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 |
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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 | |
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) |
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Volume | 2018-July |
Conference
Conference | 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 |
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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.
Keywords
- Aerial imagery
- Convolutional neural networks
- Deep learning
- Mobile home park
- Multilevel
- Remote sensing
- Semantic segmentation