Abstract
Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia.
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Notes
- 1.
The source code necessary for reproducing our results is available at https://github.com/skriegman/ppsn_2016.
- 2.
Raw satellite data was pre-processed by Dr. Jeff Dozier (UCSB) using previously reported techniques and is available upon request.
- 3.
The shape of circles are in reality so-called “small circles,” as they lie on the surface of earth.
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Acknowledgements
Thanks to Dr. Jeff Dozier (UCSB) for posing the high-mountain Asia SWE problem and providing associated datasets.
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Kriegman, S., Szubert, M., Bongard, J.C., Skalka, C. (2016). Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_66
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