105 citations · 116 across the 10 of their papers we have counts for
12 papers
A Multi-Dataset Benchmark of YOLO-Based Weed Detection in Precision Agriculture
Hristina Zdraveska, Vlatko Spasev, Ivica Dimitrovski +2
Weed detection is an important component of precision agriculture, enabling site-specific weed management and reducing unnecessary herbicide use. Although deep learning methods hav…
Towards Practical Precision Agriculture: Real-Time Fruit Detection and Video Analytics on Embedded Edge Hardware
Ivica Dimitrovski, Vlatko Spasev, Ivan Kitanovski +2
Static-image benchmarks do not capture the computational and temporal requirements of practical orchard video analytics. This study presents an end-to-end framework for real-time f…
A Dataset-Centric Benchmark of Deep Learning Methods for Grape Leaf Disease Classification and Detection
Petar Canoski, Vlatko Spasev, Ivica Dimitrovski +2
Grape leaf disease recognition is important for precision agriculture, enabling early diagnosis, timely intervention, and improved vineyard management. Although deep learning has a…
Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series
Ivica Dimitrovski, Ivan Kitanovski, Danco Davcev +2
Accurate early warning of vineyard disease risk from environmental observations is essential for timely intervention and more sustainable crop protection. However, many existing st…
Few-Shot Remote Sensing Image Scene Classification with CLIP and Prompt Learning
Ivica Dimitrovski, Vlatko Spasev, Ivan Kitanovski
Remote sensing applications increasingly rely on deep learning for scene classification. However, their performance is often constrained by the scarcity of labeled data and the hig…
Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer
Vlatko Spasev, Ivica Dimitrovski, Ivan Chorbev +1
The escalating use of Unmanned Aerial Vehicles (UAVs) as remote sensing platforms has garnered considerable attention, proving invaluable for ground object recognition. While satel…
Deep Multimodal Fusion for Semantic Segmentation of Remote Sensing Earth Observation Data
Ivica Dimitrovski, Vlatko Spasev, Ivan Kitanovski
Accurate semantic segmentation of remote sensing imagery is critical for various Earth observation applications, such as land cover mapping, urban planning, and environmental monit…
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification
Ivica Dimitrovski, Ivan Kitanovski, Nikola Simidjievski +1
We investigate the utility of in-domain self-supervised pre-training of vision models in the analysis of remote sensing imagery. Self-supervised learning (SSL) has emerged as a pro…
Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021
Dragi Kocev, Nikola Simidjievski, Ana Kostovska +2
The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conf…
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
Ivica Dimitrovski, Ivan Kitanovski, Dragi Kocev +1
We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To…
AiTLAS: Artificial Intelligence Toolbox for Earth Observation
Ivica Dimitrovski, Ivan Kitanovski, Panče Panov +2
The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite i…
Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning
Ivica Obadić, Gjorgji Madjarov, Ivica Dimitrovski +1
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and it…