AIMC Topic: Remote Sensing Technology

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Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.

GigaScience
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent a...

Comparison of mixing layer height inversion algorithms using lidar and a pollution case study in Baoding, China.

Journal of environmental sciences (China)
Beijing-Tianjin-Hebei area is suffering from atmospheric pollution from a long time. The understanding of the air pollution mechanism is of great importance for officials to design strategies for the environmental governance. Mixing layer height (MLH...

Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network.

Sensors (Basel, Switzerland)
Unmanned aerial vehicles (UAVs) require data-link system to link ground data terminals to the real-time controls of each UAV. Consequently, the ability to predict the health status of a UAV data-link system is vital for safe and efficient operations....

Continuous patrolling in uncertain environment with the UAV swarm.

PloS one
The research about unmanned aerial vehicle (UAV) swarm has developed rapidly in recent years, especially the UAV swarm with sensors which is becoming common means of achieving situational awareness. Due to inadequate researches of the UAV swarm with ...

Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM.

Environmental pollution (Barking, Essex : 1987)
Fine particulate matter (PM) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to m...

Early anomaly detection in smart home: A causal association rule-based approach.

Artificial intelligence in medicine
As the world's population grows older, an increasing number of people are facing health issues. For the elderly, living alone can be difficult and dangerous. Consequently, smart homes are becoming increasingly popular. A sensor-rich environment can b...

UAVs, Hyperspectral Remote Sensing, and Machine Learning Revolutionizing Reef Monitoring.

Sensors (Basel, Switzerland)
Recent advances in unmanned aerial system (UAS) sensed imagery, sensor quality/size, and geospatial image processing can enable UASs to rapidly and continually monitor coral reefs, to determine the type of coral and signs of coral bleaching. This pap...

Modeling Dengue vector population using remotely sensed data and machine learning.

Acta tropica
Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this v...

A machine learning method to estimate PM concentrations across China with remote sensing, meteorological and land use information.

The Science of the total environment
BACKGROUND: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at...

Human Activity Recognition from Body Sensor Data using Deep Learning.

Journal of medical systems
In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring...