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1.

001-es BibID:BIBFORM089499
Első szerző:Abriha Dávid (geográfus)
Cím:Városi zöldfelületek osztályozása nagy felbontású PlanetScope és SkySat felvételek alapján / Abriha Dávid, Szabó Loránd, Kwanele Phinzi, Szabó Szilárd
Dátum:2020
ISBN:978-963-318-886-6
Tárgyszavak:Természettudományok Környezettudományok előadáskivonat
könyvrészlet
Megjelenés:Az elmélet és a gyakorlat találkozása a térinformatikában XI. : Theory meets practice in gis / szerk. Molnár Vanda Éva. - p. 13-16. -
További szerzők:Szabó Loránd (1991-) (geográfus) Phinzi, Kwanele (1989-) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:EFOP-3.6.1-16-2016-00022
EFOP
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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2.

001-es BibID:BIBFORM119435
Első szerző:Phinzi, Kwanele
Cím:Predictive machine learning for gully susceptibility modeling with geo?environmental covariates: main drivers, model performance, and computational efciency / Kwanele Phinzi, Szilárd Szabó
Dátum:2024
ISSN:0921-030X
Megjegyzések:Currently, machine learning (ML) based gully susceptibility prediction is a rapidly expanding research area. However, when assessing the predictive performance of ML models, previous research frequently overlooked the critical component of computational efficiency in favor of accuracy. This study aimed to evaluate and compare the predictive performance of six commonly used algorithms in gully susceptibility modeling. Artificial neural networks (ANN), partial least squares, regularized discriminant analysis, random forest (RF), stochastic gradient boosting, and support vector machine (SVM) were applied. The comparison was conducted under three scenarios of input feature set sizes: small (six features), medium (twelve features), and large (sixteen features). Results indicated that SVM was the most efficient algorithm with a medium-sized feature set, outperforming other algorithms across all overall accuracy (OA) metrics (OA?=?0.898, F1-score?=?0.897) and required a relatively short computation time (<?1 min). Conversely, ensemble-based algorithms, mainly RF, required a larger feature set to reach optimal accuracy and were computationally demanding, taking about 15 min to compute. ANN also showed sensitivity to the number of input features, but unlike RF, its accuracy consistently decreased with larger feature sets. Among geo-environmental covariates, NDVI, followed by elevation, TWI, population density, SPI, and LULC, were critical for gully susceptibility modeling. Therefore, using SVM and involving these covariates in gully susceptibility modeling in similar environmental settings is strongly suggested to ensure higher accuracy and minimal computation time.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Gully erosion
Machine learning
Predictive modeling
Accuracy
Computational efficiency
Geo-environmental predictors
Megjelenés:Natural Hazards. - [Epub ahead of print] : - (2024), p. -. -
További szerzők:Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:K 138079
Egyéb
KKP144068
Egyéb
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3.

001-es BibID:BIBFORM115323
035-os BibID:(WoS)001086503900002 (Scopus)85174061140
Első szerző:Phinzi, Kwanele
Cím:Understanding the role of training sample size in the uncertainty of high-resolution LULC mapping using random forest / Kwanele Phinzi, Njoya Silas Ngetar, Quoc Bao Pham, Gashaw Gismu Chakilu, Szilárd Szabó
Dátum:2023
ISSN:1865-0473 1865-0481
Megjegyzések:High-resolution sensors onboard satellites are generally reputed for rapidly producing land-use/land-cover (LULC) maps with improved spatial detail. However, such maps are subject to uncertainties due to several factors, including the training sample size. We investigated the effects of different training sample sizes (from 1000 to 12,000 pixels) on LULC classification accuracy using the random forest (RF) classifier. Then, we analyzed classification uncertainties by determining the median and the interquartile range (IQR) of the overall accuracy (OA) values through repeated k-fold cross-validation. Results showed that increasing training pixels significantly improved OA while minimizing model uncertainty. Specifically, larger training samples, ranging from 9000 to 12,000 pixels, exhibited narrower IQRs than smaller samples (1000-2000 pixels). Furthermore, there was a significant variation (Chi2 = 85.073; df = 11; p < 0.001) and a significant trend (J-T = 4641, p < 0.001) in OA values across various training sample sizes. Although larger training samples generally yielded high accuracies, this trend was not always consistent, as the lowest accuracy did not necessarily correspond to the smallest training sample. Nevertheless, models using 9000-11,000 pixels were effective (OA > 96%) and provided an accurate visual representation of LULC. Our findings emphasize the importance of selecting an appropriate training sample size to reduce uncertainties in high-resolution LULC classification.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
High-resolution sensor
LULC
Training sample size
Random forest
Classification uncertainty
Megjelenés:Earth Science Informatics. - 16 : 4 (2023), p. 3667-3677. -
További szerzők:Ngetar Njoya Silas Pham, Quoc Bao Chakilu, Gashaw Gismu Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:K138079
Egyéb
K138503
Egyéb
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4.

001-es BibID:BIBFORM111582
Első szerző:Phinzi, Kwanele
Cím:NDVI-based land-use/cover change detection in a mountainous heterogeneous landscape / Kwanele Phinzi, Szilárd Szabó
Dátum:2020
ISBN:978-963-318-886-6
Tárgyszavak:Természettudományok Földtudományok tanulmány,értekezés
könyvrészlet
Megjelenés:Az elmélet és a gyakorlat találkozása a térinformatikában XI. : theory meets practice in GIS / szerk. Molnár Vanda Éva. - p. 201-206. -
További szerzők:Szabó Szilárd (1974-) (geográfus)
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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5.

001-es BibID:BIBFORM095974
035-os BibID:(cikkazonosító)2980 (WOS)000682299100001 (Scopus)85112129721
Első szerző:Phinzi, Kwanele
Cím:Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems / Kwanele Phinzi, Dávid Abriha, Szilárd Szabó
Dátum:2021
ISSN:2072-4292
Megjegyzések:The availability of aerial and satellite imageries has greatly reduced the costs and time associated with gully mapping, especially in remote locations. Regardless, accurate identification of gullies from satellite images remains an open issue despite the amount of literature addressing this problem. The main objective of this work was to investigate the performance of support vector machines (SVM) and random forest (RF) algorithms in extracting gullies based on two resampling methods: bootstrapping and k-fold cross-validation (CV). In order to achieve this objective, we used PlanetScope data, acquired during the wet and dry seasons. Using the Normalized Difference Vegetation Index (NDVI) and multispectral bands, we also explored the potential of the PlanetScope image in discriminating gullies from the surrounding land cover. Results revealed that gullies had significantly different (p < 0.001) spectral profiles from any other land cover class regarding all bands of the PlanetScope image, both in the wet and dry seasons. However, NDVI was not efficient in gully discrimination. Based on the overall accuracies, RF's performance was better with CV, particularly in the dry season, where its performance was up to 4% better than the SVM's. Nevertheless, class level metrics (omission error: 11.8%; commission error: 19%) showed that SVM combined with CV was more successful in gully extraction in the wet season. On the contrary, RF combined with bootstrapping had relatively low omission (16.4%) and commission errors (10.4%), making it the most efficient algorithm in the dry season. The estimated gully area was 88 ? 14.4 ha in the dry season and 57.2 - 18.8 ha in the wet season. Based on the standard error (8.2 ha), the wet season was more appropriate in gully identification than the dry season, which had a slightly higher standard error (8.6 ha). For the first time, this study sheds light on the influence of these resampling techniques on the accuracy of satellite-based gully mapping. More importantly, this study provides the basis for further investigations into the accuracy of such resampling techniques, especially when using different satellite images other than the PlanetScope data.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
satellite imagery
gully mapping
machine learning
random forest
support vector machine
south africa
semi-arid environment
Megjelenés:Remote Sensing. - 13 : 15 (2021), p. 1-18. -
További szerzők:Abriha Dávid (1995-) (geográfus) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:TKP2020-NKA-04
Egyéb
Department of Higher Education and Training (DHET) of South Africa
Egyéb
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6.

001-es BibID:BIBFORM091585
035-os BibID:(cikkazonosító)333 (WoS)000621996400001
Első szerző:Phinzi, Kwanele
Cím:Mapping Permanent Gullies in an Agricultural Area Using Satellite Images : Efficacy of Machine Learning Algorithms / Phinzi Kwanele, Holb Imre, Szabó Szilárd
Dátum:2021
ISSN:2073-4395
Megjegyzések:Gullies are responsible for detaching massive volumes of productive soil, dissecting natural landscape and causing damages to infrastructure. Despite existing research, the gravity of the gully erosion problem underscores the urgent need for accurate mapping of gullies, a first but essential step toward sustainable management of soil resources. This study aims to obtain the spatial distribution of gullies through comparing various classifiers: k-dimensional tree K-Nearest Neighbor (k-d tree KNN), Minimum Distance (MD), Maximum Likelihood (ML), and Random Forest (RF). Results indicated that all the classifiers, with the exception of ML, achieved an overall accuracy (OA) of at least 0.85. RF had the highest OA (0.94), although it was outperformed in gully identification by MD (0% commission), but the omission error was 20% (MD). Accordingly, RF was considered as the best algorithm, having 13% error in both adding (commission) and omitting pixels as gullies. Thus, RF ensured a reliable outcome to map the spatial distribution of gullies. RF-derived gully density map reflected the agricultural areas most exposed to gully erosion. Our approach of using satellite imagery has certain limitations, and can be used only in arid or semiarid regions where gullies are not covered by dense vegetation as the vegetation biases the extracted gullies. The approach also provides a solution to the lack of laser scanned data, especially in the context of the study area, providing better accuracy and wider application possibilities.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
gully erosion
image classification
K-Nearest Neighbor
random forest
minimum distance
maximum likelihood
Megjelenés:Agronomy-Basel. - 11 : 2 (2021), p. 1-16. -
További szerzők:Holb Imre (1973-) (agrármérnök) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:TNN 123457 NKFI
Egyéb
Thematic Excellence Programme (TKP2020-NKA-04) of the Ministry for Innovation and Technology in Hungary projects
Egyéb
K131478
OTKA
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7.

001-es BibID:BIBFORM087952
Első szerző:Phinzi, Kwanele
Cím:Comparison of Rusle and Supervised Classification Algorithms for Identifying Erosion-Prone Areas in a Mountainous Rural Landscape / Kwanele Phinzi, Njoya Silas Ngetar, Osadolor Ebhuoma, Szilárd Szabó
Dátum:2020
Megjegyzések:The identification of erosion prone areas with reasonably high accuracy is a prerequisite for formulating relevant soil conservation measures especially in rural areas where there is much reliance on subsistence agriculture. The aim of this paper was to compare and exploit the complementary advantage of fusing three independent methods including the Revised Universal Soil Loss Equation (RUSLE) and two supervised image classification algorithms: Random Forest (RF) and Maximum Likelihood (ML). All analyses were conducted using a GIS proprietary software, ArcGIS. The results indicated that RF was the best with the highest overall accuracy (OA), producer's accuracy (PA), and user's accuracy (UA) of 87%, 78%, and 95%, respectively. RUSLE poorly performed relative to other methods, scoring the lowest PA (34%) and OA (66%), but slightly outperformed ML in terms of UA. From the user's perspective, the performance of individual methods was satisfactory with each method achieving an UA of greater than 90% although ML and RUSLE were not satisfactory from the producer's perspective, recording respective PAs of 56% and 34%. When the results from individual methods were fused, the accuracy increased above 90% across all accuracy indices, which is far above the 85% acceptable level for planning and management purposes.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
RUSLE
supervised classification
random forest
maximum likelihood
soil erosion
Megjelenés:Carpathian Journal of Earth and Environmental Science. - 15 : 2 (2020), p. 405-413. -
További szerzők:Ngetar Njoya Silas Ebhuoma Osadolor Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:TKP - Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary (ED 18-1-2019-0028)
Egyéb
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8.

001-es BibID:BIBFORM084936
035-os BibID:(cikkazonosító)252
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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9.

001-es BibID:BIBFORM098446
Első szerző:Szabó Loránd (geográfus)
Cím:Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery / Szabó, Loránd; Abriha, Dávid; Phinzi, Kwanele; Szabó, Szilárd
Dátum:2021
ISSN:1789-4921 1789-7556
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény hazai lapban
folyóiratcikk
vegetation classification
high-resolution
satellite imagery
Planet
SkySat
urban vegetation
NDVI
ROC curve
classification performance
Random Forest
Support Vector Machine
Megjelenés:Acta geographica Debrecina. Landscape & environment series. - 15 : 1 (2021), p. 66-75. -
További szerzők:Abriha Dávid (1995-) (geográfus) Phinzi, Kwanele (1989-) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:EFOP-3.6.1-16-2016-00022
EFOP
Doctoral Student Scholarship Program of the Co-operative Doctoral Program of the Ministry of Innovation and Technology financed from the National Research, Development and Innovation Fund
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