Towards Global Glacier Mapping with Deep Learning and Open Earth Observation Data
- URL: http://arxiv.org/abs/2401.15113v2
- Date: Wed, 29 May 2024 15:58:03 GMT
- Title: Towards Global Glacier Mapping with Deep Learning and Open Earth Observation Data
- Authors: Konstantin A. Maslov, Claudio Persello, Thomas Schellenberger, Alfred Stein,
- Abstract summary: Glacier-VisionTransformer-U-Net (GlaViTU) is a convolutional-transformer deep learning model.
Adding synthetic aperture radar data, namely, backscatter and interferometric coherence, increases the accuracy in all regions where available.
We release a benchmark dataset that covers 9% of glaciers worldwide.
- Score: 0.718723384367814
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Accurate global glacier mapping is critical for understanding climate change impacts. Despite its importance, automated glacier mapping at a global scale remains largely unexplored. Here we address this gap and propose Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep learning model, and five strategies for multitemporal global-scale glacier mapping using open satellite imagery. Assessing the spatial, temporal and cross-sensor generalisation shows that our best strategy achieves intersection over union >0.85 on previously unobserved images in most cases, which drops to >0.75 for debris-rich areas such as High-Mountain Asia and increases to >0.90 for regions dominated by clean ice. A comparative validation against human expert uncertainties in terms of area and distance deviations underscores GlaViTU performance, approaching or matching expert-level delineation. Adding synthetic aperture radar data, namely, backscatter and interferometric coherence, increases the accuracy in all regions where available. The calibrated confidence for glacier extents is reported making the predictions more reliable and interpretable. We also release a benchmark dataset that covers 9% of glaciers worldwide. Our results support efforts towards automated multitemporal and global glacier mapping.
Related papers
- FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - Residual Diffusion Modeling for Km-scale Atmospheric Downscaling [51.061954281398116]
A cost-effective downscaling model is trained from a high-resolution 2-km weather model over Taiwan.
textitCorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes.
Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.
arXiv Detail & Related papers (2023-09-24T19:57:22Z) - AMD-HookNet for Glacier Front Segmentation [17.60067480799222]
knowledge on changes in glacier calving front positions is important for assessing the status of glaciers.
Deep learning-based methods have shown great potential for glacier calving front delineation from optical and radar satellite imagery.
We propose Attention-Multi-hooking-Deep-supervision HookNet (AMD-HookNet), a novel glacier calving front segmentation framework.
arXiv Detail & Related papers (2023-02-06T12:39:40Z) - Boundary Aware U-Net for Glacier Segmentation [1.1715858161748574]
We propose a modified version of the U-Net for large-scale, spatially non-overlapping, clean glacial ice, and debris-covered glacial ice segmentation.
We introduce a novel self-learning boundary-aware loss to improve debris-covered glacial ice segmentation performance.
We conclude that red, shortwave infrared, and near-infrared bands have the highest contribution toward debris-covered glacial ice segmentation from Landsat 7 images.
arXiv Detail & Related papers (2023-01-26T22:58:23Z) - GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data.
It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute.
We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
arXiv Detail & Related papers (2022-12-24T18:15:39Z) - GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine
Glacier Mapping [5.953569982292301]
Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change.
accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques.
The objective of this research is to improve upon an earlier proposed deep-learning-based approach, GlacierNet, which was developed to exploit a convolutional neural-network segmentation model to accurately outline regional DCG ablation zones.
arXiv Detail & Related papers (2022-04-06T14:39:34Z) - Jalisco's multiclass land cover analysis and classification using a
novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis.
In this work, we combine three real-world open data sources to obtain 13 channels.
Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
arXiv Detail & Related papers (2022-01-26T14:58:51Z) - Country-wide Retrieval of Forest Structure From Optical and SAR
Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution.
Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables.
We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
arXiv Detail & Related papers (2021-11-25T16:21:28Z) - Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya [54.12023102155757]
Glacier mapping is key to ecological monitoring in the hkh region.
Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems.
We present a machine learning based approach to support ecological monitoring, with a focus on glaciers.
arXiv Detail & Related papers (2020-12-09T12:48:06Z) - Lake Ice Detection from Sentinel-1 SAR with Deep Learning [15.493845481313924]
We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network.
We cast ice detection as a two class (frozen, non-frozen) semantic problem and solve it using a state-of-the-art deep convolutional network (CNN)
We report results on two winters 2016 - 17 and 2017 - 18 and three alpine lakes in Switzerland.
arXiv Detail & Related papers (2020-02-17T16:31:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.