A transfer learning approach to parking lot classification in aerial imagery

Cisek, Daniel; Mahajan, M.; Dale, Jedidiah; Pepper, Susan; Lin, Yuewei; Yoo, Shinjae

doi:10.1109/nysds.2017.8085049

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Convolutional neural networks (CNNs) were successfully used to detect parking lot patches from aerial imagery in Reference [19] with a best test classification accuracy of 94.3%. Authors in [20] use a CNN based on AlexNet for occupancy detection over PKLot and their own dataset CNRPark, achieving roughly a 3% overall accuracy improvement compared with previous methods based on two textural descriptors, namely Local Binary Patterns and Local Phase Quantization presented in Reference [9].…”

Section: Methodsmentioning

confidence: 99%

Robust Parking Block Segmentation from a Surveillance Camera Perspective

Hurst-Tarrab¹,

Chang²,

González-Mendoza³

et al. 2020

Applied Sciences

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Parking block regions host dangerous behaviors that can be detected from a surveillance camera perspective. However, these regions are often occluded, subject to ground bumpiness or steep slopes, and thus they are hard to segment. Firstly, the paper proposes a pyramidal solution that takes advantage of satellite views of the same scene, based on a deep Convolutional Neural Network (CNN). Training a CNN from the surveillance camera perspective is rather impossible due to the combinatory explosion generated by multiple point-of-views. However, CNNs showed great promise on previous works over satellite images. Secondly, even though there are many datasets for occupancy detection in parking lots, none of them were designed to tackle the parking block segmentation problem directly. Given the lack of a suitable dataset, we also propose APKLOT, a dataset of roughly 7000 polygons for segmenting parking blocks from the satellite perspective and from the camera perspective. Moreover, our method achieves more than 50% intersection over union (IoU) in all the testing sets, that is, at both the satellite view and the camera view.

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Section: Methodsmentioning

confidence: 99%

Robust Parking Block Segmentation from a Surveillance Camera Perspective

Hurst-Tarrab¹,

Chang²,

González-Mendoza³

et al. 2020

Applied Sciences

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“…and channel member selection Verhetsel, 2005;Maxham et al, 2008;Liu et al, 2016;Mani et al, 2015;Gauri et al, 2017;Katona et al, 2018;Tian et al, 2021;Feng and Fay, 2022), we examine whether a positive correlation exists between parked cars and sales performance. While large companies use reports of the number of vehicles in their parking lots as a crucial variable in developing models to predict store earnings over a given period (Cisek et al, 2017), academia has not paid much attention to the subject. Most papers focus solely on developing models of parking lot classification without emphasis on practical applications (Lechgar et al, 2019;Minetto et al, 2021).…”

Section: Satellite Imagesmentioning

confidence: 99%

“…, 2021; Feng and Fay, 2022), we examine whether a positive correlation exists between parked cars and sales performance. While large companies use reports of the number of vehicles in their parking lots as a crucial variable in developing models to predict store earnings over a given period (Cisek et al. , 2017), academia has not paid much attention to the subject.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Satellite imagery and machine learning for channel member selection

Brei,

Rech,

Bozkaya

et al. 2023

IJRDM

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PurposeThis study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.Design/methodology/approachThe authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.FindingsThe relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.Research limitations/implicationsThe proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.Originality/valueThis research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.

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“…[54] Generic -Detecting moving targets for UAV. [55] Generic -Extracting parking lots from the images taken by UAV optical. sensors.…”

mentioning

confidence: 99%

Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation

Al-Sharman

Zweiri

Jaradat

et al. 2020

IEEE Trans. Instrum. Meas.

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Achieving precise state estimation is needed for the unmanned aerial vehicle to perform a successful flight with a high degree of stability. Nonetheless, obtaining accurate state estimation is considered challenging due to the inaccuracies associated with the measurements of the onboard commercial-offthe-shelf (COTS) Inertial Measurement Unit (IMU). The immense vibration of the vehicle's rotors makes these measurements suffer from issues like; large drifts, biases and immense unpredictable noise sequences. These issues cannot be significantly tackled using classical estimators and an accurate sensor fusion technique needs to be developed. In this paper, a deep learning framework is developed to enhance the performance of the state estimator. A deep neural network (DNN) is trained using a deep-learning-based technique to identify the associated measurement noise models and filter them out. Dropout technique is adopted for training the DNN to avoid overfitting and reduce the complexity of nets computations. Compared to the classical estimation results, the proposed deep learning technique demonstrates capabilities in identifying the measurement's noise characteristics. As an example, an enhancement in estimating the attitude states at near hover is proved using this approach. Furthermore, an actual hover flight was performed to validate the proposed estimation enhancement method.

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A transfer learning approach to parking lot classification in aerial imagery

Cited by 5 publications

References 12 publications

Robust Parking Block Segmentation from a Surveillance Camera Perspective

Robust Parking Block Segmentation from a Surveillance Camera Perspective

Satellite imagery and machine learning for channel member selection

Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation

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