Considering the success of generative adversarial networks (GANs) for image-to-image translation, researchers have attempted to translate satellite images to maps (si2map) through GAN for cartography. However, these studies involved limited scales, which hinders multiscale map creation. By extending their method, high-resolution satellite images can be trivially translated to multiscale maps through scale-wise si2map generators trained for certain scales. However, this strategy has two theoretical limitations. First, inconsistency between high-resolution satellite images and object generalization on multiscale maps (SI-M inconsistency) increasingly complicates the extraction of geographical information from satellite images for generators with decreasing scale. Second, as si2map translation is cross-domain, generators incur high computation costs to transform the pixel distribution on satellite images to that on maps. Thus, we designed a series strategy of generators for multiscale si2map translation to address these limitations. In this strategy, high-resolution satellite images are inputted to an si2map generator to output large-scale maps, which are translated to multiscale maps through series multiscale map generators. The series strategy avoids SI-M inconsistency as high-resolution satellite images are only translated to large-scale maps and transforms cross-domain translation to approximately intradomain translation when generating multiscale maps. Our experimental results showed better quality multiscale 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 (SSIM), edge structural similarity index (ESSI), intersection over union (road), and intersection over union (water) for data from Mexico City and Tokyo at zoom levels 17–13.

Traditional online map tiles, which are widely used on the Internet, such as by Google Maps and Baidu Maps, are rendered from vector data. The timely updating of online map tiles from vector data, for which generation is time-consuming, is a difficult mission. Generating map tiles over time from remote sensing images is relatively simple and can be performed quickly without vector data. However, this approach used to be challenging or even impossible. Inspired by image-to-image translation (img2img) techniques based on generative adversarial networks (GANs), we proposed a semisupervised generation of styled map tiles based on the GANs (SMAPGAN) model to generate styled map tiles directly from remote sensing images. In this model, we designed a semisupervised learning strategy to pretrain SMAPGAN on rich unpaired samples and fine-tune it on limited paired samples in reality. We also designed the image gradient L1 loss and the image gradient structure loss to generate a styled map tile with global topological relationships and detailed edge curves for objects, which are important in cartography. Moreover, we proposed the edge structural similarity index (ESSI) as a metric to evaluate the quality of the topological consistency between the generated map tiles and ground truth. The experimental results show that SMAPGAN outperforms state-of-the-art (SOTA) works according to the mean squared error, the structural similarity index, and the ESSI. Also, SMAPGAN gained higher approval than SOTA in a human perceptual test on the visual realism of cartography. Our work shows that SMAPGAN is a new tool with excellent potential for producing styled map tiles. Our implementation of SMAPGAN is available at https://github.com/imcsq/SMAPGAN.

Bug reports in a repository are generally organized line by line in a list-view, with their titles and other meta-data displayed. In this list-view, a concise and precise title plays an important role that enables project practitioners to quickly and correctly digest the core idea of the bug, without carefully reading the corresponding details. However, the quality of bug report titles varies in open-source communities, which may be due to the limited time and unprofessionalism of authors. To help report authors efficiently draft good-quality titles, we propose a method, named iTAPE, to automatically generate titles for their bug reports. iTAPE formulates title generation into a one-sentence summarization task. By properly tackling two domain-specific challenges (i.e. lacking off-the-shelf dataset and handling the low-frequency human-named tokens), iTAPE then generates titles using a Seq2Seq-based model. A comprehensive experimental study shows that iTAPE can obtain fairly satisfactory results, in terms of the comparison with three latest one-sentence summarization works, as well as the feedback from human evaluation.