Multilevel Semantic Labeling of Mobile Homes from Overhead Imagery

Conference · Sun Jul 01 00:00:00 EDT 2018

DOI:https://doi.org/10.1109/IGARSS.2018.8517660· OSTI ID:1474650

Lunga, Dalton  ^[1]; Seals, Matthew ^[1]; Bhaduri, Budhu  ^[1]

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1. ORNL

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. Leveraging on the availability of high resolution satellite imagery, advances in machine learning and high performance computing hardware, it is now possible 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 to our future goal for deploying very large scale deep convolutional neural networks toward both broad and finer characterization of man-made structures from one-meter resolution NAIP images.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1474650
• Resource Relation: Conference: 38th annual symposium of the IEEE Geoscience and Remote Sensing Society - Valencia, , Spain - 7/23/2018 4:00:00 AM-7/27/2018 4:00:00 AM
• Country of Publication: United States
• Language: English