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Remote Sensing Technology

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Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China.

Journal of environmental sciences (China)
Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on s...

Artificial Neural Network and Remote Sensing combined to predict the Aboveground Biomass in the Cerrado biome.

Anais da Academia Brasileira de Ciencias
Cerrado is the second largest biome in Brazil, and it is responsible for providing us several ecosystem services, including the functions of storing Carbon and biodiversity conservation. In this study, we developed a modeling approach to predict the ...

Rapid estimation of soil water content based on hyperspectral reflectance combined with continuous wavelet transform, feature extraction, and extreme learning machine.

PeerJ
BACKGROUND: Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and e...

Soil organic carbon estimation using remote sensing data-driven machine learning.

PeerJ
Soil organic carbon (SOC) is a crucial component of the global carbon cycle, playing a significant role in ecosystem health and carbon balance. In this study, we focused on assessing the surface SOC content in Shandong Province based on land use type...

Segmentation of LiDAR point cloud data in urban areas using adaptive neighborhood selection technique.

PloS one
Semantic segmentation of urban areas using Light Detection and Ranging (LiDAR) point cloud data is challenging due to the complexity, outliers, and heterogeneous nature of the input point cloud data. The machine learning-based methods for segmenting ...

Utilizing deep learning algorithms for automated oil spill detection in medium resolution optical imagery.

Marine pollution bulletin
This study evaluates the performance of three typical convolutional neural network based deep learning algorithms for oil spill detection using medium-resolution optical satellite imagery from Sentinel-2 MSI, Landsat-8 OLI, and Landsat-9 OLI2. Oil sl...

Accounting for minimum data required to train a machine learning model to accurately monitor Australian dairy pastures using remote sensing.

Scientific reports
Precision in grazing management is highly dependent on accurate pasture monitoring. Typically, this is often overlooked because existing approaches are labour-intensive, need calibration, and are commonly perceived as inaccurate. Machine-learning pro...

Mapping of temperate upland habitats using high-resolution satellite imagery and machine learning.

Environmental monitoring and assessment
Upland habitats provide vital ecological services, yet they are highly threatened by natural and anthropogenic stressors. Monitoring these vulnerable habitats is fundamental for conservation and involves determining information about their spatial lo...

Digital innovations for monitoring sustainability in food systems.

Nature food
Monitoring systems that incentivize, track and verify compliance with social and environmental standards are widespread in food systems. In particular, digital monitoring approaches using remote sensing, machine learning, big data, smartphones, platf...

Remote Tongue-Based Control of a Wheelchair Mounted Assistive Robotic Manipulator and the Effect of Adaptive Semi-Automation.

IEEE transactions on bio-medical engineering
Increasing the independence of individuals with tetraplegia is a challenging task. One potential solution is to allow for use of an assistive robotic manipulator (ARM), when solving tasks in personal and remote space. There is a lack in available con...