Related papers: Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning

Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning

URL: http://arxiv.org/abs/2010.14300v1
Date: Tue, 27 Oct 2020 14:02:00 GMT
Title: Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning
Authors: Manu Tom and Rajanie Prabha and Tianyu Wu and Emmanuel Baltsavias and Laura Leal-Taixe and Konrad Schindler
Abstract summary: Multi-temporal satellite images publicly available webcam images are among a viable data sources to monitor lake ice. In this work we investigate machine learning-based image analysis as a tool to determine the extent of ice on Swiss Alpine lakes. We model lake ice monitoring as a pixel-wise semantic segmentation problem, i.e., each pixel on the lake surface is classified to obtain a spatially explicit map of ice cover.
Score: 16.71718177904522
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set up operational monitoring capabilities. Multi-temporal satellite images and publicly available webcam streams are among the viable data sources to monitor lake ice. In this work we investigate machine learning-based image analysis as a tool to determine the spatio-temporal extent of ice on Swiss Alpine lakes as well as the ice-on and ice-off dates, from both multispectral optical satellite images (VIIRS and MODIS) and RGB webcam images. We model lake ice monitoring as a pixel-wise semantic segmentation problem, i.e., each pixel on the lake surface is classified to obtain a spatially explicit map of ice cover. We show experimentally that the proposed system produces consistently good results when tested on data from multiple winters and lakes. Our satellite-based method obtains mean Intersection-over-Union (mIoU) scores >93%, for both sensors. It also generalises well across lakes and winters with mIoU scores >78% and >80% respectively. On average, our webcam approach achieves mIoU values of 87% (approx.) and generalisation scores of 71% (approx.) and 69% (approx.) across different cameras and winters respectively. Additionally, we put forward a new benchmark dataset of webcam images (Photi-LakeIce) which includes data from two winters and three cameras.

Related papers

Multiple Random Masking Autoencoder Ensembles for Robust Multimodal Semi-supervised Learning [64.81450582542878]
There is an increasing number of real-world problems in computer vision and machine learning. In the case of Earth Observations from satellite data, it is important to be able to predict one observation layer.
arXiv Detail & Related papers (2024-02-12T20:08:58Z)
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting [52.77986479871782]
Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts. In this work, we investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin and improve its quality by using additional weather data and training on multiple regions.
arXiv Detail & Related papers (2022-10-17T09:14:35Z)
CVLNet: Cross-View Semantic Correspondence Learning for Video-based Camera Localization [89.69214577915959]
This paper tackles the problem of Cross-view Video-based camera localization. We propose estimating the query camera's relative displacement to a satellite image before similarity matching. Experiments have demonstrated the effectiveness of video-based localization over single image-based localization.
arXiv Detail & Related papers (2022-08-07T07:35:17Z)
Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network [55.41644538483948]
We propose to aggregate simulations of lake temperature at a certain depth together with a range of meteorological features. This work demonstrates that the proposed model can deliver homogeneously good performance covering the whole lake surface. Results are compared with a state-of-the-art Bayesian deep learning method.
arXiv Detail & Related papers (2021-09-27T09:19:53Z)
Learning a Sensor-invariant Embedding of Satellite Data: A Case Study for Lake Ice Monitoring [19.72060218456938]
We learn a joint, sensor-invariant embedding within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. By fusing satellite data, we map lake ice at a temporal resolution of 1.5 days.
arXiv Detail & Related papers (2021-07-19T18:11:55Z)
Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery [19.72060218456938]
Depleting lake ice can serve as an indicator for climate change, just like sea level rise or glacial retreat. Several Lake Ice Phenological (LIP) events serve as sentinels to understand the regional and global climate change. We focus on observing the LIP events such as freeze-up, break-up and temporal freeze extent in the Oberengadin region of Switzerland.
arXiv Detail & Related papers (2021-03-23T10:25:02Z)
Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset [67.44497676652173]
We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 $km2$, sampled from three Swiss cities. The dataset is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras.
arXiv Detail & Related papers (2020-12-23T21:48:47Z)
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)
Integrated monitoring of ice in selected Swiss lakes. Final project report [0.36700088931938823]
Lakes (and as part of them lake ice) is considered an Essential Climate Variable (ECV) of the Global Climate Observing System (GCOS) MeteoSwiss supported this 2-year project to explore not only the use of satellite images but also the possibilities of Webcams and in-situ measurements. The aim of this project is to monitor some target lakes and detect the extent of ice and especially the ice-on/off dates.
arXiv Detail & Related papers (2020-08-02T16:18:51Z)
Lake Ice Monitoring with Webcams and Crowd-Sourced Images [15.616179111087053]
This paper moves towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise annotations across different winters and different lakes. We introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth.
arXiv Detail & Related papers (2020-02-18T20:46:46Z)
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.