  Considering the success of generative adversarial networks (GANs) for
image-to-image translation, researchers have attempted to translate remote
sensing images (RSIs) to maps (rs2map) through GAN for cartography. However,
these studies involved limited scales, which hinders multi-scale map creation.
By extending their method, multi-scale RSIs can be trivially translated to
multi-scale maps (multi-scale rs2map translation) through scale-wise rs2map
models trained for certain scales (parallel strategy). However, this strategy
has two theoretical limitations. First, inconsistency between various spatial
resolutions of multi-scale RSIs and object generalization on multi-scale maps
(RS-m inconsistency) increasingly complicate the extraction of geographical
information from RSIs for rs2map models with decreasing scale. Second, as
rs2map translation is cross-domain, generators incur high computation costs to
transform the RSI pixel distribution to that on maps. Thus, we designed a
series strategy of generators for multi-scale rs2map translation to address
these limitations. In this strategy, high-resolution RSIs are inputted to an
rs2map model to output large-scale maps, which are translated to multi-scale
maps through series multi-scale map translation models. The series strategy
avoids RS-m inconsistency as inputs are high-resolution large-scale RSIs, and
reduces the distribution gap in multi-scale map generation through similar
pixel distributions among multi-scale maps. Our experimental results showed
better quality multi-scale map generation with the series strategy, as shown by
average increases of 11.69%, 53.78%, 55.42%, and 72.34% in the structural
similarity index, edge structural similarity index, intersection over union
(road), and intersection over union (water) for data from Mexico City and Tokyo
at zoom level 17-13.
