Project

Nocturnal Scenario

Goal: Remote sensing images at night may miss important profile of population in administrative districts. Then, how about the combination of night-time images from remote sensing and UAV picture?

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Jingyi Cheng
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A study aiming to quantify the weather impact on cycling activities on both system-level and grid-level by multivariate regression.
 
Teng Fei
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In contrast to daytime remote sensing used for observing the Earth, night-time light remote sensing with satellites primarily assesses human activity using urban parameters such as building lights or lighted highways to help determine population density and other habitation characteristics. One limitation to conventional night-time remote sensing is that light emitted from high-rise buildings, for example, is not easily detected because of optical geometry as satellite sensors are generally pointed in only a downward direction. Furthermore, satellite sensors often receive weak optical signals because of streetlights reflected from the Earth’s surface. As a result, accurate information on night-time human activity cannot be gathered from existing satellite remote-sensing methods. To address this, a new method for night-time remote setting is presented. Specifically, an unmanned aerial vehicle (UAV) is used to capture panoramic images of night-time light and processed to reveal side-view light spot information from urban buildings. This dataset was used to predict population density alone, and with the Visible Infrared Imaging Radiometer Suite (VIIRS) data by simple multiple linear regression. The results confirm that nocturnal UAV side-view data or VIIRS data alone can be used to estimate population density, while the combination of the two significantly increases the accuracy of population density estimation compared against estimating population density using nocturnal UAV side-view data or VIIRS data alone. This outcome suggests that multi-angular night-time remote-sensing data sources increase the accuracy of urban population density estimation. One reason for this may be that the side-view night-time data and orthophoto data infer urban population density from different agent variables: building occupancy is a proxy of side-view night-time data, while density of illuminated road network is that of orthophoto data.
Wenyuan Kong
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Remote sensing images at night may miss important profile of population in administrative districts. Then, how about the combination of night-time images from remote sensing and UAV picture?