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Placer mining is a mineral extraction method in floodplains that involves the removal of earth material to access mineral-laden sediments, a process that can have significant and long-term impacts on aquatic ecosystems. Given the widespread nature of mining, new tools are required to monitor the potential watershed-scale ecological impacts of placer mining. This study adapted and evaluated a deep learning model – a U-Net convolution neural network, and compared it to a traditional image classification method – random forests (RF) – to detect and quantify the area of post-placer mining disturbance at the watershed scale. Overall, both random forest and U-Net models performed well at classifying digitized image samples where placer disturbances were mapped. Sensitivity in placer classification was high, with both modelling frameworks achieving at least 75% accuracy in the classification of digitized placer samples in 7 out of 12 modelling scenarios. Misclassification of non-placer pixels as placer was highly variable among different models, data configurations, study sites, and time periods. Commission errors (i.e., incorrectly classifying a non-placer pixel as placer) were typically the result of models labelling water areas or forest areas as placer – errors which may have only marginal practical significance. In general, U-Net models performed better in terms of minimizing misclassification errors, whereas RF models performed slightly better in classifying known placer pixels. We conclude with discussions on the advantages of deploying U-Net and RF models for placer detection, challenges that may be encountered in operational systems that employ the models, and identifying outstanding issues which need to be addressed in future placer modelling studies.
Karim Malik; Colin Robertson; Douglas Braun; Clara Greig. U-Net convolutional neural network models for detecting and quantifying placer mining disturbances at watershed scales. International Journal of Applied Earth Observation and Geoinformation 2021, 104, 102510 .
AMA StyleKarim Malik, Colin Robertson, Douglas Braun, Clara Greig. U-Net convolutional neural network models for detecting and quantifying placer mining disturbances at watershed scales. International Journal of Applied Earth Observation and Geoinformation. 2021; 104 ():102510.
Chicago/Turabian StyleKarim Malik; Colin Robertson; Douglas Braun; Clara Greig. 2021. "U-Net convolutional neural network models for detecting and quantifying placer mining disturbances at watershed scales." International Journal of Applied Earth Observation and Geoinformation 104, no. : 102510.
Even though the contribution of the aviation sector to the global economy is very notable, it also has an adverse impact on climate change. Improvements have been made in different areas (i.e., technology, sustainable aviation fuel, and design) to mitigate these adverse effects. However, the rate of improvement is small compared to the increase in the demand for air transportation. Hence, greenhouse gas emissions in the aviation sector are steadily increasing and this trend is expected to continue unless adequately addressed. In this context, this study examined the following: (i) the factors that affect the growth of aviation, (ii) trends in greenhouse gas emissions in the sector, (iii) trends in energy demand, (iv) mitigation pathways of emissions, (v) mitigation challenges for the International Civil Aviation Organization, (vi) achievements in mitigating emissions, (vii) barriers against mitigating emissions, and (viii) approaches of overcoming barriers against emissions mitigation. This study finds that continued research and development efforts targeting aircraft fuel burn efficiency are crucial in reducing greenhouse gas emissions. Although biofuels are promising for the reduction of aviation emissions, techniques to reduce NOx emissions could enhance large-scale deployment. Pragmatic market-based mechanisms, such as the Emissions Trading Scheme (ETS) and/or carbon tax must be enforced on a global scale to capitalize on a collective stakeholder effort to curb CO2 emissions. The findings of this study will help in understanding the emissions and energy consumption scenarios, which will provide a comprehensive package of mitigation pathways to overcome future emissions reduction challenges in the aviation sector.
Arif Hasan; Abdullah Mamun; Syed Rahman; Karim Malik; Al Amran; Abu Khondaker; Omer Reshi; Surya Tiwari; Fahad Alismail. Climate Change Mitigation Pathways for the Aviation Sector. Sustainability 2021, 13, 3656 .
AMA StyleArif Hasan, Abdullah Mamun, Syed Rahman, Karim Malik, Al Amran, Abu Khondaker, Omer Reshi, Surya Tiwari, Fahad Alismail. Climate Change Mitigation Pathways for the Aviation Sector. Sustainability. 2021; 13 (7):3656.
Chicago/Turabian StyleArif Hasan; Abdullah Mamun; Syed Rahman; Karim Malik; Al Amran; Abu Khondaker; Omer Reshi; Surya Tiwari; Fahad Alismail. 2021. "Climate Change Mitigation Pathways for the Aviation Sector." Sustainability 13, no. 7: 3656.
Convolutional neural networks (CNNs) are known for their ability to learn shape and texture descriptors useful for object detection, pattern recognition, and classification problems. Deeper layer filters of CNN generally learn global image information vital for whole-scene or object discrimination. In landscape pattern comparison, however, dense localized information encoded in shallow layers can contain discriminative information for characterizing changes across image local regions but are often lost in the deeper and non-spatial fully connected layers. Such localized features hold potential for identifying, as well as characterizing, process–pattern change across space and time. In this paper, we propose a simple yet effective texture-based CNN (Tex-CNN) via a feature concatenation framework which results in capturing and learning texture descriptors. The traditional CNN architecture was adopted as a baseline for assessing the performance of Tex-CNN. We utilized 75% and 25% of the image data for model training and validation, respectively. To test the models’ generalization, we used a separate set of imagery from the Aerial Imagery Dataset (AID) and Sentinel-2 for model development and independent validation. The classical CNN and the Tex-CNN classification accuracies in the AID were 91.67% and 96.33%, respectively. Tex-CNN accuracy was either on par with or outcompeted state-of-the-art methods. Independent validation on Sentinel-2 data had good performance for most scene types but had difficulty discriminating farm scenes, likely due to geometric generalization of discriminative features at the coarser scale. In both datasets, the Tex-CNN outperformed the classical CNN architecture. Using the Tex-CNN, gradient-based spatial attention maps (feature maps) which contain discriminative pattern information are extracted and subsequently employed for mapping landscape similarity. To enhance the discriminative capacity of the feature maps, we further perform spatial filtering, using PCA and select eigen maps with the top eigen values. We show that CNN feature maps provide descriptors capable of characterizing and quantifying landscape (dis)similarity. Using the feature maps histogram of oriented gradient vectors and computing their Earth Movers Distances, our method effectively identified similar landscape types with over 60% of target-reference scene comparisons showing smaller Earth Movers Distance (EMD) (e.g., 0.01), while different landscape types tended to show large EMD (e.g., 0.05) in the benchmark AID. We hope this proposal will inspire further research into the use of CNN layer feature maps in landscape similarity assessment, as well as in change detection.
Karim Malik; Colin Robertson. Landscape Similarity Analysis Using Texture Encoded Deep-Learning Features on Unclassified Remote Sensing Imagery. Remote Sensing 2021, 13, 492 .
AMA StyleKarim Malik, Colin Robertson. Landscape Similarity Analysis Using Texture Encoded Deep-Learning Features on Unclassified Remote Sensing Imagery. Remote Sensing. 2021; 13 (3):492.
Chicago/Turabian StyleKarim Malik; Colin Robertson. 2021. "Landscape Similarity Analysis Using Texture Encoded Deep-Learning Features on Unclassified Remote Sensing Imagery." Remote Sensing 13, no. 3: 492.
This study conducted linear and change‐point analyses of historical trends since 1942 in the length and number of days suitable for skating on backyard rinks in the “Original Six” National Hockey League cities of Boston, Chicago, Detroit, Montreal, New York, and Toronto. Analysis is based on the relationship between ambient air temperatures and the probability of skating, using thresholds identified through the RinkWatch citizen science project. In all cities, coefficient estimates suggest the number of high‐probability skating days per winter is declining, with easternmost cities displaying notable declines and growing inter‐annual variability in skating days in recent decades. Linear analysis shows a statistically significant decline in Toronto, with a step‐change emerging in 1980, after which there is on average one‐third fewer skating days compared with preceding decades. The outdoor skating season trends towards later start dates in Boston, Montreal, New York, and Toronto. Future monitoring of outdoor rinks provides an opportunity for engaging the public in identification of winter warming trends that might otherwise be imperceptible, and for raising awareness of the impacts of climate change.
Karim Malik; Robert McLeman; Colin Robertson; Haydn Lawrence. Reconstruction of past backyard skating seasons in the Original Six NHL cities from citizen science data. The Canadian Geographer/Le Géographe canadien 2020, 64, 564 -575.
AMA StyleKarim Malik, Robert McLeman, Colin Robertson, Haydn Lawrence. Reconstruction of past backyard skating seasons in the Original Six NHL cities from citizen science data. The Canadian Geographer/Le Géographe canadien. 2020; 64 (4):564-575.
Chicago/Turabian StyleKarim Malik; Robert McLeman; Colin Robertson; Haydn Lawrence. 2020. "Reconstruction of past backyard skating seasons in the Original Six NHL cities from citizen science data." The Canadian Geographer/Le Géographe canadien 64, no. 4: 564-575.
Detection of changes in spatial processes has long been of interest to quantitative geographers seeking to test models, validate theories, and anticipate change. Given the current “data‐rich” environment of today, it may be time to reconsider the methodological approaches used for quantifying change in spatial processes. New tools emerging from computer vision research may hold particular potential to make significant advances in quantifying changes in spatial processes. In this article, two comparative indices from computer vision, the structural similarity (SSIM) index, and the complex wavelet structural similarity (CWSSIM) index were examined for their utility in the comparison of real and simulated spatial data sets. Gaussian Markov random fields were simulated and compared with both metrics. A case study into comparison of snow water equivalent spatial patterns over northern Canada was used to explore the properties of these indices on real‐world data. CWSSIM was found to be less sensitive than SSIM to changing window dimension. The CWSSIM appears to have significant potential in characterizing change and/or similarity; distinguishing between map pairs that possess subtle structural differences. Further research is required to explore the utility of these approaches for empirical comparison cases of different forms of landscape change and in comparison to human judgments of spatial pattern differences.
Karim Malik; Colin Robertson. Exploring the Use of Computer Vision Metrics for Spatial Pattern Comparison. Geographical Analysis 2019, 52, 617 -641.
AMA StyleKarim Malik, Colin Robertson. Exploring the Use of Computer Vision Metrics for Spatial Pattern Comparison. Geographical Analysis. 2019; 52 (4):617-641.
Chicago/Turabian StyleKarim Malik; Colin Robertson. 2019. "Exploring the Use of Computer Vision Metrics for Spatial Pattern Comparison." Geographical Analysis 52, no. 4: 617-641.
In the abstract, the statement "The GHG emissions avoidance expected to be achieved by the GCC countries will vary between 5 and 247 million tons of CO equivalent by 2030."
Karim Malik; Syed Masiur Rahman; Abu Nasser Khondaker; Ismaila Rimi Abubakar; Yusuf Adedoyin Aina; Arif Hasan. Correction to: Renewable energy utilization to promote sustainability in GCC countries: policies, drivers, and barriers. Environmental Science and Pollution Research 2019, 26, 31550 -31551.
AMA StyleKarim Malik, Syed Masiur Rahman, Abu Nasser Khondaker, Ismaila Rimi Abubakar, Yusuf Adedoyin Aina, Arif Hasan. Correction to: Renewable energy utilization to promote sustainability in GCC countries: policies, drivers, and barriers. Environmental Science and Pollution Research. 2019; 26 (30):31550-31551.
Chicago/Turabian StyleKarim Malik; Syed Masiur Rahman; Abu Nasser Khondaker; Ismaila Rimi Abubakar; Yusuf Adedoyin Aina; Arif Hasan. 2019. "Correction to: Renewable energy utilization to promote sustainability in GCC countries: policies, drivers, and barriers." Environmental Science and Pollution Research 26, no. 30: 31550-31551.