Szilárd Szabó

发表

GIS-based sinkhole susceptibility mapping using the best worst method

Mohammad Salman, M. Maleki, S. Sahebi Vayghan, 2023, Spatial Information Research.

Analysis of the association between image resolution and landscape metrics using multi-sensor LULC maps

G. A. Aksu, Elif Sertel, R. H. Topaloğlu, 2023, Journal of Environmental Planning and Management.

Performance evaluation of machine learning algorithms to assess soil erosion in Mediterranean farmland: A case‐study in Syria

S. Mohammed, Mona Barakat, Basharat Bashir, 2023, Land Degradation & Development.

A bibliographic review of climate change and fertilization as the main drivers of maize yield: implications for food security

S. Mohammed, I. Holb, E. Harsányi, 2023, Agriculture & Food Security.

Strategies in training deep learning models to extract building from multisource images with small training sample sizes

Dávid Abriha, Szilárd Szabó, Dávid Abriha, 2023, Int. J. Digit. Earth.

Smaller is better? Unduly nice accuracy assessments in roof detection using remote sensing data with machine learning and k-fold cross-validation

P. Srivastava, Dávid Abriha, Szilárd Szabó, 2023, Heliyon.

UAV-based multispectral and thermal cameras to predict soil water content – A machine learning approach

L. Bertalan, S. Szabó, G. Szabó, 2022, Computers and Electronics in Agriculture.

Assessment of multi-source satellite products using hydrological modelling approach

K. Rawat, Aiswarya Rani Mahanta, Szilárd Szabó, 2023, Physics and Chemistry of the Earth, Parts A/B/C.

Aquatic vegetation mapping with UAS-cameras considering phenotypes

G. Dévai, L. Bertalan, I. Grigorszky, 2024, Ecological Informatics.

Deep learning‐based training data augmentation combined with post‐classification improves the classification accuracy for dominant and scattered invasive forest tree species

I. Holb, P. Burai, Szilárd Balázs Likó, 2023, Remote Sensing in Ecology and Conservation.

Predictive machine learning for gully susceptibility modeling with geo-environmental covariates: main drivers, model performance, and computational efficiency

Kwanele Phinzi, Szilárd Szabó, 2024, Natural Hazards.