Review
Environmental Sciences
Junbo Wang, Lanying Wang, Shufang Feng, Benrong Peng, Lingfeng Huang, Sarah N. Fatholahi, Lisa Tang, Jonathan Li
Summary: This paper provides a narrative review of shoreline mapping using airborne LiDAR over the past two decades. More than 130 articles were summarized to assess the current state and challenges of this method. It was found that while there are limitations and challenges, the combination of LiDAR point cloud processing techniques, such as deep-learning algorithms, shows promise for improving shoreline extraction and mapping.
Article
Environmental Sciences
Oula Seitsonen, Janne Ikaheimo
Summary: Open access ALS data has been available in Finland for over a decade, actively utilized by Finnish archaeologists. A new countrywide ALS survey with higher resolution was initiated in 2020, leading to promising results in archaeological studies, especially in the northern wilderness areas.
Article
Environmental Sciences
Benjamin Stular, Stefan Eichert, Edisa Lozic
Summary: This research aims to create a processing pipeline for archaeology-specific point cloud processing, optimizing the classification and interpolation of LiDAR data, reducing manual workload, and improving the overall efficiency of data processing in the field of archaeology.
Article
Remote Sensing
Michael J. Campbell, Jessie F. Eastburn, Katherine A. Mistick, Allison M. Smith, Atticus E. L. Stovall
Summary: Pin similar to on-juniper (PJ) woodlands are a widespread dryland ecosystem in the US with a poorly constrained amount of aboveground biomass (AGB). Mapping the AGB of PJ is of unique importance due to its uncertain future in a changing climate. This study compared lidar platforms (airborne laser scanner, ALS vs. mobile laser scanner, MLS) and analytical frameworks (area-based modeling vs. individual tree-based modeling) to quantify AGB in PJ, and found that ALS is a suitable substitute for MLS when mapping PJ AGB under an area-based modeling framework.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Environmental Sciences
Maria Danese, Dario Gioia, Valentino Vitale, Nicodemo Abate, Antonio Minervino Amodio, Rosa Lasaponara, Nicola Masini
Summary: Illegal archaeological excavations, also known as looting, cause significant damage to cultural heritage sites. Remote sensing techniques, particularly LiDAR, have proven to be effective tools for the detection and monitoring of looting, especially in densely vegetated areas. This paper presents an ad hoc approach using LiDAR data to detect looting, and tests its efficacy on the Etruscan site of San Giovenale in Italy, showing a high success rate of 85-95%.
Article
Environmental Sciences
Benjamin Stular, Edisa Lozic, Stefan Eichert
Summary: This paper introduces the concept of an archaeology-specific DEM and highlights the importance of confidence mapping for archaeological interpretation. The study also discusses the unresolved issue of segmentation in DEM interpolation and aims to contribute to the methodological maturity of airborne LiDAR in archaeology.
Article
Chemistry, Analytical
Fanghua Liu, Yan He, Weibiao Chen, Yuan Luo, Jiayong Yu, Yongqiang Chen, Chongmiao Jiao, Meizhong Liu
Summary: This article discusses the development of Geiger-mode lidar (GML) systems and introduces a circular scanning GML system simulation model. By using a real-time data compression algorithm, the system is able to maintain high accuracy and efficiency while reducing data transmission rate and storage space. The initial flight tests demonstrate the system's ability to operate under different conditions, with good coverage and accuracy in mapping.
Article
Ecology
Marc Fuhr, Etienne Lalechere, Jean-Matthieu Monnet, Laurent Berges
Summary: Building a network of interconnected overmature forests is crucial for biodiversity conservation. LiDAR technology can accurately assess forest structural parameters and identify overmature forest patches over large areas. In this study, an index combining forest structural maturity attributes was developed to characterize the maturity of field plots. LiDAR metrics, along with elevation, slope, and echo intensity distribution, were important for predicting forest maturity. The model showed a high correlation between observed and predicted maturity values, indicating accurate ranking of field plots.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2022)
Article
Chemistry, Analytical
Ali Massoud, Ahmed Fahmy, Umar Iqbal, Sidney Givigi, Aboelmagd Noureldin
Summary: In the past two decades, there has been an increasing demand for real-time generation of digital surface models (DSMs), especially for aircraft landing in degraded visual environments. However, existing filtering algorithms for airborne laser scanning (ALS) data are computationally expensive and unsuitable for real-time applications. This research aims to design and implement an efficient algorithm that can be used in real-time on limited-resource embedded processors without the need for a supercomputer. The proposed algorithm effectively identifies the safest landing zone for aircraft/helicopter based on 3D LiDAR point cloud data.
Article
Remote Sensing
Martin Mokros, Tomas Mikita, Arunima Singh, Julian Tomastik, Juliana Chuda, Piotr Wezyk, Karel Kuzelka, Peter Surovy, Martin Klimanek, Karolina Zieba-Kulawik, Rogerio Bobrowski, Xinlian Liang
Summary: The development of devices capable of generating 3D point clouds of the forest has flourished in recent years. Low-cost technologies such as MultiCam, iPad Pro, GeoSlam Horizon, and FARO Focus s70 were compared for tree detection and diameter at breast height estimation. Results showed that TLS provided the most accurate data, while iPad Pro achieved results closest to TLS when DBH > 7 cm.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Edisa Lozic
Summary: This study utilized LiDAR data to analyze the agricultural land use of early medieval settlements in the subalpine microregion of Bled, Slovenia, revealing that settlers in the early Middle Ages favored light soils with high water retention capacity. Such soils were particularly suitable for cultivating barley, one of the staple crops of the period, especially in colder climates.
Article
Biodiversity Conservation
Tristan R. H. Goodbody, Nicholas C. Coops, Cornelius Senf, Rupert Seidl
Summary: Effective forest stewardship relies on comprehensive field-inventories. This study explores the benefits of incorporating airborne laser scanning (ALS) data as an auxiliary dataset in forest inventory campaigns. The research evaluates sampling approaches and methods to allocate new field plots, demonstrating the value of ALS in improving data availability and sampling efficiency.
ECOLOGICAL INDICATORS
(2023)
Article
Multidisciplinary Sciences
Luciene Sales Dagher Arce, Lucas Prado Osco, Mauro dos Santos de Arruda, Danielle Elis Garcia Furuya, Ana Paula Marques Ramos, Camila Aoki, Arnildo Pott, Sarah Fatholahi, Jonathan Li, Fabio Fernando de Araujo, Wesley Nunes Goncalves, Jose Marcato Junior
Summary: The use of deep learning approach successfully detected and geolocated the Buriti palm tree, showing improved accuracy compared to other methods and presenting potential applications for mapping individual tree species in dense forest environments.
SCIENTIFIC REPORTS
(2021)
Article
Environmental Sciences
Yasmine Megahed, Ahmed Shaker, Wai Yeung Yan
Summary: According to the World Health Organization, urban residents are expected to make up 70% of the global population by 2050. This study explores the effects of using traditional spectral signatures acquired by different sensors on the classification of LiDAR point clouds, achieving an overall classification accuracy of over 97% with the use of machine learning algorithms.
Article
Geochemistry & Geophysics
Janne Raty, Petri Varvia, Lauri Korhonen, Pekka Savolainen, Matti Maltamo, Petteri Packalen
Summary: This study compared single-photon and linear-mode airborne LiDAR for predicting species-specific volumes in forests, and found that the linear-mode Riegl VQ-1560i performed the best.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Jasmin Bogatinovski, Ljupco Todorovski, Saso Dzeroski, Dragi Kocev
Summary: In this study, a comprehensive meta-learning analysis of data sets and methods for multilabel classification was conducted. The results showed that meta features describing the label space were the most important, and meta features describing label relationships occurred more frequently than those describing label distributions. Furthermore, optimizing hyperparameters can improve predictive performance, although the extent of improvement may not always be justified by resource utilization.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Cell Biology
Nadja Anneliese Ruth Ring, Maria Concetta Volpe, Tomaz Stepisnik, Maria Grazia Mamolo, Pane Panov, Dragi Kocev, Simone Vodret, Sara Fortuna, Antonella Calabretti, Michael Rehman, Andrea Colliva, Pietro Marchesan, Luca Camparini, Thomas Marcuzzo, Rossana Bussani, Sara Scarabellotto, Marco Confalonieri, Tho X. Pham, Giovanni Ligresti, Nunzia Caporarello, Francesco S. Loffredo, Daniele Zampieri, Saso Dzeroski, Serena Zacchigna
Summary: This study used machine learning analysis to virtually screen millions of compounds and identified new anti-fibrotic drugs that inhibit myofibroblast differentiation. Dopamine and its derivative TS1 were found to be effective inhibitors of myofibroblast activation, acting through dopamine receptor 3 and the transforming growth factor beta pathway. TS1 also had a preventive and reversing effect on lung fibrosis.
CELL DEATH & DISEASE
(2022)
Article
Biotechnology & Applied Microbiology
Katja Kavkler, Miha Humar, Davor Krzisnik, Martina Turk, Crtomir Tavzes, Cene Gostincar, Saso Dzeroski, Stefan Popov, Ana Penko, Nina Gunde-Cimerman, Polona Zalar
Summary: Interdisciplinary investigations were conducted to assess the damage of a 17th century tempera painting. The study found that the position of the painting and microclimatic conditions were the main factors influencing mold biodeterioration, while the presence of a coating layer, protein binders, and pigments also played important roles in mold development.
INTERNATIONAL BIODETERIORATION & BIODEGRADATION
(2022)
Article
Computer Science, Artificial Intelligence
Jure Brence, Jovan Tanevski, Jennifer Adams, Edward Malina, Saso Dzeroski
Summary: This study presents the development of surrogate models for two radiative transfer models (RTMs), which can speed up the simulation process and accurately emulate satellite observations. The surrogate models show good performance in simulating Sentinel 5P spectra and exhibit broad applicability in different parameter sets and applications.
Article
Microbiology
Monika Novak Babic, Nina Gunde-Cimerman, Martin Breskvar, Saso Dzeroski, Joao Brandao
Summary: This study focuses on the fungal communities in beach sand and seawater, revealing the impact of environmental and seasonal changes on these communities. It also finds certain fungi with drug resistance and identifies pollution indicators for beach microbial regulation.
Article
Engineering, Industrial
Yang Jiao, Saso Dzeroski, Ales Jurca
Summary: This study investigates the variation in toe shape, as measured by the hallux valgus angle, and finds that it has a normal distribution in the general population. Females have larger angles compared to males, and people from Asia have larger angles compared to those from North America and Europe.
Article
Environmental Sciences
Marjan Stoimchev, Dragi Kocev, Saso Dzeroski
Summary: Images are now being generated at an unprecedented rate, and remote sensing images have attracted considerable research attention in image classification. Recently, the task of assigning multiple semantic categories to an image, known as multi-label classification, has become increasingly complex. This work explores different strategies for model training using pre-trained convolutional neural network architectures and traditional tree ensemble methods for multi-label classification, and conducts extensive experimental analysis on publicly available remote sensing image datasets.
Article
Chemistry, Analytical
Csaba Voros, David Bauer, Ede Migh, Istvan Grexa, Attila Gergely Vegh, Balazs Szalontai, Gastone Castellani, Tivadar Danka, Saso Dzeroski, Krisztian Koos, Filippo Piccinini, Peter Horvath
Summary: Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and Raman microscopy techniques provide detailed morphological information and spatial distribution of molecular fingerprints, respectively, enabling the study of cellular stages at the single-cell level. Artificial intelligence can characterize the molecular backgrounds of phenotypes and biological processes by analyzing spectral maps, making it a valuable solution for interpreting complex subcellular spectral maps.
Article
Biology
Nina Omejc, Manca Peskar, Aleksandar Miladinovic, Voyko Kavcic, Saso Dzeroski, Uros Marusic
Summary: The use of non-invasive EEG as an input sensor in brain-computer interfaces is common, but the collected EEG data face challenges, such as age-related variability of ERPs. To assess the effects of aging, a study was conducted with young and older individuals using EEG. Two types of EEG datasets were created for classifier training, and linear classifiers performed best. It was found that classification performance differs between dataset types, and using temporal features resulted in higher and more consistent performance scores. The effect of aging on classification performance depends on the classifier and its feature ranking, highlighting the importance of careful feature extraction and selection to avoid age-related performance degradation in practice.
Article
Computer Science, Information Systems
Jure Brence, Saso Dzeroski, Ljupco Todorovski
Summary: This paper proposes using attribute grammars to ensure the dimensional consistency of the induced equations, which can combine cross-domain knowledge and domain-specific knowledge effectively. The study also demonstrates that attribute grammars can be transformed into probabilistic context-free grammars for equation discovery efficiently. Furthermore, empirical evidence shows that attribute grammars ensuring dimensional consistency of equations can significantly improve the performance of equation discovery on the standard set of a hundred Feynman benchmarks.
INFORMATION SCIENCES
(2023)
Article
Engineering, Aerospace
Bozhidar Stevanoski, Dragi Kocev, Aljaz Osojnik, Ivica Dimitrovski, Saso Dzeroski
Summary: The Mars Express spacecraft, operated by the European Space Agency, has provided unprecedented scientific data about Mars but also needs accurate power modeling due to degradation. This pilot study predicts the thermal power consumption of the spacecraft using telemetry data, employing multi-target regression and considering both local and global approaches.
Article
Multidisciplinary Sciences
Ziga Kokalj, Saso Dzeroski, Ivan Sprajc, Jasmina Stajdohar, Andrej Draksler, Maja Somrak
Summary: This study aims to collect a multimodal annotated dataset suitable for deep learning in remote sensing of Maya archaeology. The dataset covers the area around Chactun, one of the largest ancient Maya urban centres in the central Yucatan Peninsula. It includes raster visualisations and canopy height models from airborne laser scanning, satellite data from Sentinel-1 and Sentinel-2, as well as manual data annotations representing different types of ancient Maya structures.
Article
Microbiology
Monika Novak Babic, Gregor Marolt, Jernej Imperl, Martin Breskvar, Saso Dzeroski, Nina Gunde-Cimerman
Summary: The presence and abundance of fungi in water are influenced by the source of water, water cleaning methods, and the materials in contact with water. Chlorination reduces fungal numbers, but its effect diminishes with longer water networks. Different fungi are observed on different materials, with plastic materials being more susceptible to colonization by basidiomycetous fungi.
Proceedings Paper
Computer Science, Artificial Intelligence
Ana Nikolikj, Saso Dzeroski, Mario Andres Munoz, Carola Doerr, Peter Korosec, Tome Eftimov
Summary: In black-box optimization, understanding the behavior of an algorithm instance is crucial. We propose a methodology that formulates an algorithm instance footprint by identifying easy and difficult problem instances. This methodology uses meta-representations to link algorithm performance and landscape properties, and clustering to detect regions of poor and good algorithm performance.
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023
(2023)
Article
Computer Science, Artificial Intelligence
Viktor Andonovikj, Pavle Boskoski, Saso Dzeroski, Biljana Mileva Boshkoska
Summary: This study addresses the problem of estimating the time-to-employment of a jobseeker using survival analysis and oblique predictive clustering tree. The approach treats censored data as missing data and shows its effectiveness on jobseekers' personal and professional characteristics.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)