Machine Learning-Based Active Layer Thickness Estimation over Permafrost Landscapes by Upscaling Airborne Remote Sensing Measurements with Cloud-Computing Geotechnologies
Earth observation (EO) plays a pivotal role in understanding our planet’s rapidly changing environment. Recently, geospatial technologies used to analyse EO data have made remarkable progress, in particular from innovations in Artificial Intelligence (AI) and scalable cloud-computing reso...
| Main Authors: | , |
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| Format: | Book Part |
| Language: | English |
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IntechOpen
2024
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| Subjects: | |
| Online Access: | https://mts.intechopen.com/articles/show/title/ https://doi.org/10.5772/intechopen.1004315 |
| Summary: | Earth observation (EO) plays a pivotal role in understanding our planet’s rapidly changing environment. Recently, geospatial technologies used to analyse EO data have made remarkable progress, in particular from innovations in Artificial Intelligence (AI) and scalable cloud-computing resources. This chapter presents a brief overview of these developments, with a focus on geospatial “big data.” A case study is presented where Google Earth Engine (GEE) was used to upscale airborne active layer thickness (ALT) measurements over an extensive permafrost region. GEE’s machine learning (ML) capabilities were leveraged for upscaling measurements to several multi-source satellite EO datasets. Novel Explainable Artificial Intelligence (XAI) techniques were also used for model feature selection and interpretation. The optimized ML model achieved an R 2 of 0.476, although performance varied by ecosystem. This chapter highlights the capabilities of new RS sensors and geospatial technologies for better understanding permafrost environments, which is important in the face of climate change. |
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