Balázs Abriha Dávid (1995-) (geográfus)
Pályázati támogatás:	TÁMOP-4.1.1.C-13/1/KONV-2014-0001 SUPPORT
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat
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