MapGAN: An Intelligent Generation Model for Network Tile Maps

Li, Jingtao; Chen, Zhanlong; Zhao, Xiaofan; Shao, Lingyun

doi:10.3390/s20113119

Cited by 20 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automatic map translation from RS images has recently attracted more and more attention from academia and industry [26]. Pix2pix [2] is first used in map translation.…”

Section: Automatic Map Translation From Rs Imagesmentioning

confidence: 99%

CscGAN: Conditional Scale-Consistent Generation Network for Multi-Level Remote Sensing Image to Map Translation

et al. 2021

Self Cite

View full text Add to dashboard Cite

Automatic remote sensing (RS) image to map translation is a crucial technology for intelligent tile map generation. Although existing methods based on a generative network (GAN) generated unannotated maps at a single level, they have limited capacity in handling multi-resolution map generation at different levels. To address the problem, we proposed a novel conditional scale-consistent generation network (CscGAN) to simultaneously generate multi-level tile maps from multi-scale RS images, using only a single and unified model. Specifically, the CscGAN first uses the level labels and map annotations as prior conditions to guide hierarchical feature learning with different scales. Then, a multi-scale discriminator and two multi-scale generators are introduced to describe both high-resolution and low-resolution representations, aiming to improve the similarity of generated maps and thus produce high-quality multi-level tile maps. Meanwhile, a level classifier is designed for further exploring the characteristics of tile maps at different levels. Moreover, the CscGAN is optimized by jointly multi-scale adversarial loss, level classification loss, and scale-consistent loss in an end-to-end manner. Extensive experiments on multiple datasets and study areas demonstrate that the CscGAN outperforms the state-of-the-art methods in multi-level map translation, with great robustness and efficiency.

show abstract

“…Automatic map translation from RS images has recently attracted more and more attention from academia and industry [26]. Pix2pix [2] is first used in map translation.…”

Section: Automatic Map Translation From Rs Imagesmentioning

confidence: 99%

CscGAN: Conditional Scale-Consistent Generation Network for Multi-Level Remote Sensing Image to Map Translation

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Researchers have recently begun to investigate applications of GANs with geospatial data. For example, unconditional GANs have been used to generate realistic synthetic satellite images of landscapes and cities from randomly initiated noises (Abady et al, 2020;Zhao et al, 2021), and conditional GANs have been explored to translate satellite images into cartographic representations (Isola et al, 2017;Li et al, 2020a) or generate cartographic representations from geospatial vector data (Kang et al, 2019). Conditional GANs can also create semantic-responsive land-cover maps with user-drawn colour masks, adding human input in the generative process Baier et al, 2021).…”

Section: Applications Of Generative Adversarial Network In Gismentioning

confidence: 99%

GANmapper: geographical data translation

Wu,

Biljecki

2021

Preprint

View full text Add to dashboard Cite

We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints, and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables generating approximate maps of the urban form, and it is generalisable to augment other types of geoinformation, enhancing the completeness and quality of spatial data infrastructure. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data.

show abstract

“…During the training process, we also add the reshape and convolution operation for converting the feature matrix dimension of Mixed_7c layer to 2 × 3 × 3 to concatenate with the original image. There is only one generator in our model and GAN chooses the similar generator architecture as MapGAN [40], which is mainly composed of the down-sampling layer, residual block, and up-sampling layer as shown in Figure 7. Each residual block contains two convolutional layers that do not change the dimensions of the input data.…”

Section: Gan Embedded Cnn and Lstmmentioning

confidence: 99%

An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

Shen

Yang

2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

show abstract

MapGAN: An Intelligent Generation Model for Network Tile Maps

Cited by 20 publications

References 15 publications

CscGAN: Conditional Scale-Consistent Generation Network for Multi-Level Remote Sensing Image to Map Translation

CscGAN: Conditional Scale-Consistent Generation Network for Multi-Level Remote Sensing Image to Map Translation

GANmapper: geographical data translation

An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

Contact Info

Product

Resources

About