Parking lot delineation and object detection using a localized Convolutional Neural Network

Daniel Cisek; Jedidiah Dale; Susan Pepper; Manoj Mahajan; Shinjae Yoo

doi:10.1109/NYSDS.2016.7747821

Parking lot delineation and object detection using a localized Convolutional Neural Network

Daniel Cisek
, Jedidiah Dale
, Susan Pepper
, Manoj Mahajan
, Shinjae Yoo

Center for Excellence in Wireless Technology

Saint Joseph's College
University of Pennsylvania
Brookhaven National Laboratory

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

2 Scopus citations

Abstract

Satellite imagery analysis is becoming increasingly important in a variety of fields. Automation of analysts' workflows has the potential to greatly increase their efficiency and effectiveness. Specifically, developing automatic parking lot extraction from overhead imagery has huge importance to predicting stores future sales. However, to the best of our knowledge, there is no prior work for the development of an end-to-end pipeline for this task. This paper outlines a method for automating parking lot extraction from overhead imagery. We address this challenge using the combination of a Convolutional Neural Network (CNN) and various morphological tools of analysis. Our pilot end-to-end pipeline has been shown to be effective even in complex environments, and exhibits promising results for the automation of imagery analysis.

Original language	English
Title of host publication	2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467390514
DOIs	https://doi.org/10.1109/NYSDS.2016.7747821
State	Published - Nov 17 2016
Event	2016 New York Scientific Data Summit, NYSDS 2016 - New York, United States Duration: Aug 14 2016 → Aug 17 2016

Publication series

Name	2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings

Conference

Conference	2016 New York Scientific Data Summit, NYSDS 2016
Country/Territory	United States
City	New York
Period	08/14/16 → 08/17/16

Keywords

Automation
Deep Learning
Geospatial
Neural Network
Satellite Imagery

Access to Document

10.1109/NYSDS.2016.7747821

Cite this

Cisek, D., Dale, J., Pepper, S., Mahajan, M., & Yoo, S. (2016). Parking lot delineation and object detection using a localized Convolutional Neural Network. In 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings Article 7747821 (2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NYSDS.2016.7747821

@inproceedings{64479946acbd401cb424f70d2c6d48eb,

title = "Parking lot delineation and object detection using a localized Convolutional Neural Network",

abstract = "Satellite imagery analysis is becoming increasingly important in a variety of fields. Automation of analysts' workflows has the potential to greatly increase their efficiency and effectiveness. Specifically, developing automatic parking lot extraction from overhead imagery has huge importance to predicting stores future sales. However, to the best of our knowledge, there is no prior work for the development of an end-to-end pipeline for this task. This paper outlines a method for automating parking lot extraction from overhead imagery. We address this challenge using the combination of a Convolutional Neural Network (CNN) and various morphological tools of analysis. Our pilot end-to-end pipeline has been shown to be effective even in complex environments, and exhibits promising results for the automation of imagery analysis.",

keywords = "Automation, Deep Learning, Geospatial, Neural Network, Satellite Imagery",

author = "Daniel Cisek and Jedidiah Dale and Susan Pepper and Manoj Mahajan and Shinjae Yoo",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 New York Scientific Data Summit, NYSDS 2016 ; Conference date: 14-08-2016 Through 17-08-2016",

year = "2016",

month = nov,

day = "17",

doi = "10.1109/NYSDS.2016.7747821",

language = "English",

series = "2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Cisek, D, Dale, J, Pepper, S, Mahajan, M & Yoo, S 2016, Parking lot delineation and object detection using a localized Convolutional Neural Network. in 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings., 7747821, 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2016 New York Scientific Data Summit, NYSDS 2016, New York, United States, 08/14/16. https://doi.org/10.1109/NYSDS.2016.7747821

Parking lot delineation and object detection using a localized Convolutional Neural Network. / Cisek, Daniel; Dale, Jedidiah; Pepper, Susan et al.
2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7747821 (2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings).

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

TY - GEN

T1 - Parking lot delineation and object detection using a localized Convolutional Neural Network

AU - Cisek, Daniel

AU - Dale, Jedidiah

AU - Pepper, Susan

AU - Mahajan, Manoj

AU - Yoo, Shinjae

PY - 2016/11/17

Y1 - 2016/11/17

N2 - Satellite imagery analysis is becoming increasingly important in a variety of fields. Automation of analysts' workflows has the potential to greatly increase their efficiency and effectiveness. Specifically, developing automatic parking lot extraction from overhead imagery has huge importance to predicting stores future sales. However, to the best of our knowledge, there is no prior work for the development of an end-to-end pipeline for this task. This paper outlines a method for automating parking lot extraction from overhead imagery. We address this challenge using the combination of a Convolutional Neural Network (CNN) and various morphological tools of analysis. Our pilot end-to-end pipeline has been shown to be effective even in complex environments, and exhibits promising results for the automation of imagery analysis.

AB - Satellite imagery analysis is becoming increasingly important in a variety of fields. Automation of analysts' workflows has the potential to greatly increase their efficiency and effectiveness. Specifically, developing automatic parking lot extraction from overhead imagery has huge importance to predicting stores future sales. However, to the best of our knowledge, there is no prior work for the development of an end-to-end pipeline for this task. This paper outlines a method for automating parking lot extraction from overhead imagery. We address this challenge using the combination of a Convolutional Neural Network (CNN) and various morphological tools of analysis. Our pilot end-to-end pipeline has been shown to be effective even in complex environments, and exhibits promising results for the automation of imagery analysis.

KW - Automation

KW - Deep Learning

KW - Geospatial

KW - Neural Network

KW - Satellite Imagery

UR - https://www.scopus.com/pages/publications/85006932934

U2 - 10.1109/NYSDS.2016.7747821

DO - 10.1109/NYSDS.2016.7747821

M3 - Conference contribution

AN - SCOPUS:85006932934

T3 - 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings

BT - 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 New York Scientific Data Summit, NYSDS 2016

Y2 - 14 August 2016 through 17 August 2016

ER -

Cisek D, Dale J, Pepper S, Mahajan M, Yoo S. Parking lot delineation and object detection using a localized Convolutional Neural Network. In 2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 7747821. (2016 New York Scientific Data Summit, NYSDS 2016 - Proceedings). doi: 10.1109/NYSDS.2016.7747821

Parking lot delineation and object detection using a localized Convolutional Neural Network

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Keywords

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