2020
DOI: 10.5194/isprs-archives-xlii-3-w10-431-2020
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Research on Semantic Map Generation and Location Intelligent Recognition Method for Scenic Spot Space Perception

Abstract: Abstract. In recent years, Tourism has become more and more Chinese leisure travel choice The research on the smart scenic spot is getting deeper and deeper, but the problem of accurate location l in the natural scenic spot still needs to be solved. Semantic maps contain a wealth of environmental information and can be more efficient for location-aware services, and are attracting more and more attention from researchers at home and abroad. In order to better ensure the travel experience of tourists, the range… Show more

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“…Because the images and geographic coordinates of tourist attraction are easy to obtain, some researches have tried to extract features from tourist attraction images, compute similarities between these images, and identify the tourist attraction locations, with the help of trained deep neural networks; but their algorithms often have a high overhead [19][20][21][22][23]. Doan et al [24] trained a deep neural network, using tourist attraction images with weak labels of global positioning system (GPS), and obtained the more accurate feature called NetVLAD (where VLAD stands for vector of locally aggregated descriptors); their approach cannot effectively recognize images taken at night, owing to its poor generalization ability and susceptibility to training samples.…”
Section: Introductionmentioning
confidence: 99%
“…Because the images and geographic coordinates of tourist attraction are easy to obtain, some researches have tried to extract features from tourist attraction images, compute similarities between these images, and identify the tourist attraction locations, with the help of trained deep neural networks; but their algorithms often have a high overhead [19][20][21][22][23]. Doan et al [24] trained a deep neural network, using tourist attraction images with weak labels of global positioning system (GPS), and obtained the more accurate feature called NetVLAD (where VLAD stands for vector of locally aggregated descriptors); their approach cannot effectively recognize images taken at night, owing to its poor generalization ability and susceptibility to training samples.…”
Section: Introductionmentioning
confidence: 99%