AIMC Topic: Remote Sensing Technology

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Autonomous unobtrusive detection of mild cognitive impairment in older adults.

IEEE transactions on bio-medical engineering
The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adu...

Detection of pine wood nematode infections in Chinese pine (Pinus tabuliformis) using hyperspectral drone images.

Pest management science
BACKGROUND: The pine wood nematode (PWN) has caused tremendous damage to pine forests in China. Accurately predicting the infestation stage of PWN is crucial for implementing appropriate management, such as chemically controlling early-infested trees...

Enhancing realism in LiDAR scene generation with CSPA-DFN and linear cross-attention via Diffusion Transformer model.

Neural networks : the official journal of the International Neural Network Society
Point cloud diffusion models have found extensive applications in autonomous driving and robotics. However, there is still a big gap between their generated LiDAR scene samples and real-world data in terms of visual quality. This discrepancy primaril...

Review on sea water quality (SWQ) monitoring using satellite remote sensing techniques (SRST).

Marine pollution bulletin
Due to extensive anthropogenic activities in coastal areas and rivers connected to the seas, effective and timely monitoring the sea water quality (SWQ) is crucial for maintaining ecosystem health. SWQ monitoring involves examining the chemical, phys...

Plastics detection and sorting using hyperspectral sensing and machine learning algorithms.

Waste management (New York, N.Y.)
Plastic waste second life management requires effective detection (and sorting if necessary) techniques to tackle the environmental challenge it poses. This research explores the application of hyperspectral imaging in the spectral range 900-1700 nm ...

Attribute-guided feature fusion network with knowledge-inspired attention mechanism for multi-source remote sensing classification.

Neural networks : the official journal of the International Neural Network Society
Land use and land cover (LULC) classification is a popular research area in remote sensing. The information of single-modal data is insufficient for accurate classification, especially in complex scenes, while the complementarity of multi-modal data ...

Advancing Remote Monitoring for Patients With Alzheimer Disease and Related Dementias: Systematic Review.

JMIR aging
BACKGROUND: Using remote monitoring technology in the context of Alzheimer disease (AD) care presents exciting new opportunities to lessen caregiver stress and improve patient care quality. The application of wearables, environmental sensors, and sma...

UAV-based water pollutants detection and classification framework using multi-modal and multi-sensor ensemble learning.

Environmental monitoring and assessment
The massive increment in water pollutants due to the release of plastic, industrial, and household waste has threatened the delicate balance of ecosystems and the well-being of human life. Therefore, detection and monitoring of such water pollutants ...

Satellite Remote Sensing-Implemented Nontargeted Screening of Emerging Contaminant Fingerprints in a River-to-Ocean Continuum through Interpretable Machine Learning: The Pivotal Intermediary Role of Dissolved Organic Matter.

Environmental science & technology
Emerging contaminants (ECs) can exert irreversible health impacts on humans, even at trace concentrations. Currently, nontargeted screening of ECs has been developed for their assessment, which requires sophisticated instrumentation. Although satelli...

Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.

The plant genome
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide pol...