Data fusion in buildings: Synthesis of high-resolution IEQ and occupant tracking data

2021 ◽  
pp. 146047
Author(s):  
Brett Pollard ◽  
Fabian Held ◽  
Lina Engelen ◽  
Lauren Powell ◽  
Richard de Dear
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Tracking Data

scholarly journals High-resolution gravity and geoid models in Tahiti obtained from new airborne and land gravity observations: data fusion by spectral combination

2015 ◽  
Vol 67 (1) ◽  
Author(s):  
Hsuan-Chang Shih ◽  
Cheinway Hwang ◽  
Jean-Pierre Barriot ◽  
Maxime Mouyen ◽  
Pascal Corréia ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Fusion

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

scholarly journals DNA diet profiles with high‐resolution animal tracking data reveal levels of prey selection relative to habitat choice in a crepuscular insectivorous bird

2020 ◽  
Vol 10 (23) ◽  
pp. 13044-13056
Author(s):  
Ruben Evens ◽  
Greg Conway ◽  
Kirsty Franklin ◽  
Ian Henderson ◽  
Jennifer Stockdale ◽  
...  
Keyword(s):  
High Resolution ◽  
Prey Selection ◽  
Habitat Choice ◽  
Tracking Data ◽  
Animal Tracking ◽  
Insectivorous Bird

scholarly journals SteadEye-Head—Improving MARG-Sensor Based Head Orientation Measurements Through Eye Tracking Data

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2759 ◽  
Author(s):  
Lukas Wöhle ◽  
Marion Gebhard
Keyword(s):  
Data Fusion ◽  
Eye Tracking ◽  
Medical Diagnostics ◽  
Human Motion ◽  
Rehabilitation Robotics ◽  
Sensor Data ◽  
Head Orientation ◽  
Human Motion Analysis ◽  
Tracking Data ◽  
Sensor Data Fusion

This paper presents the use of eye tracking data in Magnetic AngularRate Gravity (MARG)-sensor based head orientation estimation. The approach presented here can be deployed in any motion measurement that includes MARG and eye tracking sensors (e.g., rehabilitation robotics or medical diagnostics). The challenge in these mostly indoor applications is the presence of magnetic field disturbances at the location of the MARG-sensor. In this work, eye tracking data (visual fixations) are used to enable zero orientation change updates in the MARG-sensor data fusion chain. The approach is based on a MARG-sensor data fusion filter, an online visual fixation detection algorithm as well as a dynamic angular rate threshold estimation for low latency and adaptive head motion noise parameterization. In this work we use an adaptation of Madgwicks gradient descent filter for MARG-sensor data fusion, but the approach could be used with any other data fusion process. The presented approach does not rely on additional stationary or local environmental references and is therefore self-contained. The proposed system is benchmarked against a Qualisys motion capture system, a gold standard in human motion analysis, showing improved heading accuracy for the MARG-sensor data fusion up to a factor of 0.5 while magnetic disturbance is present.

Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection

2020 ◽  
Vol 170 ◽  
pp. 174-191 ◽  
Author(s):  
Chandi Witharana ◽  
Md Abul Ehsan Bhuiyan ◽  
Anna K. Liljedahl ◽  
Mikhail Kanevskiy ◽  
Howard E. Epstein ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Data Fusion ◽  
Satellite Imagery ◽  
Ice Wedge
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