An Enhanced GAN Model for Automatic Satellite-to-Map Image Conversion

Ying Zhang,See-Kiong Ng,Roger Zimmermann,Guanfeng Wang,Jagannadan Varadarajan,Yifang Yin

doi:10.1109/access.2020.3025008

Ying Zhang, See-Kiong Ng + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.3025008

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Abstract

Location-based service significantly relies on accurate and up-to-date maps. The conventional map generation involves labor-intensive and time-consuming manual efforts, which restricts the map-update frequency to a few years or even longer. In recent years, satellite images become more ubiquitous, and converting them to map-style images has attracted attention due to its frequent-updating and cost-effective nature. Generative adversarial network (GAN) is a promising approach for automatic satellite-to-map image conversion. However, it is still challenging to process satellite images when the underlying road structure is complex and irregular, or when some objects are visually indistinguishable due to obstruction or bad weather. To address these issues, we propose an enhanced GAN model to generate improved quality map images by bringing in the external geographic data as implicit guidance. The textual geographic data is converted to an image so that it can work collaboratively and seamlessly with the satellite image during the conversion. A high-level semantic regulation is also introduced to further reduce the noisy patterns generated during the translation, which occur frequently for the regions with sparse geographic data. The proposed method is versatile to various backbone GAN structures with a 20% performance improvement on three popular metrics (Inception Score, Frechet Inception Distance Score and SSIM score). Our proposed Semantic-regulated Geographic GAN (SG-GAN) is anticipated to reduce the manual identification efforts in broad geospatial applications.

Highlights

Accurate and up-to-date map images are fundamentally important for many location-based services such as map inference, road attribute detection [19]
GENERATIVE ADVERSARIAL NETWORK (GAN) Our work is based on Generative Adversarial Network (GAN) models and we show some preliminary background in this part
Image segmentation is another potential way for satelliteto-map image conversion but with a few differences from a GAN-based method in the following aspects: 1) The objective of segmentation is to partition an image into sub-regions so that pixels with similar characteristics are grouped together

Summary

INTRODUCTION

Accurate and up-to-date map images are fundamentally important for many location-based services such as map inference, road attribute detection [19]. In this model, the input consists of satellite images and geographic data. To overcome the above challenges, Generative Adversarial Network (GAN) is a new method to direct convert the satellite images to map images [21], [40]. These methods do not require extract the geospatial data for each object and greatly reduce the manual efforts that spent in traditional methods. C. SEMANTIC-REGULATED GAN REFINEMENT Following the above formulation, GPS works as natural guidance to recover the road structure and improves the output’s quality. This predicted semantic mask (M ) is layered together with the satellite image and the GPS image as the generator’s input:

MODEL STRUCTURE

EXPERIMENTS

Findings

CONCLUSION