Monitoring Meteorological Parameters with Crowdsourced Air Traffic Control Data

Trüb, Roman; Moser, Daniel; Schäfer, Matthias; Pinheiro, Rui; Lenders, Vincent

doi:10.1109/ipsn.2018.00010

Cited by 20 publications

(10 citation statements)

References 12 publications

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“…For instance, commercial aircraft can be equipped with Synthetic Aperture Radar (SAR) or with near-infrared (NIR) cameras that can (partly) penetrate clouds [31] and thus mitigate the cloud limitations. That said, going beyond remote sensing in the form of aerial imagery, usage of commercial flights could be extended to different types of sensing, as well, such as meteorological parameters, as proposed in [32]. The results obtained in this study could also be used to provide some insights into the the usability of commercial flights for those scenarios, as well.…”

Section: Discussionmentioning

confidence: 93%

Aerial Imagery Based on Commercial Flights as Remote Sensing Platform

Mastelić

Lörincz

Ivandić

et al. 2020

Sensors

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Remote sensing is commonly performed via airborne platforms such as satellites, specialized aircraft, and unmanned aerial systems (UASs), which perform airborne photography using mounted cameras. However, they are limited by their coverage (UASs), irregular flyover frequency (aircraft), and/or low spatial resolution (satellites) due to their high altitude. In this paper, we examine the utilization of commercial flights as an airborne platform for remote sensing. Namely, we simulate a situation where all aircraft on commercial flights are equipped with a mounted camera used for airborne photography. The simulation is used to estimate coverage, the temporal and spatial resolution of aerial imagery acquired this way, as well as the storage capacity required for storing all imagery data. The results show that Europe is 83.28 percent covered with an average of one aerial photography every half an hour and a ground sampling distance of 0.96 meters per pixel. Capturing such imagery results in 20 million images or four petabytes of image data per day. More detailed results are given in the paper for separate countries/territories in Europe, individual commercial airlines and alliances, as well as three different cameras.

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Section: Discussionmentioning

confidence: 93%

Aerial Imagery Based on Commercial Flights as Remote Sensing Platform

Mastelić

Lörincz

Ivandić

et al. 2020

Sensors

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“…To this end, I use data from the OpenSky network [11], a non-profit association that processes and archives ADS-B data from a global network of sensors. OpenSky data has previously been used by researchers for a diverse range of studies, including aviation security [12,13], retrieving meteorological data [14], inferring company information from business jet movements [15] and monitoring both aircraft and airspace performance [16,17].…”

Section: Opensky Aircraft Position Datamentioning

confidence: 99%

Go-Around Detection Using Crowd-Sourced ADS-B Position Data

Proud

2020

Aerospace

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The decision of a flight crew to undertake a go-around, aborting a landing attempt, is primarily to ensure the safe conduct of a flight. Although go-arounds are rare, they do cause air traffic disruption, especially in busy airspace, due to the need to accommodate an aircraft in an unusual position, and a go-around can also result in knock-on delays due to the time taken for the aircraft to re-position, fit into the landing sequence and execute a successful landing. Therefore, it is important to understand and alleviate the factors that can result in a go-around. In this paper, I present a new method for automatically detecting go-around events in aircraft position data, such as that sent via the ADS-B system, and apply the method to one year of approach data for Chhatrapati Shivaji Maharaj International Airport (VABB) in Mumbai, India. I show that the method is significantly more accurate than other methods, detecting go-arounds with very few false positives or negatives. Finally, I use the new method to reveal that while there is no one cause for go-arounds at this airport, the majority can be attributed to weather and/or an unstable approach. I also show that one runway (14/32) has a significantly higher proportion of go-arounds than the other (09/27).

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“…Benefiting from a global‐scale sensor network crowdsourcing 15+ billion transponder messages per day from aircraft, Trub et al . proposed a method to estimate meteorological parameters in upper air conditions with a relatively low error (Trub et al , ). Data subject to timing constraints tend to be more affected when the authoritative data are scarce, or unavailable for some periods.…”

Section: Review Of Previous Work and Analysismentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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Monitoring Meteorological Parameters with Crowdsourced Air Traffic Control Data

Cited by 20 publications

References 12 publications

Aerial Imagery Based on Commercial Flights as Remote Sensing Platform

Aerial Imagery Based on Commercial Flights as Remote Sensing Platform

Go-Around Detection Using Crowd-Sourced ADS-B Position Data

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

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