Truck traffic monitoring with satellite images

Kaack, Lynn H.; Chen, George H.; Morgan, M. Granger

doi:10.1145/3314344.3332480

Cited by 21 publications

(14 citation statements)

References 19 publications

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“…The results from our previous Viewpoint were widely covered − and led to speculations regarding other system-level approaches to decrease the required energy, , thereby lowering the battery weight and improving the ability to carry cargo. One of the avenues proposed to address the uphill challenge is to exploit the platooning of multiple trucks .…”

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confidence: 99%

Evaluating the Potential of Platooning in Lowering the Required Performance Metrics of Li-Ion Batteries to Enable Practical Electric Semi-Trucks

2017

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confidence: 99%

Evaluating the Potential of Platooning in Lowering the Required Performance Metrics of Li-Ion Batteries to Enable Practical Electric Semi-Trucks

2017

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“…the volume of a building or the distance travelled by a vehiclecan be predictive of energy use and GHG emissions (Liu, Li, Wu, & Li, 2018;Robinson et al, 2017). This route can utilize several ML methods sequentially for different tasks (Kaack, Chen, & Morgan, 2019;Liu et al, 2018). ML and other modelling can also be combined (Abdulkareem, 2019).…”

Section: Generating Climate Semanticsmentioning

confidence: 99%

Machine learning for geographically differentiated climate change mitigation in urban areas

Milojevic-Dupont

Creutzig

2021

Sustainable Cities and Society

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Artificial intelligence and machine learning are transforming scientific disciplines, but their full potential for climate change mitigation remains elusive. Here, we conduct a systematic review of applied machine learning studies that are of relevance for climate change mitigation, focusing specifically on the fields of remote sensing, urban transportation, and buildings. The relevant body of literature spans twenty years and is growing exponentially. We show that the emergence of big data and machine learning methods enables climate solution research to overcome generic recommendations and provide policy solutions at urban, street, building and household scale, adapted to specific contexts, but scalable to global mitigation potentials. We suggest a meta-algorithmic architecture and framework for using machine learning to optimize urban planning for accelerating, improving and transforming urban infrastructure provision. climate change mitigation a distant goal, but also making comparisons between local-scale studies difficult (Lamb, Callaghan, Creutzig, Khosla, & Minx, 2018). The IPCC's AR5 reports knowledge gaps on urban climate action (IPCC, 2014): there is too little understanding of the magnitude of the emissions reductions from altering urban form, and emissions savings from integrated infrastructure and land use planning. New analyses are required both to understand relationships (

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“…The generated images contain essential information for civilian and military domains when ground sensors are not locally available. Sample civilian applications include urban planning (Wijnands et al, 2021), automatic traffic monitoring (Kaack et al, 2019), driving behavioral research (Chen et al, 2021), or commerce management with ship monitoring (Cao et al, 2019). Similarly, object detection and tracking contribute to military applications such as border protection or abnormal activity monitoring.…”

Section: Introductionmentioning

confidence: 99%

Small Object Detection and Tracking in Satellite Videos With Motion Informed-CNN and GM-PHD Filter

Aguilar

Ortner

Zerubia

2022

Front. Signal Process.

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Small object tracking in low-resolution remote sensing images presents numerous challenges. Targets are relatively small compared to the field of view, do not present distinct features, and are often lost in cluttered environments. In this paper, we propose a track-by-detection approach to detect and track small moving targets by using a convolutional neural network and a Bayesian tracker. Our object detection consists of a two-step process based on motion and a patch-based convolutional neural network (CNN). The first stage performs a lightweight motion detection operator to obtain rough target locations. The second stage uses this information combined with a CNN to refine the detection results. In addition, we adopt an online track-by-detection approach by using the Probability Hypothesis Density (PHD) filter to convert detections into tracks. The PHD filter offers a robust multi-object Bayesian data-association framework that performs well in cluttered environments, keeps track of missed detections, and presents remarkable computational advantages over different Bayesian filters. We test our method across various cases of a challenging dataset: a low-resolution satellite video comprising numerous small moving objects. We demonstrate the proposed method outperforms competing approaches across different scenarios with both object detection and object tracking metrics.

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Truck traffic monitoring with satellite images

Cited by 21 publications

References 19 publications

Evaluating the Potential of Platooning in Lowering the Required Performance Metrics of Li-Ion Batteries to Enable Practical Electric Semi-Trucks

Evaluating the Potential of Platooning in Lowering the Required Performance Metrics of Li-Ion Batteries to Enable Practical Electric Semi-Trucks

Machine learning for geographically differentiated climate change mitigation in urban areas

Small Object Detection and Tracking in Satellite Videos With Motion Informed-CNN and GM-PHD Filter

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