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From Change Detection to Change Analytics: Decomposing Multi-Temporal Pixel Evolution Vectors (Short Paper)

Authors Victoria Scherelis , Patrick Laube , Michael Doering



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Author Details

Victoria Scherelis
  • Zurich University of Applied Sciences, Wädenswil, Switzerland
  • University of Zurich, Switzerland
Patrick Laube
  • Zurich University of Applied Sciences, Wädenswil, Switzerland
  • University of Zurich, Switzerland
Michael Doering
  • Zurich University of Applied Sciences, Wädenswil, Switzerland

Acknowledgements

We thank Dominic Lüönd for his support with Figure 3.

Cite AsGet BibTex

Victoria Scherelis, Patrick Laube, and Michael Doering. From Change Detection to Change Analytics: Decomposing Multi-Temporal Pixel Evolution Vectors (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 65:1-65:6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.65

Abstract

Change detection is a well-established process of detaining spatial and temporal changes of entities between two or more timesteps. Current advancements in digital map processing offer vast new sources of multitemporal geodata. As the temporal aspect gains complexity, the dismantling of detected changes on a pixel-based scale becomes a costly undertaking. In efforts to establish and preserve the evolution of detected changes in long time series, this paper presents a method that allows the decomposition of pixel evolution vectors into three dimensions of change, described as directed change, change variability, and change magnitude. The three dimensions of change compile to complex change analytics per individual pixels and offer a multi-faceted analysis of landscape changes on an ordinal scale. Finally, the integration of class confidence from learned uncertainty estimates illustrates the avenue to include uncertainty into the here presented change analytics, and the three dimensions of change are visualized in complex change maps.

Subject Classification

ACM Subject Classification
  • Information systems → Spatial-temporal systems
Keywords
  • Digital map processing
  • spatio-temporal modelling
  • land-use change

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References

  1. P Aplin and GM Smith. Advances in object-based image classification. The Int. Archives of the Photogrammetry, Remote Sensing and Spatial Info. Sciences, 37(B7):725-728, 2008. Google Scholar
  2. Priti Attri, Smita Chaudhry, and Subrat Sharma. Remote Sensing & GIS based Approaches for LULC Change Detection – A Review. Remote Sensing, 2015. Google Scholar
  3. A. Comber and M. Wulder. Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use. Transactions in GIS, 23(5):879-891, 2019. Google Scholar
  4. Limin Dai, Shanlin Li, Bernard J. Lewis, Jian Wu, Dapao Yu, Wangming Zhou, Li Zhou, and Shengnan Wu. The influence of land use change on the spatial–temporal variability of habitat quality between 1990 and 2010 in Northeast China. J. of Forestry Res., 30(6):2227-2236, 2019. Google Scholar
  5. Tianqi Gao, Hao Li, Maoguo Gong, Mingyang Zhang, and Wenyuan Qiao. Superpixel-based multiobjective change detection based on self-adaptive neighborhood-based binary differential evolution. Expert Systems with Applications, 212:118811, 2023. Google Scholar
  6. T. Hermosilla, M. A. Wulder, J. C. White, N. C. Coops, G. W. Hobart, and L. B. Campbell. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. Int. J. of Digital Earth, 9(11):1035-1054, 2016. Google Scholar
  7. Masroor Hussain, Dongmei Chen, Angela Cheng, Hui Wei, and David Stanley. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS J. of Photogrammetry and Remote Sensing, 80:91-106, 2013. Google Scholar
  8. L. A. James, M. E. Hodgson, S. Ghoshal, and M. M. Latiolais. Geomorphic change detection using historic maps and DEM differencing: The temporal dimension of geospatial analysis. Geomorphology, 137(1):181-198, 2012. Google Scholar
  9. Chenjing Jiao, Magnus Heitzler, and Lorenz Hurni. A survey of road feature extraction methods from raster maps. Transactions in GIS, 25(6):2734-2763, 2021. Google Scholar
  10. Victoria Scherelis, Michael Doering, Marta Antonelli, and Patrick Laube. Hydromorphological Information in Historical Maps of Switzerland: From Map Feature Definition to Ecological Metric Derivation. Annals of the Am. Asso. Geographers, pages 1-18, 2023. Google Scholar
  11. Diego Tonolla, Martin Geilhausen, and Michael Doering. Seven decades of hydrogeomorphological changes in a near‐natural and a hydropower‐regulated pre‐Alpine river floodplain in Western Switzerland. Earth Surface Proc. and Landforms, page 5017, 2020. Google Scholar
  12. Sidi Wu, Magnus Heitzler, and Lorenz Hurni. Leveraging uncertainty estimation and spatial pyramid pooling for extracting hydrological features from scanned historical topographic maps. GIScience & Remote Sensing, 59(1):200-214, 2022. Google Scholar
  13. Song Xiaolu and Cheng Bo. Change detection using change vector analysis from landsat tm images in wuhan. Procedia Environmental Sciences, 11:238-244, 2011. Google Scholar
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