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Rapid building detection using machine learning

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Abstract

This work describes algorithms for performing discrete object detection, specifically in the case of buildings, where usually only low quality RGB-only geospatial reflective imagery is available. We utilize new candidate search and feature extraction techniques to reduce the problem to a machine learning (ML) classification task. Here we can harness the complex patterns of contrast features contained in training data to establish a model of buildings. We avoid costly sliding windows to generate candidates; instead we innovatively stitch together well known image processing techniques to produce candidates for building detection that cover 80–85 % of buildings. Reducing the number of possible candidates is important due to the scale of the problem. Each candidate is subjected to classification which, although linear, costs time and prohibits large scale evaluation. We propose a candidate alignment algorithm to boost classification performance to 80–90 % precision with a linear time algorithm and show it has negligible cost. Also, we propose a new concept called a Permutable Haar Mesh (PHM) which we use to form and traverse a search space to recover candidate buildings which were lost in the initial preprocessing phase. All code and datasets from this paper are made available online (http://kdl.cs.umb.edu/w/datasets/ and https://github.com/caitlinkuhlman/ObjectDetectionCLUtility).

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Notes

  1. https://maps.google.com/

  2. http://www.cs.toronto.edu/~vmnih/data/

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Acknowledgments

This work is partially funded by a grant from the National Nuclear Security Agency of the U.S. Department of Energy (grant number: DE-NA0001123) as well as by the National Science Foundation Graduate Research Fellowship Program (grant number: DGE-1356104). This work utilized the supercomputing facilities managed by the Research Computing Department at the University of Massachusetts Boston as well as the resources provided by the Open Science Grid, which is supported by the National Science Foundation and the U.S. Department of Energy’s Office of Science.

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Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA

    Joseph Paul Cohen, Wei Ding & Caitlin Kuhlman

  2. Spatial Information Science and Systems, College of Science, George Mason University, Fairfax, VA, USA

    Aijun Chen & Liping Di

Authors
  1. Joseph Paul Cohen
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  2. Wei Ding
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  3. Caitlin Kuhlman
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  4. Aijun Chen
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  5. Liping Di
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Corresponding authors

Correspondence to Joseph Paul Cohen or Wei Ding.

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Cohen, J.P., Ding, W., Kuhlman, C. et al. Rapid building detection using machine learning. Appl Intell 45, 443–457 (2016). https://doi.org/10.1007/s10489-016-0762-6

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