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Article ; Online: Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering

KC, Ujjwal / Aryal, Jagannath

Fire. 2022 Dec. 09, v. 5, no. 6

2022

Abstract: Fire authorities have started widely using operational fire simulations for effective wildfire management. The aggregation of the simulation outputs on a massive scale creates an opportunity to apply the evolving data-driven approach to closely estimate ... ...

Abstract	Fire authorities have started widely using operational fire simulations for effective wildfire management. The aggregation of the simulation outputs on a massive scale creates an opportunity to apply the evolving data-driven approach to closely estimate wildfire risks even without running computationally expensive simulations. In one of our previous works, we validated the application with a probability-based risk metric that gives a series of probability values for a fire starting at a start location under a given weather condition. The probability values indicate how likely it is that a fire will fall into different risk categories. The metric considered each fire start location as a unique entity. Such a provision in the metric could expose the metric to scalability issues when the metric is used for a larger geographic area and consequently make the metric hugely intensive to compute. In this work, in an investigative effort, we investigate whether the spatial clustering of fire start locations based on historical fire areas can address the issue without significantly compromising the accuracy of the metric. Our results show that spatially clustering all fire start locations in Tasmania into three risk clusters could leverage the probability-based risk metric by reducing the computational requirements of the metric by a theoretical factor in thousands with a mere compromise of approximately 5% in accuracy for two risk categories of high and low, thereby validating the possibility of the leverage of the metric with spatial clustering.
Keywords	prediction ; risk ; weather ; wildfires ; wildland fire management ; Tasmania
Language	English
Dates of publication	2022-1209
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ISSN	2571-6255
DOI	10.3390/fire5060213
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia.

Aryal, Jagannath / Sitaula, Chiranjibi / Frery, Alejandro C

Scientific reports

2023 Volume 13, Issue 1, Page(s) 13510

Abstract: Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been ...

Abstract	Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been made considering spectral (Sp), statistical (St), and index-based (Ind) features in developing LULC maps for city planning. However, no work has been reported to automate LULC performance modeling for their robustness with machine learning (ML) algorithms. In this paper, we design seven schemes and automate the LULC performance modeling with six ML algorithms-Random Forest, Support Vector Machine with Linear kernel, Support Vector Machine with Radial basis function kernel, Artificial Neural Network, Naïve Bayes, and Generalised Linear Model for the city of Melbourne, Australia on Sentinel-2A images. Experimental results show that the Random Forest outperforms remaining ML algorithms in the classification accuracy (0.99) on all schemes. The robustness and statistical analysis of the ML algorithms (for example, Random Forest imparts over 0.99 F1-score for all five categories and p value [Formula: see text] 0.05 from Wilcoxon ranked test over accuracy measures) against varying training splits demonstrate the effectiveness of the proposed schemes. Thus, providing a robust measure of LULC maps in city planning.
Language	English
Publishing date	2023-08-19
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-40564-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network.

Neupane, Bipul / Horanont, Teerayut / Aryal, Jagannath

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 10

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; ... ...

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 samples 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.
MeSH term(s)	Algorithms ; Deep Learning ; Humans ; Neural Networks, Computer
Language	English
Publishing date	2022-05-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22103813
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia

Aryal, Jagannath / Sitaula, Chiranjibi / Aryal, Sunil

Land. 2022 Feb. 27, v. 11, no. 3

2022

Abstract: Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning ... ...

Abstract	Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning urban lives, and availability of state-of-the-art remote sensing data and technologies in open access forms, in this work, we develop a simple three-level hierarchical mapping of urban green space with multiple usability to various stakeholders. We utilize the established Normalized Difference Vegetation Index (NDVI) threshold on Sentinel-2A Earth Observation image data to classify the urban vegetation of each Victorian Local Government Area (LGA). Firstly, we categorize each LGA region into two broad classes as vegetation and non-vegetation; secondly, we further categorize the vegetation regions of each LGA into two sub-classes as shrub (including grassland) and trees; thirdly, for both shrub and trees classes, we further classify them as stressed and healthy. We not only map the urban vegetation in hierarchy but also develop Urban Green Space Index (UGSI) and Per Capita Green Space (PCGS) for the Victorian Local Government Areas (LGAs) to provide insights on the association of demography with urban green infrastructure using urban spatial analytics. To show the efficacy of the applied method, we evaluate our results using a Google Earth Engine (GEE) platform across different NDVI threshold ranges. The evaluation result shows that our method produces excellent performance metrics such as mean precision, recall, f-score and accuracy. In addition to this, we also prepare a recent Sentinel-2A dataset and derived products of urban green space coverage of the Victorian LGAs that are useful for multiple stakeholders ranging from bushfire modellers to biodiversity conservationists in contributing to sustainable and resilient urban lives.
Keywords	Internet ; biodiversity ; data collection ; demography ; grasslands ; green infrastructure ; land ; local government ; normalized difference vegetation index ; shrubs ; spatial data ; stakeholders
Language	English
Dates of publication	2022-0227
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2682955-1
ISSN	2073-445X
ISSN	2073-445X
DOI	10.3390/land11030351
Database	NAL-Catalogue (AGRICOLA)

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Article: A probability-based risk metric for operational wildfire risk management

KC, Ujjwal / Hilton, James / Garg, Saurabh / Aryal, Jagannath

Environmental modelling & software. 2022 Feb., v. 148

2022

Abstract: With the advancement in scientific understanding and computing technologies, fire practitioners have started relying on operational fire simulation tools to make better-informed decisions during wildfire emergencies. This increased use has created an ... ...

Abstract	With the advancement in scientific understanding and computing technologies, fire practitioners have started relying on operational fire simulation tools to make better-informed decisions during wildfire emergencies. This increased use has created an opportunity to employ an emerging data-driven approach for wildfire risk estimation as an alternative to running computationally expensive simulations. In an investigative attempt, we propose a probability-based risk metric that gives a series of probability values for fires starting at any possible start location under any given weather condition falling into different categories. We investigate the validity of the proposed approach by applying it to use cases in Tasmania, Australia. Results show that the proposed risk metric can be a convenient and accurate method of estimating imminent risk during operational wildfire management. Additionally, the knowledge base of our proposed risk metric based on a data-driven approach can be constantly updated to improve its accuracy.
Keywords	computer software ; risk ; risk estimate ; risk management ; weather ; wildfires ; wildland fire management ; Tasmania
Language	English
Dates of publication	2022-02
Publishing place	Elsevier Ltd
Document type	Article
ISSN	1364-8152
DOI	10.1016/j.envsoft.2021.105286
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: A Comparative Study of Knowledge Transfer Methods for Misaligned Urban Building Labels

Neupane, Bipul / Aryal, Jagannath / Rajabifard, Abbas

2023

Abstract: Misalignment in Earth observation (EO) images and building labels impact the training of accurate convolutional neural networks (CNNs) for semantic segmentation of building footprints. Recently, three Teacher-Student knowledge transfer methods have been ... ...

Abstract	Misalignment in Earth observation (EO) images and building labels impact the training of accurate convolutional neural networks (CNNs) for semantic segmentation of building footprints. Recently, three Teacher-Student knowledge transfer methods have been introduced to address this issue: supervised domain adaptation (SDA), knowledge distillation (KD), and deep mutual learning (DML). However, these methods are merely studied for different urban buildings (low-rise, mid-rise, high-rise, and skyscrapers), where misalignment increases with building height and spatial resolution. In this study, we present a workflow for the systematic comparative study of the three methods. The workflow first identifies the best (with the highest evaluation scores) hyperparameters, lightweight CNNs for the Student (among 43 CNNs from Computer Vision), and encoder-decoder networks (EDNs) for both Teachers and Students. Secondly, three building footprint datasets are developed to train and evaluate the identified Teachers and Students in the three transfer methods. The results show that U-Net with VGG19 (U-VGG19) is the best Teacher, and U-EfficientNetv2B3 and U-EfficientNet-lite0 are among the best Students. With these Teacher-Student pairs, SDA could yield upto 0.943, 0.868, 0.912, and 0.697 F1 scores in the low-rise, mid-rise, high-rise, and skyscrapers respectively. KD and DML provide model compression of upto 82%, despite marginal loss in performance. This new comparison concludes that SDA is the most effective method to address the misalignment problem, while KD and DML can efficiently compress network size without significant loss in performance. The 158 experiments and datasets developed in this study will be valuable to minimise the misaligned labels. Comment: This work has been submitted to Elsevier for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	720
Publishing date	2023-11-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Benchmarking Deep Learning Architectures for Urban Vegetation Points Segmentation

Aditya, Aditya / Lohani, Bharat / Aryal, Jagannath / Winter, Stephan

2023

Abstract: Vegetation is crucial for sustainable and resilient cities providing various ecosystem services and well-being of humans. However, vegetation is under critical stress with rapid urbanization and expanding infrastructure footprints. Consequently, mapping ... ...

Abstract	Vegetation is crucial for sustainable and resilient cities providing various ecosystem services and well-being of humans. However, vegetation is under critical stress with rapid urbanization and expanding infrastructure footprints. Consequently, mapping of this vegetation is essential in the urban environment. Recently, deep learning for point cloud semantic segmentation has shown significant progress. Advanced models attempt to obtain state-of-the-art performance on benchmark datasets, comprising multiple classes and representing real world scenarios. However, class specific segmentation with respect to vegetation points has not been explored. Therefore, selection of a deep learning model for vegetation points segmentation is ambiguous. To address this problem, we provide a comprehensive assessment of point-based deep learning models for semantic segmentation of vegetation class. We have selected four representative point-based models, namely PointCNN, KPConv (omni-supervised), RandLANet and SCFNet. These models are investigated on three different datasets, specifically Chandigarh, Toronto3D and Kerala, which are characterized by diverse nature of vegetation, varying scene complexity and changing per-point features. PointCNN achieves the highest mIoU on the Chandigarh (93.32%) and Kerala datasets (85.68%) while KPConv (omni-supervised) provides the highest mIoU on the Toronto3D dataset (91.26%). The paper develops a deeper insight, hitherto not reported, into the working of these models for vegetation segmentation and outlines the ingredients that should be included in a model specifically for vegetation segmentation. This paper is a step towards the development of a novel architecture for vegetation points segmentation. Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004 ; 006
Publishing date	2023-06-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Rethinking the U-Net, ResUnet, and U-Net3+ architectures with dual skip connections for building footprint extraction

Neupane, Bipul / Aryal, Jagannath / Rajabifard, Abbas

2023

Abstract: The importance of building footprints and their inventory has been recognised as foundational spatial information for multiple societal problems. Extracting complex urban buildings involves the segmentation of very high-resolution (VHR) earth observation ...

Abstract	The importance of building footprints and their inventory has been recognised as foundational spatial information for multiple societal problems. Extracting complex urban buildings involves the segmentation of very high-resolution (VHR) earth observation (EO) images. U-Net is a common deep learning network and foundation for its new incarnations like ResUnet, U-Net++ and U-Net3+ for such segmentation. The re-incarnations look for efficiency gain by re-designing the skip connection component and exploiting the multi-scale features in U-Net. However, skip connections do not always improve these networks and removing some of them provides efficiency gains and reduced network parameters. In this paper, we propose three dual skip connection mechanisms for U-Net, ResUnet, and U-Net3+. These mechanisms deepen the feature maps forwarded by the skip connections and allow us to study which skip connections need to be denser to yield the highest efficiency gain. The mechanisms are evaluated on feature maps of different scales in the three networks, producing nine new network configurations. The networks are evaluated against their original vanilla versions using four building footprint datasets (three existing and one new) of different spatial resolutions: VHR (0.3m), high-resolution (1m and 1.2m), and multi-resolution (0.3+0.6+1.2m). The proposed mechanisms report efficiency gain on four evaluation measures for U-Net and ResUnet, and up to 17.7% and 18.4% gain in F1 score and Intersection over Union (IoU) for U-Net3+. The codes will be available in a GitHub link after peer review. Comment: This work has been submitted to Wiley for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
Subject code	004
Publishing date	2023-03-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification

Sitaula, Chiranjibi / KC, Sumesh / Aryal, Jagannath

2023

Abstract: Very high-resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems. Recently, the existing deep learning (DL)-based methods have shown great promise in ... ...

Abstract	Very high-resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems. Recently, the existing deep learning (DL)-based methods have shown great promise in VHR RS scene classification. However, they still provide an unstable classification performance. To address such a problem, we, in this letter, propose a novel DL-based approach. For this, we devise an enhanced VHR attention module (EAM), followed by the atrous spatial pyramid pooling (ASPP) and global average pooling (GAP). This procedure imparts the enhanced features from the corresponding level. Then, the multi-level feature fusion is performed. Experimental results on two widely-used VHR RS datasets show that the proposed approach yields a competitive and stable/robust classification performance with the least standard deviation of 0.001. Further, the highest overall accuracies on the AID and the NWPU datasets are 95.39% and 93.04%, respectively. Comment: This paper is under consideration in the International Journal of Intelligent Systems (Wiley) journal. Based on the journal's policy and restrictions, this version may be updated or deleted
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-05-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

Neupane, Bipul / Horanont, Teerayut / Aryal, Jagannath

Remote Sensing. 2021 Feb. 23, v. 13, no. 4

2021

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 ... ...

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.
Keywords	image analysis ; meta-analysis ; satellites
Language	English
Dates of publication	2021-0223
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs13040808
Database	NAL-Catalogue (AGRICOLA)

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