collaborators

6 papers

cs.CV2026

Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types

Michał Romaszewski, Kamil Drejer, Katarzyna Kołodziej +7

Sentinel-2 imagery offers open access, global coverage, and frequent revisit times, making it attractive for practical building mapping at scale; however, its native 10m resolution…

cs.PF2026

Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing

Aygün Varol, Katarzyna Kołodziej, Łukasz Sobczak +5

Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces la…

cs.CV2026

From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts

Michał Romaszewski, Dominik Kopeć, Michał Cholewa +6

Hyperspectral tree species classification is challenging due to limited and imbalanced class labels, spectral mixing (overlapping light signatures from multiple species), and ecolo…

eess.SP2024

Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials

Katarzyna Kołodziej, Michał Cholewa, Przemysław Głomb +2

Calibration is a critical process for reducing uncertainty in Water Distribution Network Hydraulic Models (WDN HM). However, features of certain WDNs, such as oversized pipelines,…

eess.SY2024

`Just One More Sensor is Enough' -- Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors

Michał Cholewa, Michał Romaszewski, Przemysław Głomb +6

In this article, we propose an approach to leak localisation in a complex water delivery grid with the use of data from physical simulation (e.g. EPANET software). This task is usu…

cs.CL2024

Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models

Michał Romaszewski, Przemysław Sekuła, Przemysław Głomb +2

Large Language Models (LLMs) have shown exceptional performance in text processing. Notably, LLMs can synthesize information from large datasets and explain their decisions similar…