Segmentation of geospatial rasters: analysis of the temporal characteristics of the AREAONAREAOVERLAYER algorithm

O.V. Tsaryniuk, A.M. Glybovets

Abstract


This paper investigates the performance of the AreaOnAreaOverlayer algorithm used for segmenting geospatial rasters based on elevation features within the FME environment. The main focus is on analyzing the algorithm’s temporal characteristics when processing large volumes of data, particularly in vegetation cover classification tasks. The study describes the experimental setup, typical input data, and the impact of polygon geometric parameters on execution time. The results provide insight into the algorithm’s application limits and reveal dependencies between the structure of input data and computational complexity.

Prombles in programming 2025; 2: 87-97


Keywords


spatial analysis; raster segmentation; geographic information systems; temporal characteristics; algorithm performance

References


Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. http://arxiv.org/abs/1505.04597.

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. http://arxiv.org/abs/1512.03385

S. Liu, et al, The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Science Advances 9(37), 2023, doi: 10.1126/sciadv.adh4097.

Liu S, Brandt M, Nord-Larsen T, Chave J, Reiner F, Lang N, Tong X, Ciais P, Igel C, Pascual A, Guerra-Hernandez J, Li S, Mugabowindekwe M, Saatchi S, Yue Y, Chen Z, Fensholt R. The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Sci Adv. 2023 Sep 15;9(37):eadh4097. Epub 2023 Sep 15. PMID: 37713489; PMCID: PMC10881069.

P. Hofmann, D. Tiede,Image segmentation based on hexagonal sampling grids, South Eastern European Journal of Earth Observation and Geomatics 3, 2014 pp. 173-177.

J.N. Mueller, J.N. Corcoran, A Random Point Initialization Approach to Image Segmentation with Variational Level-sets. 2021, http://arxiv.org/abs/2112.12355.

R. Douss, I.R Farah, Extraction of individual trees based on Canopy Height Model to monitor the state of the forest. Trees, Forests and People 8, 2022.

W. Li, Z. Niu, S. Gao, N. Huang, H. Chen, Correlating the horizontal and vertical distribution of LiDAR point clouds with components of biomass in a Picea crassifolia forest. Forests 5(8), 2014, pp. 1910–1930.

E. Lindberg, J. Holmgren, H. Olsson, Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method. International Journal of Applied Earth Geoinformation Observation 100, 2021.

M.K. Jakubowski, W. Li, Q. Guo, M. Kelly, Delineating individual trees from lidar data: A comparison of vector- and raster-based segmentation approaches. Remote Sensing 5(9), 2013, pp. 4163–4186.

Tsaryniuk, O., Hlybovets, A., & Oletsky, O. (2023). Automated Pipelines for Large-Scale Height-Based Vegetation Segmentation.

Safe Software, FME: Feature Manipulation Engine https://www.safe.com/fme.

FME Hub, HexagonSampler, https://hub.safe.com/publishers/larry/transfor mers/hexagonsampler


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