AIMC Topic: Parks, Recreational

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Artificial Intelligence and Public Health: An Exploratory Study.

International journal of environmental research and public health
Artificial intelligence (AI) has the potential to revolutionize research by automating data analysis, generating new insights, and supporting the discovery of new knowledge. The top 10 contribution areas of AI towards public health were gathered in t...

Assessing Inequity in Green Space Exposure toward a "15-Minute City" in Zhengzhou, China: Using Deep Learning and Urban Big Data.

International journal of environmental research and public health
Green space exposure is considered an important aspect of a livable environment and human well-being. It is often regarded as an indicator of social justice. However, due to the difficulties in obtaining green space exposure data from a ground-based ...

A comparative study on image-based snake identification using machine learning.

Scientific reports
Automated snake image identification is important from different points of view, most importantly, snake bite management. Auto-identification of snake images might help the avoidance of venomous snakes and also providing better treatment for patients...

The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning.

International journal of environmental research and public health
Population agglomeration and real estate development encroach on public green spaces, threatening human settlement equity and perceptual experience. Perceived greenery is a vital interface for residents to interact with the urban eco-environment. Nev...

Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure.

The Science of the total environment
BACKGROUND: Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on th...

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

PloS one
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...

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)
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-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils.

Scientific reports
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...