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001-es BibID:	BIBFORM084936
035-os BibID:	(WoS)000539535700067 (Scopus)85083768890
Első szerző:	Phinzi, Kwanele
Cím:	Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach / Kwanele Phinzi, Abriha Dávid, Bertalan László, Holb Imre, Szabó Szilárd
Dátum:	2020
ISSN:	2220-9964
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk machine learning gully erosion pan sharpening feature extraction
Megjelenés:	ISPRS International Journal of Geo-Information. - 9 : 4 (2020), p. 1-20. -
További szerzők:	Abriha Dávid (1995-) (geográfus) Bertalan László (1989-) (geográfus) Holb Imre (1973-) (agrármérnök) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	ED_18-1-2019-0028 Egyéb
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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. - 74 : 4 (2025), p. 399-357. -
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 DOI
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001-es BibID:	BIBFORM119489
035-os BibID:	(Scopus)85187957595 (WoS)001207916000001
Első szerző:	Szabó Szilárd (geográfus)
Cím:	Classification Assessment Tool: A program to measure the uncertainty of classification models in terms of class-level metrics / Szilárd Szabó, Imre J. Holb, Vanda Éva Abriha-Molnár, Gábor Szatmári, Sudhir Kumar Singh, Dávid Abriha
Dátum:	2024
ISSN:	1568-4946
Megjegyzések:	Accuracy assessments are important steps of classifications and get higher relevance with the soar of machine and deep learning techniques. We provided a method for quick model evaluations with several options: calculate the class level accuracy metrics for as many models and classes as needed; calculate model stability using random subsets of the testing data. The outputs are single calculations, summaries of the repetitions, and/or all accuracy results per repetitions. Using the application, we demonstrated the possibilities of the function and analyzed the accuracies of three experiments. We found that some popular metrics, the binary Overall Accuracy, Sensitivity, Precision, and Specificity, as well as ROC curve, can provide false results when the true negative cases dominate. F1-score, Intersection over Union and the Matthews correlation coefficient were reliable in all experiments. Medians and interquartile ranges (IQR) of the repeated sampling from the testing dataset showed that IQR were small when a model was almost perfect or completely unacceptable; thus, IQR reflected the model stability, reproducibility. We found that there were no general, statistically justified relationship with the median and IQR, furthermore, correlations of accuracy metrics varied by experiments, too. Accordingly, a multi-metric evaluation is suggested instead of a single metric.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Model evaluation Model stability Testing Repetitions Python
Megjelenés:	Applied Soft Computing. - 155 (2024), p. 1-15. -
További szerzők:	Holb Imre (1973-) (agrármérnök) Molnár Vanda Éva (1994-) (környezetkutató) Szatmári Gábor (1988-) (geográfus) Singh, Sudhir Kumar (1970-) (geográfus) Abriha Dávid (1995-) (geográfus)
Pályázati támogatás:	K 138079 Egyéb KKP 144068 OTKA K 138503 OTKA K 131478 OTKA
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001-es BibID:	BIBFORM123652
035-os BibID:	(Scopus)85202727566 (WoS)001302328300001
Első szerző:	Szopos Noémi Mária (geográfus)
Cím:	Flood risk assessment of a small river with limited available data / Noémi Mária Szopos, Imre J. Holb, Abriha Dávid, Szilárd Szabó
Dátum:	2024
ISSN:	2366-3286 2366-3294
Megjegyzések:	Flood risk modeling of small watercourses is challenging when only limited input data are available. Therefore, this study assessed the flood characteristics of a small river (Tarna River: entire watershed-C, upper-VS, middle-TMS, and lower section-TOS) from 1990 to 2019. The assessment focused on modeling, model calibration, and validation using feature event-based time-series data in data-scarce environments. We showed that since the 2000s, the number of high-water levels above 250 cm, and the frequency of three flood types had increased. Flood simulation results showed the largest flooded area in the TMS section, followed by the VS, and then the TOS. The outcomes from the VS, TMS, and TOS sections did not exhibit superior performance compared to the C area. Models performed well for larger flood events, with Kling Gupta Efficiency corresponding well to NRMSE and Nash-Sutcliffe efficiency metrics. Accordingly, flood events characterized by the longest duration and high-water levels yielded outstanding results across all areas, followed by moderate flood events with good accuracy. Normal water level events exhibited significant deviations from the reference across all sections. In summary, despite the event-based modeling challenges in data-limited environments, such models can still mitigate potential flood events and improve decision-making processes.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Hydrological modeling HEC-RAS Model accuracy Flood events
Megjelenés:	Spatial Information Research. - 32 (2024), p. 787-800. -
További szerzők:	Holb Imre (1973-) (agrármérnök) Abriha Dávid (1995-) (geográfus) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	RRF-2.3.1-21-2022-00008 Egyéb
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