TY - JOUR AU - Nagy, Loránd Attila AU - Szabó, Szilárd AU - Burai, Péter AU - Bertalan, László TI - Improving Urban Mapping Accuracy: Investigating the Role of Data Acquisition Methods and SfM Processing Modes in UAS‑Based Survey Through Explainable AI Metrics JF - Journal of Geovisualization and Spatial Analysis J2 - Journal of Geovisualization and Spatial Analysis VL - 8 PY - 2024 IS - 1 PG - 19 SN - 2509-8810 DO - 10.1007/s41651-024-00179-z UR - https://m2.mtmt.hu/api/publication/34847884 ID - 34847884 AB - In this study, we investigated the accuracy of surface models and orthophoto mosaics generated from images acquired using different data acquisition methods at different processing levels in two urban study areas with different characteristics. Experimental investigations employed single- and double-grid flight directions with nadir and tilted (60°) camera angles, alongside the Perimeter 3D method. Three processing levels (low, medium, and high) were applied using SfM software, resulting in 42 models. Ground truth data from RTK GNSS points and aerial LiDAR surveys were used to assess horizontal and vertical accuracies. For the horizontal accuracy test, neither the oblique camera angle nor the double grid resulted in an improvement in accuracy. In contrast, when examining the vertical accuracy, it was concluded that for several processing levels, the tilted camera angle yielded better results, and in these cases, the double grid also improved accuracy. Feature importance analysis revealed that, among the four variables, the data acquisition method was the most important factor affecting accuracy in two out of three cases. LA - English DB - MTMT ER - TY - JOUR AU - Szabó, Loránd AU - Bertalan, László AU - Szabó, Gergely AU - Grigorszky, István AU - Somlyai, Imre AU - Dévai, György AU - Nagy, Sándor Alex AU - Holb, Imre AU - Szabó, Szilárd TI - Aquatic vegetation mapping with UAS-cameras considering phenotypes JF - ECOLOGICAL INFORMATICS J2 - ECOL INFORM PY - 2024 SN - 1574-9541 DO - 10.1016/j.ecoinf.2024.102624 UR - https://m2.mtmt.hu/api/publication/34839516 ID - 34839516 AB - Aquatic vegetation species at the genus level in an oxbow lake were identified in Hungary based on a multispectral Uncrewed Aerial System (UAS ) survey within an elongated oxbow lake area of the Tisza River under continental climate. Seven and 13 classes were discriminated using three different classification methods (Support Vector Machine [SVM], Random Forest [RF] , and Multivariate Adaptive Regression Splines [MARS]) using different input data in ten combinations: original spectral bands , spectral indices, Digital Surface Model (DSM) , and Haralick texture indices. We achieved a high (97.1%) overall accuracies (OAs) by applying the SVM classifier, but the RF performed only <1% worse, as it was represented in the first places of the classification rank before the MARS. The highest classification accuracies (>84% OA) were obtained using the most important variables derived by the Recursive Feature Elimination (RFE) method . The best classification required DSM as an input variable. The poorest classification performance belonged to the model that used only texture indices or spectral indices. On the class level, Stratoites aloides exhibit the lowest degree of separability compared to the other classes. Accordingly, we recommend using supplementary input data for the classifications beside s the original spectral bands, for example , DSM, spectral , and texture indices, as these variables significantly improve the classification accuracies in the proper combinations of the input variables . LA - English DB - MTMT ER - TY - CONF AU - Bertalan, László AU - Négyesi, Gábor AU - Szabó, Gergely AU - Túri, Zoltán AU - Szabó, Szilárd TI - Evaluating the efficacy of multitemporal TLS and UAS surveys for quantifying wind erosion magnitudes of sand dune topography T2 - EGU General Assembly 2024 : abstracts PB - European Geosciences Union (EGU) C1 - Wien PY - 2024 UR - https://m2.mtmt.hu/api/publication/34799801 ID - 34799801 LA - English DB - MTMT ER - TY - JOUR AU - Phinzi, Kwanele AU - Szabó, Szilárd TI - Predictive machine learning for gully susceptibility modeling with geo-environmental covariates: main drivers, model performance, and computational efficiency JF - NATURAL HAZARDS J2 - NAT HAZARDS PY - 2024 SN - 0921-030X DO - 10.1007/s11069-024-06481-9 UR - https://m2.mtmt.hu/api/publication/34742919 ID - 34742919 AB - 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, F 1-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. LA - English DB - MTMT ER - 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 - de Oliveira-Júnior, José Francisco AU - Mendes, David AU - Szabó, Szilárd AU - Singh, Sudhir Kumar AU - Jamjareegulgarn, Punyawi AU - Cardoso, Kelvy Rosalvo Alencar AU - Bertalan, László AU - da Silva, Marcos Vinicius AU - da Rosa Ferraz Jardim, Alexandre Maniçoba AU - da Silva, Jhon Lennon Bezerra AU - Lyra, Gustavo Bastos AU - Abreu, Marcel Carvalho AU - Filho, Washington Luiz Félix Correia AU - de Sousa, Amaury AU - de Barros Santiago, Dimas AU - da Silva Santos, Iwldson Guilherme AU - Maksudovna, Vafaeva Khristina TI - Impact of the El Niño on Fire Dynamics on the African Continent JF - EARTH SYSTEMS AND ENVIRONMENT J2 - EARTH SYST ENVIRON VL - - PY - 2024 PG - 17 SN - 2509-9426 DO - 10.1007/s41748-023-00363-z UR - https://m2.mtmt.hu/api/publication/34476689 ID - 34476689 AB - Several studies investigated the occurrence of fires in Africa with numerical modeling or applied statistics; however, only a few studies focused on the influence of El Niño on the fire risk using a coupled model. The study aimed to assess the influence of El Niño on wildfire dynamics in Africa using the SPEEDY-HYCOM model. El Niño events in the Eastern Tropical Pacific were classified via sea surface temperature (SST) anomaly based on a predefined climatology between 1961 and 2020 for the entire time series of SST, obtaining linear anomalies. The time series of the SST anomalies was created for the region between 5° N and 5° S and 110° W and 170° W. The events were defined in three consecutive 3-month periods as weak, moderate, and strong El Niño conditions. The Meteorological Fire Danger Index (MFDI) was applied to detect fire hazards. The MFDI simulated by the SPEEDY-HYCOM model for three El Niño categories across different lagged months revealed relevant distinctions among the categories. In the case of ‘Weak’, the maximum variability of fire risk observed at time lags (0, -3, -6, and -9 months) was primarily in Congo, Gabon, and Madagascar. The ‘Moderate’ pattern had similar characteristics to ‘Weak’ except for the lag-6 months and its occurrence in the equatorial zone of Africa. ‘Strong’ showed a remarkable impact in East Africa, resulting in high fire risk, regardless of time lags. Precipitation and evaporation simulations (SPEEDY-HYCOM) indicated that El Niño categories in Africa need particular attention in the central, southern, and southeastern regions emphasizing the significance of lag-0 and lag-6 (evaporation) as well as lag-0, lag-6, and lag-9 (precipitation). The SPEEDY-HYCOM coupled model in conjunction with the MFDI was efficient in assessing climate variabilities in Africa during El Niño events. This model allows the analysis and prediction of wildfire risks based on El Niño events, providing crucial information for wildfire management and prevention. Its simulations uncover significant variations in risks among different El Niño categories and lagged months, contributing to the understanding and mitigation of this environmental challenge. LA - English DB - MTMT ER - TY - JOUR AU - Mahanta, Aiswarya Rani AU - Rawat, Kishan Singh AU - Kumar, Nirmal AU - Szabó, Szilárd AU - Srivastava, Prashant K. AU - Singh, Sudhir Kumar TI - Assessment of multi-source satellite products using hydrological modelling approach JF - PHYSICS AND CHEMISTRY OF THE EARTH (2002-) J2 - PHYS CHEM EARTH (2002-) VL - 133 PY - 2024 SN - 1474-7065 DO - 10.1016/j.pce.2023.103507 UR - https://m2.mtmt.hu/api/publication/34396433 ID - 34396433 AB - Multi-source satellite products performance evaluation for varied geographical locations aids in quantification of hydrological variables and is useful in the strategy making and conservation of the hydrological resources available in a basin. The work was focused on assessing utility of multi-source satellite datasets to obtain the estimation of hydrologic variables and provide solution for areas that are poorly gauged or un-gauged. Assessment of the multi-source-satellite products was performed for the poorly gauged river basin with the help of SWAT concerning the Palar River basin, India. We analysed time series at the monthly, seasonal, and annual scales to quantify surface runoff, water yield, ET, & PET at the calibration site and for the entire basin for the period 2003 to 2021, depending on the common period of availability of all the data sets. SWAT model estimated highest monthly water yield during November–December, with annual water yield being maximum (220 mm in 2010) and average (99 mm), which can be used to understand water resources for irrigation, drinking aspects, and net storage. Average monthly surface runoff patterns were similar for SWAT, TerraClimate, and FLDAS. The FLDAS and SWAT simulated surface runoff show a resemblance in pattern and magnitude for the monthly and annual time series of the average basin scenario. The monthly PET obtained from SWAT and ERA-5 show a similar pattern for the entire basin and at the calibration site. The ET derived from satellite observation has over-predicted the model output at both the calibration site and entire basin LA - English DB - MTMT ER - TY - JOUR AU - Mohammed, Safwan AU - Gill, Abid Rashid AU - Ghosal, Kaushik AU - Al-Dalahmeh, Main AU - Alsafadi, Karam AU - Szabó, Szilárd AU - Oláh, Judit AU - Alkerdi, Ali AU - Ocwa, Akasairi AU - Harsányi, Endre TI - Assessment of the environmental kuznets curve within EU-27: Steps toward environmental sustainability (1990–2019) JF - Environmental Science and Ecotechnology J2 - Environmental Science and Ecotechnology VL - 18 PY - 2024 PG - 36 SN - 2666-4984 DO - 10.1016/j.ese.2023.100312 UR - https://m2.mtmt.hu/api/publication/34148822 ID - 34148822 LA - English DB - MTMT ER - TY - JOUR AU - Likó, Szilárd Balázs AU - Holb, Imre AU - Oláh, Viktor AU - Burai, Péter AU - Szabó, Szilárd TI - Deep learning‐based training data augmentation combined with post‐classification improves the classification accuracy for dominant and scattered invasive forest tree species JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2024 SN - 2056-3485 DO - 10.1002/rse2.365 UR - https://m2.mtmt.hu/api/publication/34092358 ID - 34092358 N1 - Early Access: AUG 2023 AB - Species composition of forests is a very important component from the point of view of nature conservation and forestry. We aimed to identify 10 tree species in a hilly forest stand using a hyperspectral aerial image with a particular focus on two invasive species, namely Ailanthus tree and black locust. Deep learning‐based training data augmentation (TDA) and post‐classification techniques were tested with Random Forest and Support Vector Machine (SVM) classifiers. SVM had better performance with 81.6% overall accuracy (OA). TDA increased the OA to 82.5% and post‐classification with segmentation improved the total accuracy to 86.2%. The class‐level performance was more convincing: the invasive Ailanthus trees were identified with 40% higher producer's and user's accuracies (PA and UA) to 70% related to the common technique (using a training dataset and classifying the trees). The PA and UA did not change in the case of the other invasive species, black locust. Accordingly, this new method identifies well Ailanthus, a sparsely distributed species in the area; while it was less efficient with black locust that dominates larger patches in the stand. The combination of the two ancillary steps of hyperspectral image classification proved to be reasonable and can support forest management planning and nature conservation in the future. LA - English DB - MTMT ER - TY - CHAP AU - N., Antić AU - M., Kašanin-Grubin AU - Bertalan, László AU - V., Gajić AU - L., Kaluđerović AU - N., Mijatović AU - B., Jovančićević ED - Željko, Jaćimović ED - Miljan, Bigović ED - Milica Kosović, Perutović TI - How Bad Volcano-Clastic Badlands Actually Are? T2 - 23rd European Meeting on Environmental Chemistry, EMEC23, Book of abstracts PB - Chemical Society of Montenegro CY - Podgorica PY - 2023 SP - 22 UR - https://m2.mtmt.hu/api/publication/34735335 ID - 34735335 LA - English DB - MTMT ER -