Space eye on flying aircraft: From Sentinel-2 MSI parallax to hybrid computing

Liu, Yongchao; Xu, Bihua; Zhi, Weifeng; Hu, Chuanmin; Dong, Yu; Jin, Song; Lu, Yingcheng; Chen, Tianxin; Xu, Wenxuan; Zhao, Biao; Lu, Wei

doi:10.1016/j.rse.2020.111867

Cited by 20 publications

(17 citation statements)

References 47 publications

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“…The fragmented pattern of land in some European countries, especially that in eastern and central Europe due to land reform in 1989 (Hartvigsen, 2013(Hartvigsen, , 2014, was clearly evident; Fig. 9f shows land in Estonia as an example (Jürgenson and Rasva, 2020;Looga et al, 2018). Although the algorithm was applied to different climates, terrains, and landscapes over a very large region, its classification accuracy across 33 countries was satisfactory.…”

Section: Additional Features Of Rapeseed Maps Derived Using Our New Methodsmentioning

confidence: 97%

The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

Han

Zhang

Luo

et al. 2021

Earth Syst. Sci. Data

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Abstract. Large-scale, high-resolution maps of rapeseed (Brassica napus L.), a major oilseed crop, are critical for predicting annual production and ensuring global energy security, but such maps are still not freely available for many areas. In this study, we developed a new pixel- and phenology-based algorithm and produced a new data product for rapeseed planting areas (2017–2019) in 33 countries at 10 m spatial resolution based on multiple data. Our product is strongly consistent at the national level with official statistics of the Food and Agricultural Organization of the United Nations. Our rapeseed maps achieved F1 spatial consistency scores of at least 0.81 when compared with the Cropland Data Layer in the United States, the Annual Crop Inventory in Canada, the Crop Map of England, and the Land Cover Map of France. Moreover, F1 scores based on independent validation samples ranged from 0.84 to 0.91, implying a good consistency with ground truth. In almost all countries covered in this study, the rapeseed crop rotation interval was at least 2 years. Our derived maps suggest, with reasonable accuracy, the robustness of the algorithm in identifying rapeseed over large regions with various climates and landscapes. Scientists and local growers can use the freely downloadable derived rapeseed planting areas to help predict rapeseed production and optimize planting structures. The product is publicly available at https://doi.org/10.17632/ydf3m7pd4j.3 (Han et al., 2021).

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Section: Additional Features Of Rapeseed Maps Derived Using Our New Methodsmentioning

confidence: 97%

The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

Han

Zhang

Luo

et al. 2021

Earth Syst. Sci. Data

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“…For instance, Paris' Charles de Gaule Airport and Rome's Leonardo da Vinci-Fiumicino Airport have, on average, approximately the same number of airplanes on the ground every day [27], even though the former has about 50% more flights in the same period [28]. Although flight traffic is available from sources other than satellite imagery, such as The OpenSky Network (OpenSky) [29] and Flightradar24 5 , Liu et al [30] point out that no individual technology can reach global coverage or support all aircraft types. For instance, OpenSky tracks flight data through Automatic Dependent Surveillance-Broadcast (ADS-B) communications.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, Landsat-8's repeat cycle of 16 days [32] hinders the ability to use temporal data to cope with these difficulties. Heiselberg [33] and Liu et al [30] utilized images captured by the Sentinel-2 satellites for the same job. These satellites' multispectral instrument design makes them observe the earth's surface at different times in each spectral band [34].…”

Section: Introductionmentioning

confidence: 99%

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Measuring Economic Activity From Space: A Case Study Using Flying Airplanes and COVID-19

Segundo

Pinto

Minetto

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This work introduces a novel solution to measure economic activity through remote sensing for a wide range of spatial areas. We hypothesize that disturbances in human behavior caused by major life-changing events leave signatures in satellite imagery that allows devising relevant image-based indicators to estimate their impacts and support decision-makers. We present a case study for the COVID-19 coronavirus outbreak, which imposed severe mobility restrictions and caused worldwide disruptions, using flying airplane detection around the 30 busiest airports in Europe to quantify and analyze the lockdown's effects and post-lockdown recovery. Our solution won the Rapid Action Coronavirus Earth observation (RACE) upscaling challenge, sponsored by the European Space Agency and the European Commission, and now is integrated into the RACE dashboard. This platform combines satellite data and artificial intelligence to promote a progressive and safe reopening of essential activities. Code, trained model, and data are available at https://github.com/maups/covid19-custom-script-contest.

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“…herein). Detection of flying aircrafts from satellites has only been studied for Landsat data in the 1.38 µm band [5] and for Sentinel-2 (S2) mostly cloud-free images [3,6,7].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Detection above Clouds by Sentinel-2 MSI Parallax

Heiselberg

2021

Remote Sensing

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Detection of aircrafts in satellite images is a challenging problem when the background is strongly reflective clouds with varying transparency. We develop a fast and effective detection algorithm that can find almost all aircrafts above and between clouds in Sentinel-2 multispectral images. It exploits the time delay of a few seconds between the recorded multispectral images such that a moving aircraft is observed at different positions due to parallax effects. The aircraft speed, heading and altitude are also calculated accurately. Analysing images over the English Channel during fall 2020, we obtain a detection accuracy of 80%, where the most of the remaining were covered by clouds. We also analyse images in the 1.38 μm water absorption band, where only 61% of the aircrafts are detected.

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Space eye on flying aircraft: From Sentinel-2 MSI parallax to hybrid computing

Cited by 20 publications

References 47 publications

The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

Measuring Economic Activity From Space: A Case Study Using Flying Airplanes and COVID-19

Aircraft Detection above Clouds by Sentinel-2 MSI Parallax

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