You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Deep Learning in Remote Sensing: An Application to Detect Snow and Water in Construction Sites

Publication Type:

Conference/Workshop Paper

Venue:

4th International Conference on Artificial Intelligence for Industries


Abstract

It is important for a construction and property development company to know weather conditions in their daily operation. In this paper, a deep learning-based approach is investigated to detect snow and rain conditions in construction sites using drone imagery. A Convolutional Neural Network (CNN) is developed for the feature extraction and performing classification on those features using machine learning (ML) algorithms. Well-known existing deep learning algorithms AlexNet and VGG16 models are also deployed and tested on the dataset. Results show that smaller CNN architecture with three convolutional layers was sufficient at extracting relevant features to the classification task at hand compared to the larger state-of-the-art architectures. The proposed model reached a top accuracy of 97.3% in binary classification and 96.5% while also taking rain conditions into consideration. It was also found that ML algorithms, i.e., support vector machine (SVM), logistic regression and k-nearest neighbors could be used as classifiers using feature maps extracted from CNNs and a top accuracy of 90% was obtained using SVM algorithms.

Bibtex

@inproceedings{Rahman6265,
author = {Hamidur Rahman and Mobyen Uddin Ahmed and Shahina Begum and Mats Fridberg and Adam Hoflin},
title = {Deep Learning in Remote Sensing: An Application to Detect Snow and Water in Construction Sites},
month = {September},
year = {2021},
booktitle = {4th International Conference on Artificial Intelligence for Industries },
url = {http://www.es.mdh.se/publications/6265-}
}