Lingcao Huang

Lingcao Huang

He/His

Remote Sensing Scientist

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Synchrotron X-ray imaging in 4D: Multiscale failure and compaction localization in triaxially compressed porous limestone

Published in EARTH AND PLANETARY SCIENCE LETTERS, 2019

Strain localization was captured from the grain to continuum scale in a limestone.

Recommended citation: Huang, L., Baud, P., Cordonnier, B., Renard, F., Liu, L., Wong, T., 2019. Synchrotron X-ray imaging in 4D: Multiscale failure and compaction localization in triaxially compressed porous limestone. Earth Planet Sc Lett 528, 115831.
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Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images

Published in REMOTE SENSING OF ENVIRONMENT, 2019

This paper is the first attempt to automatically delineate permafrost-thaw landslides from high-resolution (3-m) satellite imagery.

Recommended citation: Huang, L., Luo, J., Lin, Z., Niu, F., Liu, L., 2020. Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images. Remote Sens Environ 237, 111534.
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An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery

Published in REMOTE SENSING OF ENVIRONMENT, 2020

This paper integrates seven satellite datasets into a single deep learning network and offers a sub-weekly calving front dataset.

Recommended citation: Zhang, E., Liu, L., Huang, L., Ng, K.S., 2021. An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery. Remote Sens Environ 254, 112265.
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Detecting Changes of Retrogressive Thaw Slumps from Satellite Images Using Siamese Neural Network

Published in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2021

Testing a idea of change detection of thaw slumps using a Siamese neural network

Recommended citation: Huang, L., Liu, L., 2020. Detecting Changes of Retrogressive Thaw Slumps from Satellite Images Using Siamese Neural Network. Igarss 2020 - 2020 Ieee Int Geoscience Remote Sens Symposium 00, 3090–3093.
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Identifying active retrogressive thaw slumps from ArcticDEM

Published in ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2023

This paper processed the entire ArcticDEM (~200 TB) and is the first attempt to identify abrupt permafrost thaw (retrogressive thaw slumps) using supercomputers and deep learning at a panArctic scale.

Recommended citation: Huang, L., Willis, M.J., Li, G., Lantz, T.C., Schaefer, K., Wig, E., Cao, G., Tiampo, K.F., 2023. Identifying active retrogressive thaw slumps from ArcticDEM. ISPRS J. Photogramm. Remote Sens. 205, 301–316.
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talks

Mapping Abrupt Permafrost Thaw from Space Using Machine Learning

Published:

Permafrost is undergoing strong warming and thawing in recent decades, which can potentially cause unprecedented environmental consequences as it stores a large amount of carbon and underlays about 25% of the exposed land in the northern hemisphere. Abrupt thawing of ice-rich permafrost may result in landforms such as retrogressive thaw slumps and thermo-erosion gullies on the Earth’s surface. As reported by many local studies, the occurrence and frequency of abrupt permafrost thaw have increased significantly. However, its spatial distribution and development in most permafrost areas are poorly quantified, which hinders the understanding of permafrost degradation in large areas. The reason for this is the difficultly of analyzing big data and extracting small and subtle features on images. In the past decades, the accumulation of satellite data and the advance of machine learning algorithms, especially convolutional neural networks, make it possible to tackle this problem. In this talk, I will present my research of using high-resolution (=3 m) satellite data and machine learning algorithms to map these landforms in Tibet and Alaska north slope. Please find the recording at Youtube

teaching