Barańska A., Bac-Bronowicz J., Dejniak D., Lewiński S., Krawczyk A., Chrobak T. (2021), A Unified Methodology for the Generalisation of the Geometry of Features

The development of generalisation (simplification) methods for the geometry of features in digital cartography in most cases involves the improvement of existing algorithms without their validation with respect to the similarity of feature geometry

Kotarba A. Z., Solecki M. (2021), Uncertainty Assessment of the Vertically-Resolved Cloud Amount for Joint CloudSat–CALIPSO Radar–Lidar Observations

The joint CloudSat–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) climatology remains the only dataset that provides a global, vertically-resolved cloud amount statistic. However, data are affected by uncertainty that is the result of

Wawrzaszek A., Drzewiecki W., Krupiński M., Jenerowicz M., Aleksandrowicz S. (2021), Multifractal Features for Land Use Classification

In our work, we analyze multifractal parameters for the description and classification of a 2056 subsets of aerial images representing 21 land-use classes. In particular, we consider the degree of multifractality, estimated with different

Krupiński M., Wawrzaszek A., Drzewiecki W., Jenerowicz M., Aleksandrowicz S. (2021), Multifractal Parameters for Classification of Hyperspectral Data

(Multi)fractal character of hyperspectral imagery allows for application of new methods and parameters for data processing. In this work we apply Multifractal Detrended Fluctuation Analysis to describe the complex shape of spectral curve and

Nguyen Huu Ż, Kotarba A. Z. (2021), Reliability of visual detections of cirrus over Poland

Earth is a cloudy planet—at every moment, 62–72% of the planet is covered by cloud. Its presence and properties affect the flow of energy between the sun, Earth and the atmosphere. More comprehensive information

Krupiński, M., Wawrzaszek, A., Drzewiecki, W.,Jenerowicz, M., Aleksandrowicz, S. (2020), What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

Hyperspectral images provide complex information about the Earth’s surface due to their very high spectral resolution (hundreds of spectral bands per pixel). Effective processing of such a large amount of data requires dedicated analysis

Malinowski, R., Lewiński, S., Rybicki, M., Gromny, E., Jenerowicz, M., Krupiński, M., Nowakowski, A., Wojtkowski, C., Krupiński, M., Krätzschmar, E., Schauer, P. (2020), Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery

Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land

Kotarba A. (2020), Calibration of global MODIS cloud amount using CALIOP cloud profiles

The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOVs) as either “confident clear”, “probably clear”, “probably cloudy”, or “confident cloudy”. The cloud amount calculation requires quantitative cloud fractions

Dziob D., Krupiński M., Woźniak E., Gabryszewski R. (2020), Interdisciplinary Teaching Using Satellite Images as a Way to Introduce Remote Sensing in Secondary School

This article aims to meet two needs: (i) the need for skilled workers and students in the area of remote sensing and (ii) the need to make school science interesting for students. This article

Belcore E., Piras M., Woźniak E. (2020), Specific alpine environment land cover classification methodology: Google Earth Engine processing for Sentinel-2 data

Land Cover (LC) plays a key role in many disciplines and its classification from optical imagery is one of the prevalent applications of remote sensing. Besides years of researches and innovation on LC, the