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Parking Lot Instance Segmentation from Satellite Imagery through Associative Embeddings

Published: 05 November 2019 Publication History

Abstract

In this paper, we apply the technique of instance segmentation through associative image embeddings using stacked hourglass networks to the problem of parking lot instance segmentation in satellite imagery. We sought an instance segmentation method that, unlike other common methods such as Mask R-CNN, was independent of object classification and robust to missing labeled instances. We demonstrate how associative image embeddings can be created, which can provide instance segmentation for use with any semantic segmentation map.

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  • (2021)UVLensProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34634955:2(1-26)Online publication date: 24-Jun-2021

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cover image ACM Conferences
SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 2019
648 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 05 November 2019

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Author Tags

  1. associative embedding
  2. deep learning
  3. instance segmentation
  4. parking lots
  5. satellite imagery

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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  • (2021)UVLensProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34634955:2(1-26)Online publication date: 24-Jun-2021

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