TY  - JOUR
AU  - Szabó, Szilárd
AU  - Holb, Imre
AU  - Abriha-Molnár, Vanda Éva
AU  - Szatmári, Gábor
AU  - Singh, Sudhir Kumar
AU  - Abriha, Dávid
TI  - Classification Assessment Tool: A program to measure the uncertainty of classification models in terms of class-level metrics
JF  - APPLIED SOFT COMPUTING
J2  - APPL SOFT COMPUT
VL  - 155
PY  - 2024
PG  - 15
SN  - 1568-4946
DO  - 10.1016/j.asoc.2024.111468
UR  - https://m2.mtmt.hu/api/publication/34741333
ID  - 34741333
AB  - 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.
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Rady, Ali Shebl
AU  - Abriha, Dávid
AU  - Fahil, Amr S.
AU  - El-Dokouny, Hanna A.
AU  - Elrasheed, Abdelmajeed A.
AU  - Csámer, Árpád
TI  - PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert: Evaluating the Support Vector Machine, Random Forest, and XG Boost Machine Learning Algorithms
JF  - ORE GEOLOGY REVIEWS
J2  - ORE GEOL REV
VL  - 161
PY  - 2023
SN  - 0169-1368
DO  - 10.1016/j.oregeorev.2023.105652
UR  - https://m2.mtmt.hu/api/publication/34127620
ID  - 34127620
AB  - In essence, targeting mineralization necessitates exact structural delineation and thorough lithological mapping. The latter is still a challenge for geologists and its lack hinders meticulous exploration for various mineralizations. Here we show for the first time over a case study from Arabian Nubian Shield (ANS), the application of hyperspectral PRISMA (PRecursore IperSpettrale della Missione Applicativa) data for objective lithological mapping using the well-known Random Forest (RF), XGboost (XGB), and Support Vector Machine (SVM) algorithms. Our results manifested the worthiness of PRISMA data in further lithological mapping, especially with SVM with a resultant accuracy depending mainly on the input data combination. Upon field verification, the current research reveals the usefulness of PRISMA and its preceding four principal components in delivering a detailed lithological map for the study area. Additionally, the eligibility of RF, XGB, and SVM was confirmed in delivering acceptable results. SVM exceeds XGB and RF in their overall accuracy (95 %, 92 %, and 90 % for SVM, XGB, and RF respectively). Our research strongly recommends blending the vantages of Machine Learning Algorithms' (MLAs) objectivity and the wealth of PRISMA spectral coverage for further precise lithological mapping before applicable mineral exploration programs in similar terrains.
LA  - English
DB  - MTMT
ER  - 

TY  - GEN
AU  - Abriha-Molnár, Vanda Éva
AU  - Szabó, Szilárd
AU  - Magura, Tibor
AU  - Tóthmérész, Béla
AU  - Abriha, Dávid
AU  - Sipos , Bianka
AU  - Simon, Edina
TI  - Assessment of environmental impacts based on particulate matter, and chlorophyll content of urban trees
PY  - 2023
UR  - https://m2.mtmt.hu/api/publication/34011378
ID  - 34011378
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Pataki, Angelika
AU  - Nagy, Loránd Attila
AU  - Abriha, Dávid
AU  - Szabó, Szilárd
ED  - Abriha-Molnár, Vanda Éva
TI  - Műholdas szenzorokból származtatott talajnedvességadatok összehasonlítása
T2  - Az elmélet és gyakorlat találkozása a térinformatikában XIV. : Theory meets practice in GIS
PB  - Debreceni Egyetemi Kiadó
CY  - Debrecen
SN  - 9789636150846
PY  - 2023
SP  - 199
EP  - 203
PG  - 5
UR  - https://m2.mtmt.hu/api/publication/33999643
ID  - 33999643
AB  - A talajnedvesség monitorozása többféleképpen is lehetséges. A kutatás során két, alapvetően különböző műholdfelvételekből származtatott talajnedvesség adatot hasonlítottunk össze. Az összehasonlítás leíró statisztikával történt. Eredményként arra jutottunk, hogy a vizsgálatot még számos paraméterrel kell kiegészíteni ahhoz, hogy a vizsgált talajnedvességadatok validitásáról megbizonyosodjunk.
LA  - Hungarian
DB  - MTMT
ER  - 

TY  - THES
AU  - Abriha, Dávid
TI  - Gépi és mély tanulási eljárások alkalmazása a városi környezet vizsgálatában, nagyfelbontású és különböző dimenziójú távérzékelt adatok alapján
PY  - 2023
UR  - https://m2.mtmt.hu/api/publication/33937135
ID  - 33937135
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Abriha, Dávid
AU  - Szabó, Szilárd
TI  - Strategies in training deep learning models to extract building from multisource images with small training sample sizes
JF  - INTERNATIONAL JOURNAL OF DIGITAL EARTH
J2  - INT J DIGIT EARTH
VL  - 16
PY  - 2023
IS  - 1
SP  - 1707
EP  - 1724
PG  - 18
SN  - 1753-8947
DO  - 10.1080/17538947.2023.2210312
UR  - https://m2.mtmt.hu/api/publication/33823813
ID  - 33823813
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Abriha, Dávid
AU  - Srivastava, Prashant K.
AU  - Szabó, Szilárd
TI  - Smaller is better? Unduly nice accuracy assessments in roof detection using remote sensing data with machine learning and k-fold cross-validation
JF  - HELIYON
J2  - HELIYON
VL  - 9
PY  - 2023
PG  - 17
SN  - 2405-8440
DO  - 10.1016/j.heliyon.2023.e14045
UR  - https://m2.mtmt.hu/api/publication/33664435
ID  - 33664435
AB  - Deriving the thematic accuracy of models is a fundamental part of image classification analyses.
K-fold cross-validation (KCV), as an accuracy assessment technique, can be biased because
existing built-in algorithms of software solutions do not handle the high autocorrelation of
remotely sensed images, leading to overestimation of accuracies. We aimed to quantify the
magnitude of the overestimation of KCV-based accuracies and propose a method to overcome this
problem with the example of rooftops using a WorldView-2 (WV2) satellite image, and two
orthophotos. Random split to training/testing subsets, independent testing and different types of
repeated KCV sampling strategies were used to generate input datasets for classification. Results
revealed that applying the random splitting of reference data to training/testing subsets and KCV
methods had significantly biased the accuracies by up to 17%; overall accuracies (OAs) can
incorrectly reach >99%. We found that repeated KCV can provide similar results to independent
testing when spatial sampling is applied with a sufficiently large distance threshold (in our case
10 m). Coarser resolution of WV2 ensured more reliable results (up to a 5–9% increase in OA)
than orthophotos. Object-based pixel purity of buildings showed that when using a majority filter
for at least of 50% of objects the final accuracy approached 100% with each sampling method.
The final conclusion is that KCV-based modelling ensures better accuracy than single models
(with better pixel purity on the object level), but the accuracy metrics without spatially filtered
sampling are not reliable.
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Papp, Melitta
AU  - Szabó, Szilárd
AU  - Abriha, Dávid
ED  - Abriha-Molnár, Vanda Éva
TI  - WorldView–2 és WorldView–3 felvételek értékelése képosztályozáson keresztül
T2  - Az elmélet és gyakorlat találkozása a térinformatikában XIII.
PB  - Debreceni Egyetemi Kiadó
CY  - Debrecen
SN  - 9789636150396
PY  - 2022
SP  - 255
EP  - 259
PG  - 5
UR  - https://m2.mtmt.hu/api/publication/33239005
ID  - 33239005
LA  - Hungarian
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Abriha, Dávid
AU  - Szabó, Szilárd
AU  - Varga, György
ED  - Abriha-Molnár, Vanda Éva
TI  - Sentinel-5P műholdadatok alapján végzett légköri aeroszol koncentráció vizsgálat Google Earth Engine platformon
T2  - Az elmélet és a gyakorlat találkozása a térinformatikában XII.: Theory meets practice in GIS
PB  - Debreceni Egyetemi Kiadó
CY  - Debrecen
SN  - 9789633189771
PY  - 2021
SP  - 17
EP  - 23
PG  - 7
UR  - https://m2.mtmt.hu/api/publication/32495231
ID  - 32495231
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Abriha, Dávid
AU  - Szabó, Szilárd
AU  - Enyedi, Péter
ED  - Abriha-Molnár, Vanda Éva
TI  - Városi objektum kinyerést célzó deep learning algoritmus alkalmazása nagy felbontású légifelvételek alapján
T2  - Az elmélet és a gyakorlat találkozása a térinformatikában XII.: Theory meets practice in GIS
PB  - Debreceni Egyetemi Kiadó
CY  - Debrecen
SN  - 9789633189771
PY  - 2021
SP  - 9
EP  - 16
PG  - 8
UR  - https://m2.mtmt.hu/api/publication/32495227
ID  - 32495227
LA  - English
DB  - MTMT
ER  -