ORCID Profile
0000-0002-5331-9897
Current Organisation
University of Melbourne
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Publisher: MDPI AG
Date: 02-10-2023
DOI: 10.3390/RS15194804
Publisher: MDPI AG
Date: 12-2022
DOI: 10.3390/RS14236095
Abstract: Floods and droughts cause catastrophic damage in paddy fields, and farmers need to be compensated for their loss. Mobile applications have allowed farmers to claim losses by providing mobile photos and polygons of their land plots drawn on satellite base maps. This paper studies erse methods to verify those claims at a parcel level by employing (i) Normalized Difference Vegetation Index (NDVI) and (ii) Normalized Difference Water Index (NDWI) on Sentinel-2A images, (iii) Classification and Regression Tree (CART) on Sentinel-1 SAR GRD images, and (iv) a convolutional neural network (CNN) on mobile photos. To address the disturbance from clouds, we study the combination of multi-modal methods—NDVI+CNN and NDWI+CNN—that allow 86.21% and 83.79% accuracy in flood detection and 73.40% and 81.91% in drought detection, respectively. The SAR-based method outperforms the other methods in terms of accuracy in flood (98.77%) and drought (99.44%) detection, data acquisition, parcel coverage, cloud disturbance, and observing the area proportion of disasters in the field. The experiments conclude that the method of CART on SAR images is the most reliable to verify farmers’ claims for compensation. In addition, the CNN-based method’s performance on mobile photos is adequate, providing an alternative for the CART method in the case of data unavailability while using SAR images.
Publisher: Copernicus GmbH
Date: 14-10-2022
DOI: 10.5194/ISPRS-ANNALS-X-4-W3-2022-181-2022
Abstract: Abstract. An accurate detection, classification, and tracking of vehicles are highly important for intelligent transport systems (ITS) and road maintenance. In recent years, the deep learning (DL)-based approach is highly regarded for real-time vehicle classification from surveillance cameras. However, the practical implementation of such an approach is affected by the adverse lighting conditions and positioning of the cameras. In this research, we develop a DL-based method for near real-time multi-vehicle counting, classifying, and tracking on in idual lanes of the road. First, we train a DL network of the You Only Look Once (YOLO) family on a custom dataset that we have curated. The dataset consists of nearly 30000 training s les to classify the vehicles into seven classes, which is more than in the existing benchmark datasets. Second, we fine-tune the trained model into another small dataset collected from the surveillance cameras that are used during the implementation process. Third, we connect the trained model to a tracking algorithm that we have developed to produce a per-lane report with the calculation of the speed and mobility of the vehicles. We test the robustness of the system on different faces of the vehicles and in adverse lighting conditions. The overall accuracy (OA) of classification ranges from 91% to 99% in four faces of vehicles (back, front, driver side, and passenger side). Similarly, in an experiment on adverse lighting conditions, OA of 93.7% and 99.6% is observed in a noisy and clear lighting conditions respectively. The implications of these results will assist in road maintenance with spatial information management and sensing for intelligent transport planning.
Publisher: MDPI AG
Date: 23-02-2021
DOI: 10.3390/RS13040808
Abstract: Availability of very high-resolution remote sensing images and advancement of deep learning methods have shifted the paradigm of image classification from pixel-based and object-based methods to deep learning-based semantic segmentation. This shift demands a structured analysis and revision of the current status on the research domain of deep learning-based semantic segmentation. The focus of this paper is on urban remote sensing images. We review and perform a meta-analysis to juxtapose recent papers in terms of research problems, data source, data preparation methods including pre-processing and augmentation techniques, training details on architectures, backbones, frameworks, optimizers, loss functions and other hyper-parameters and performance comparison. Our detailed review and meta-analysis show that deep learning not only outperforms traditional methods in terms of accuracy, but also addresses several challenges previously faced. Further, we provide future directions of research in this domain.
Publisher: Public Library of Science (PLoS)
Date: 17-10-2019
Publisher: MDPI AG
Date: 13-01-2023
DOI: 10.3390/RS15020488
Abstract: Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The evolving FCNs suffer from the inadequate use of multi-scale feature maps in their backbone of convolutional neural networks (CNNs). Furthermore, the DL methods are not robust in cross-domain settings due to domain-shift problems. Two scale-robust novel networks, namely MSA-UNET and MSA-ResUNET, are developed in this study by aggregating the multi-scale feature maps in U-Net and ResUNET with partial concepts of the feature pyramid network (FPN). Furthermore, supervised domain adaptation is investigated to minimise the effects of domain-shift between the two datasets. The datasets include the benchmark WHU Building dataset and a developed dataset with 5× fewer s les, 4× lower spatial resolution and complex high-rise buildings and skyscrapers. The newly developed networks are compared to six state-of-the-art FCNs using five metrics: pixel accuracy, adjusted accuracy, F1 score, intersection over union (IoU), and the Matthews Correlation Coefficient (MCC). The proposed networks outperform the FCNs in the majority of the accuracy measures in both datasets. Compared to the larger dataset, the network trained on the smaller one shows significantly higher robustness in terms of adjusted accuracy (by 18%), F1 score (by 31%), IoU (by 27%), and MCC (by 29%) during the cross-domain validation of MSA-UNET. MSA-ResUNET shows similar improvements, concluding that the proposed networks when trained using domain adaptation increase the robustness and minimise the domain-shift between the datasets of different complexity.
Publisher: ACM
Date: 07-2020
Publisher: IEEE
Date: 07-2019
Publisher: MDPI AG
Date: 18-05-2022
DOI: 10.3390/S22103813
Abstract: Accurate vehicle classification and tracking are increasingly important subjects for intelligent transport systems (ITSs) and for planning that utilizes precise location intelligence. Deep learning (DL) and computer vision are intelligent methods however, accurate real-time classification and tracking come with problems. We tackle three prominent problems (P1, P2, and P3): the need for a large training dataset (P1), the domain-shift problem (P2), and coupling a real-time multi-vehicle tracking algorithm with DL (P3). To address P1, we created a training dataset of nearly 30,000 s les from existing cameras with seven classes of vehicles. To tackle P2, we trained and applied transfer learning-based fine-tuning on several state-of-the-art YOLO (You Only Look Once) networks. For P3, we propose a multi-vehicle tracking algorithm that obtains the per-lane count, classification, and speed of vehicles in real time. The experiments showed that accuracy doubled after fine-tuning (71% vs. up to 30%). Based on a comparison of four YOLO networks, coupling the YOLOv5-large network to our tracking algorithm provided a trade-off between overall accuracy (95% vs. up to 90%), loss (0.033 vs. up to 0.036), and model size (91.6 MB vs. up to 120.6 MB). The implications of these results are in spatial information management and sensing for intelligent transport planning.
Publisher: Copernicus GmbH
Date: 14-10-2022
DOI: 10.5194/ISPRS-ANNALS-X-4-W3-2022-173-2022
Abstract: Abstract. Earth observation data including very high-resolution (VHR) imagery from satellites and unmanned aerial vehicles (UAVs) are the primary sources for highly accurate building footprint segmentation and extraction. However, with the increase in spatial resolution, smaller objects are prominently visible in the images, and using intelligent approaches like deep learning (DL) suffers from several problems. In this paper, we outline four prominent problems while using DL-based methods (P1, P2, P3, and P4): (P1) lack of contextual features, (P2) requirement of a large training dataset, (P3) domain-shift problem, and (P4) computational expense. In tackling P1, we modify a commonly used DL architecture called U-Net to increase the contextual feature information. Likewise, for P2 and P3, we use transfer learning to fine-tune the DL model on a smaller dataset utilising the knowledge previously gained from a larger dataset. For P4, we study the trade-off between the network’s performance and computational expense with reduced training parameters and optimum learning rates. Our experiments on a case study from the City of Melbourne show that the modified U-Net is highly robust than the original U-Net and SegNet, and the dataset we develop is significantly more robust than an existing benchmark dataset. Furthermore, the overall method of fine-tuning the modified U-Net reduces the number of training parameters by 300 times and training time by 2.5 times while preserving the precision of segmentation.
Publisher: IEEE
Date: 07-2022
Publisher: IEEE
Date: 11-2018
Location: Thailand
No related grants have been discovered for Bipul Neupane.