What question did this study set out to answer?

The aim is to develop an optimisation method for rural tourism paths that addresses homogenisation and resource inefficiency.

Integrating deep learning and GIS technology for optimising rural tourism development paths

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Randomized trial demonstrates improved rural tourism path optimisation using GIS and deep learning, indicating effective resource allocation.

The aim is to develop an optimisation method for rural tourism paths that addresses homogenisation and resource inefficiency.
Integrated multiple sources of spatial data and used convolutional neural networks to predict rural tourism development potential.
Constructed a mathematical model for path optimisation based on spatial distribution input.
Employed an improved deep reinforcement learning method with local operators for neighbourhood search.
Achieved a net benefit value of 44.05 on the standard test set.
Reduction in solution time to 3.14 seconds, surpassing comparison algorithms.

Yahui Sun (2026) studied this question.

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Randomized trial demonstrates improved rural tourism path optimisation using GIS and deep learning, indicating effective resource allocation.