Image of Machine learning – An approach for consistent rock glacier mapping and inventorying – Example of Austria

Text

Machine learning – An approach for consistent rock glacier mapping and inventorying – Example of Austria

Rock glaciers (RG) are landforms that occur in high latitudes or elevations and — in their active state — consist of a mixture of rock debris and ice. Despite serving as a form of groundwater storage, they are an indicator for the occurrence of (former) permafrost and therefore carry significance in the research for the ongoing climate change. For these reasons, the past years have shown rising interest in the establishment of RG inventories to investigate the extent of permafrost and quantify water storages. Creating these inventories, however, usually involves manual, laborious, and subjective mapping of the landforms based on aerial image - and digital elevation model analysis. We propose an approach for RG mapping based on supervised machine learning which can help to increase the mapping efficiency and permits rapid RG mapping in vast and not yet covered areas. We found deep convolutional artificial neural networks (ANN) that are specifically designed for image segmentation (U-Net architecture) to be well suited for this classification problem. The general workflow consists of training the ANNs with orthophotos and slope maps of digital elevation models as input. The output (RG label-maps) is derived from a recently published RG inventory of the Austrian Alps that features 5769 individual RGs and was compiled manually by several scientists. To increase the generalization capabilities, we use live data augmentation during training. Based on this inventory, the ANNs have learned the average expert opinion and the RG map generated by the ANN can be used to increase the consistency and completeness of already existing RG inventories. Moreover, this ANN approach might be valuable for other landform mapping tasks beyond rock glaciers (e.g., other mass movements).

Ketersediaan
144551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
12 hlm PDF, 13.829 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.16, December 2022
Subjek
Machine Learning
Image segmentation
Rock glacier inventory
Permafrost
Hydrological catchment
Digital mapping
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Machine learning – An approach for consistent rock glacier mapping and inventorying – Example of Austria
    Rock glaciers (RG) are landforms that occur in high latitudes or elevations and — in their active state — consist of a mixture of rock debris and ice. Despite serving as a form of groundwater storage, they are an indicator for the occurrence of (former) permafrost and therefore carry significance in the research for the ongoing climate change. For these reasons, the past years have shown rising interest in the establishment of RG inventories to investigate the extent of permafrost and quantify water storages. Creating these inventories, however, usually involves manual, laborious, and subjective mapping of the landforms based on aerial image - and digital elevation model analysis. We propose an approach for RG mapping based on supervised machine learning which can help to increase the mapping efficiency and permits rapid RG mapping in vast and not yet covered areas. We found deep convolutional artificial neural networks (ANN) that are specifically designed for image segmentation (U-Net architecture) to be well suited for this classification problem. The general workflow consists of training the ANNs with orthophotos and slope maps of digital elevation models as input. The output (RG label-maps) is derived from a recently published RG inventory of the Austrian Alps that features 5769 individual RGs and was compiled manually by several scientists. To increase the generalization capabilities, we use live data augmentation during training. Based on this inventory, the ANNs have learned the average expert opinion and the RG map generated by the ANN can be used to increase the consistency and completeness of already existing RG inventories. Moreover, this ANN approach might be valuable for other landform mapping tasks beyond rock glaciers (e.g., other mass movements).
Komentar
Anda harus masuk sebelum memberikan komentar