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Semantic‐based urban growth prediction
Author(s) -
Mc Cutchan Marvin,
ÖzdalOktay Simge,
Giannopoulos Ioannis
Publication year - 2020
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12655
Subject(s) - geospatial analysis , semantics (computer science) , computer science , process (computing) , set (abstract data type) , feature (linguistics) , scale (ratio) , urban planning , space (punctuation) , data science , artificial intelligence , data mining , geography , cartography , engineering , civil engineering , linguistics , philosophy , programming language , operating system
Urban growth is a spatial process which has a significant impact on the earth’s environment. Research on predicting this complex process makes it therefore especially fruitful for decision‐making on a global scale, as it enables the introduction of more sustainable urban development. This article presents a novel method of urban growth prediction. The method utilizes geospatial semantics in order to predict urban growth for a set of random areas in Europe. For this purpose, a feature space representing geospatial configurations was introduced which embeds semantic information. Data in this feature space was then used to perform deep learning, which ultimately enables the prediction of urban growth with high accuracy. The final results reveal that geospatial semantics hold great potential for spatial prediction tasks.