AIMC Topic: Parks, Recreational

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Assessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery.

PloS one May 7, 2020
To analyze types and patterns of greening trends across a city, this study seeks to identify a method of creating very high-resolution urban vegetation maps that scales over space and time. Vegetation poses unique challenges for image segmentation be...
Humans Neural Networks, Computer Soil Cities Cluster Analysis Models, Statistical Parks, Recreational Poaceae Trees Data Accuracy Philadelphia Normal Distribution City Planning Water Resources
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Heavy metals prediction system in groundwater using online sensor and machine learning for water management: the case of typical industrial park.

Environmental pollution (Barking, Essex : 1987) Jun 1, 2025
With the expansion of human industrial activities, heavy metal contamination in groundwater environments has become increasingly severe. Environmental management agencies invest significant financial resources into groundwater monitoring, primarily d...
Machine Learning Groundwater Environmental Monitoring Water Pollutants, Chemical Parks, Recreational Metals, Heavy
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Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils.

Scientific reports May 20, 2025
The accumulation of heavy metal(loid)s (HMs) in the soils of urban parks in industrial cities has raised global concerns because of their environmental and health impacts. However, traditional deterministic assessments commonly overlook uncertainties...
Humans Machine Learning Soil Risk Assessment China Cities Environmental Monitoring Soil Pollutants Parks, Recreational Metals, Heavy Mining
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