Multilevel semantic labeling of mobile homes from overhead imagery

Dalton Lunga; Matthew Seals; Budhendra Bhaduri

doi:10.1109/IGARSS.2018.8517660

Multilevel semantic labeling of mobile homes from overhead imagery

Dalton Lunga, Matthew Seals, Budhendra Bhaduri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Finding where people live and the vulnerabilities of man-made facilities during natural disasters is not only critical for rescue efforts but also essential for damage assessment in the aftermath. New advances from machine learning and high performance computing are leveraging on the availability of high resolution satellite imagery to generate geographical maps for man-made facilities at scale. Mapping from satellite imagery can be a daunting task due to the enormous amount of data to be processed over large areas. In this short paper we take advantage of annotated satellite imagery and automate the semantic labeling of mobile home parks using an efficient framework rooted in patch-based and pixel-level classification. This multilevel labeling effort is a precursor for deploying very large scale deep convolutional neural networks toward broad and finer characterization of man-made structures from one-meter resolution NAIP images.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6931-6934
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8517660
State	Published - Oct 31 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: Jul 22 2018 → Jul 27 2018

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2018-July

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	07/22/18 → 07/27/18

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Funders	Funder number
U.S. Department of Energy
Battelle	DE-AC05-00OR22725

Keywords

Aerial imagery
Convolutional neural networks
Deep learning
Mobile home park
Multilevel
Remote sensing
Semantic segmentation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IGARSS.2018.8517660

Cite this

Lunga, D., Seals, M., & Bhaduri, B. (2018). Multilevel semantic labeling of mobile homes from overhead imagery. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 6931-6934). Article 8517660 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8517660

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abstract = "Finding where people live and the vulnerabilities of man-made facilities during natural disasters is not only critical for rescue efforts but also essential for damage assessment in the aftermath. New advances from machine learning and high performance computing are leveraging on the availability of high resolution satellite imagery to generate geographical maps for man-made facilities at scale. Mapping from satellite imagery can be a daunting task due to the enormous amount of data to be processed over large areas. In this short paper we take advantage of annotated satellite imagery and automate the semantic labeling of mobile home parks using an efficient framework rooted in patch-based and pixel-level classification. This multilevel labeling effort is a precursor for deploying very large scale deep convolutional neural networks toward broad and finer characterization of man-made structures from one-meter resolution NAIP images.",

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Lunga, D, Seals, M & Bhaduri, B 2018, Multilevel semantic labeling of mobile homes from overhead imagery. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8517660, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 6931-6934, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 07/22/18. https://doi.org/10.1109/IGARSS.2018.8517660

Multilevel semantic labeling of mobile homes from overhead imagery. / Lunga, Dalton; Seals, Matthew; Bhaduri, Budhendra.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 6931-6934 8517660 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Finding where people live and the vulnerabilities of man-made facilities during natural disasters is not only critical for rescue efforts but also essential for damage assessment in the aftermath. New advances from machine learning and high performance computing are leveraging on the availability of high resolution satellite imagery to generate geographical maps for man-made facilities at scale. Mapping from satellite imagery can be a daunting task due to the enormous amount of data to be processed over large areas. In this short paper we take advantage of annotated satellite imagery and automate the semantic labeling of mobile home parks using an efficient framework rooted in patch-based and pixel-level classification. This multilevel labeling effort is a precursor for deploying very large scale deep convolutional neural networks toward broad and finer characterization of man-made structures from one-meter resolution NAIP images.

AB - Finding where people live and the vulnerabilities of man-made facilities during natural disasters is not only critical for rescue efforts but also essential for damage assessment in the aftermath. New advances from machine learning and high performance computing are leveraging on the availability of high resolution satellite imagery to generate geographical maps for man-made facilities at scale. Mapping from satellite imagery can be a daunting task due to the enormous amount of data to be processed over large areas. In this short paper we take advantage of annotated satellite imagery and automate the semantic labeling of mobile home parks using an efficient framework rooted in patch-based and pixel-level classification. This multilevel labeling effort is a precursor for deploying very large scale deep convolutional neural networks toward broad and finer characterization of man-made structures from one-meter resolution NAIP images.

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Lunga D, Seals M, Bhaduri B. Multilevel semantic labeling of mobile homes from overhead imagery. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 6931-6934. 8517660. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2018.8517660

Multilevel semantic labeling of mobile homes from overhead imagery

Abstract

Publication series

Conference

Funding

Keywords

UN SDGs

Access to Document

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