scholarly journals Spatio-Temporal Interpolation of UAV Sensor Data

GI_Forum ◽  
2017 ◽  
Vol 1 ◽  
pp. 141-156
Author(s):  
Dariia Strelnikova ◽  
Karl-Heinrich Anders
Keyword(s):  
Sensor Data ◽  
Temporal Interpolation ◽  
Spatio Temporal

scholarly journals EDISON: An Edge-Native Method and Architecture for Distributed Interpolation

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2279
Author(s):  
Lauri Lovén ◽  
Tero Lähderanta ◽  
Leena Ruha ◽  
Ella Peltonen ◽  
Ilkka Launonen ◽  
...  
Keyword(s):  
Smart Cities ◽  
Sensor Data ◽  
Fine Grained ◽  
Distributed Approach ◽  
Computing Paradigm ◽  
Temporal Interpolation ◽  
Data Points ◽  
Alternative Approaches ◽  
Spatio Temporal

Spatio-temporal interpolation provides estimates of observations in unobserved locations and time slots. In smart cities, interpolation helps to provide a fine-grained contextual and situational understanding of the urban environment, in terms of both short-term (e.g., weather, air quality, traffic) or long term (e.g., crime, demographics) spatio-temporal phenomena. Various initiatives improve spatio-temporal interpolation results by including additional data sources such as vehicle-fitted sensors, mobile phones, or micro weather stations of, for example, smart homes. However, the underlying computing paradigm in such initiatives is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable, as when the spatial and temporal density of sensor data grows, the required transmission bandwidth and computational capacity become unfeasible. To address the scaling problem, we propose EDISON: algorithms for distributed learning and inference, and an edge-native architecture for distributing spatio-temporal interpolation models, their computations, and the observed data vertically and horizontally between device, edge and cloud layers. We demonstrate EDISON functionality in a controlled, simulated spatio-temporal setup with 1 M artificial data points. While the main motivation of EDISON is the distribution of the heavy computations, the results show that EDISON also provides an improvement over alternative approaches, reaching at best a 10% smaller RMSE than a global interpolation and 6% smaller RMSE than a baseline distributed approach.

Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set

2015 ◽  
Vol 14 ◽  
pp. 70-90 ◽  
Author(s):  
Caley K. Gasch ◽  
Tomislav Hengl ◽  
Benedikt Gräler ◽  
Hanna Meyer ◽  
Troy S. Magney ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Water Temperature ◽  
Soil Water ◽  
Data Set ◽  
Temporal Interpolation ◽  
Spatio Temporal

Spatio-temporal interpolation of rainfall data in western Mexico

2021 ◽  
Author(s):  
Zaira Carolina Martinez Vargas ◽  
S. Ivvan Valdez ◽  
Jorge Paredes-Tavares
Keyword(s):  
Rainfall Data ◽  
Western Mexico ◽  
Temporal Interpolation ◽  
Spatio Temporal

scholarly journals Sensing as a Service for the Internet of Things

2016 ◽  
Vol 4 (3) ◽  
pp. 28
Author(s):  
Omar Subhi Aldabbas
Keyword(s):  
Internet Of Things ◽  
Cost Effective ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Heterogeneous Services ◽  
Coordination Languages ◽  
Spatio Temporal ◽  
Temporal Events ◽  
New Applications ◽  
Common Application

Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources, which are distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and usage of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval. This approach considers objects as highly decentralized, composite and cost effective services. Such services can be constructed from objects located within close geographical proximity to retrieve spatio-temporal events from the gathered sensor data. To achieve this, we advocate Coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to achieve the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70%, while maintaining energy consumption 20% lower than its best rivals in the literature.

scholarly journals Comparison of Person Trip Survey data in 5 major Metropolitan Areas and Implementation of Spatio-temporal Interpolation

2010 ◽  
Vol 27 (0) ◽  
pp. 569-577 ◽  
Author(s):  
Tomotaka USUI ◽  
Yoshihide SEKIMOTO ◽  
Hiroshi KANASUGI ◽  
Yoshitaka MINAMI ◽  
Ryosuke SHIBASAKI
Keyword(s):  
Survey Data ◽  
Metropolitan Areas ◽  
Temporal Interpolation ◽  
Spatio Temporal

scholarly journals Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

2019 ◽  
Vol 11 (9) ◽  
pp. 1052 ◽  
Author(s):  
Reto Stöckli ◽  
Jędrzej S. Bojanowski ◽  
Viju O. John ◽  
Anke Duguay-Tetzlaff ◽  
Quentin Bourgeois ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Sensor Data ◽  
Mean Bias Error ◽  
Retrieval Algorithm ◽  
Bias Error ◽  
Cloud Detection ◽  
Climate Data ◽  
Detection Algorithms ◽  
Fractional Cover ◽  
Spatio Temporal

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

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