Some thoughts on deep learning enabling cartography

doi:10.11947/j.AGCS.2021.20210091

Abstract

Abstract: The cartography discipline includes issues of map making and map applications. Both tasks have deep associations with artificial intelligence. Among different intelligence representation methods, the symbolism intelligence approach used to apply with cartography generating mapping expert system technology, the activism intelligence applied with map analysis resulting in optimization decision technology. Nowadays the combination of cartography and connectionism intelligence deep learning faces challenging problems to improve the intelligence level. This study focuses on the issue “deep learning+cartography” discussing three questions. First from the perspective of the consistent ideas in deep learning and map space settlement argues the combination is possible, because both methods have the similar ideas of gradient descent, local spatial association, dimension reduction and non-linear processing. Secondly, by analyzing the mapping characteristics and technology contexts discusses the challenges from the combination, including the irregular data structure in map organization, sample establishment requiring geo-domain knowledge, the integration of geometric and geographic properties and the spatial scale issues in cartography. Thirdly, from the viewpoints of map making and map application respectively examines the practical methods to combine deep learning and cartography.

Key words: cartography, artificial intelligence, deep learning, graph convolution neural network

CLC Number:

P208

AI Tinghua. Some thoughts on deep learning enabling cartography[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9): 1170-1182.

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