6 papers
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…
NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders
Katarina Trojachanec Dineva, Stefan Andonov, Ilinka Ivanoska +5
Recent advances in multimodal large language models enable new possibilities for image-based decision support. However, their reliability and operational trade-offs in neuroimaging…
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…