Journal ArticleDOI
Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps
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TLDR
This study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation, and shows that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes.Abstract:
The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space a...read more
Citations
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Journal ArticleDOI
Mining public sentiments and perspectives from geotagged social media data for appraising the post-earthquake recovery of tourism destinations
TL;DR: The overall results of this study have proved that the adopted approach can effectively reveal the variations of people's sentiments and perspectives of general and specific issues regarding post-disaster tourism recovery over time.
Journal ArticleDOI
Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series
TL;DR: In this article , the authors proposed a novel unsupervised deep anomaly detection framework to detect anomalies in wind turbines based on SCADA data, which was validated on 12 failure events occurred during 20 months of operation of four wind turbines.
Journal ArticleDOI
A review of location encoding for GeoAI: methods and applications
TL;DR: Location Encoding: Location Encoding as discussed by the authors is the process to encode a single point location into an embedding space, such that this embedding is learning-friendly for downstream machine learning models.
Journal ArticleDOI
Estimating urban functional distributions with semantics preserved POI embedding
TL;DR: A novel approach for estimating the proportional distributions of function types in an urban area through learning semantics preserved embeddings of points-of-interest (POIs) and a manifold learning algorithm to capture categorical semantics is presented.
Journal ArticleDOI
AEGCN: An Autoencoder-Constrained Graph Convolutional Network
Mingyuan Ma,Sen Na,Hongyu Wang +2 more
TL;DR: In this paper, an autoencoder-constrained graph convolutional network is proposed to solve node classification task on graph domains, where the hidden layers are constrained by an auto-encoder.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.