Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3376–3385 Mostajabi M, Yadollahpour P, Shakhnarovich G (2015) Feedforward semantic segmentation with zoom-out features. Hou B, Liu Q, Wang H, Wang Y (2020) From W-Net to CDGAN: bitemporal change detection via deep learning techniques. In: International conference on medical image computing and computer-assisted intervention. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. Zhang Y, Yin Y, Zimmermann R, Wang G, Varadarajan J, Ng S-K (2020) An enhanced GAN model for automatic satellite-to-map image conversion. Zhang X, Han X, Li C, Tang X, Zhou H, Jiao L (2019) Aerial image road extraction based on an improved generative adversarial network. Shi Q, Liu X, Li X (2018) Road detection from remote sensing images by generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440 Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE international conference on computer vision (ICCV), pp 2223–2232 Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE Trans Pattern Anal Mach Intell 39(1):128–140 Pont-Tuset J, Arbelaez P, Barron JT, Marques F, Malik J (2017) Multiscale combinatorial grouping for image segmentation and object proposal generation. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 2849–2857Īndrade HJA, Fernandes BJT (2022) Synthesis of satellite-like urban images from historical maps using conditional GAN. Yi Z, Zhang H, Tan P, Gong M (2017) DualGAN: unsupervised dual learning for image-to-image translation. We have used two types of GANs for this process of conversion of satellite images to human-readable maps and compared the results by using various similarity metrics. A generative adversarial network that is GAN is a good approach for generating maps as it is automatic satellite-to-map image conversion. In recent years, satellite images have become more ubiquitous, and converting them to map-style images has attracted attention because it updates frequently and its cost-effective in nature this can be done by image-to-image translation is a general name for a task where an image from one domain is converted to a corresponding image in another domain, given sufficient training data. Conventional map generation involves labor-intensive methods as well as time-consuming manual efforts, which can restrict the updating frequency of maps to a few years or even longer. As a lot of applications like Navigation services significantly rely on up-to-date and accurate maps. To summarize, you need to use Google Earth (desktop app) to determine the capture date of Aerial Images and Google Maps for finding the date of Street View images.Automatically generating maps from satellite images is an important task. Unlike Google Earth, the capture dates available inside Street View images only reveal the month and year of the picture but not the exact date.Īlso see: Find the Location where a photograph was taken The image capture date will be instantly displayed in the status bar as shown in the screenshot below. Next, drag the yellow “Pegman” to any area on the Google Map to switch from aerial to street view. If you happen to live in a country where Google Street View is available, you can use the Google Maps website itself to determine the date when Google Street Views cars were in your area capturing pictures of the neighbourhood. Finding the capture date of Street View Images Now hover your mouse over the map and you should see the capture date of that satellite image in the status bar as seen in the above screenshot. Launch the Google Earth app on your desktop, search for any location in the sidebar and, this is important, zoom in an area as much as possible. For some unknown reason, Google doesn’t display these dates on the Google Maps website or the Google Earth web app. If you wish to know the date when satellites captured those aerial images that you see in Google Maps, you will have to use Google Earth for that. Find the capture date of Satellite Images Or when satellites and planes took those aerial pictures of any location on Google MapsĬurious to know the exact date when Google cameras captured those aerial and street view photographs of your home (or any other address) on our beautiful planet? Well, you can find the dates easily both in Google Maps and Google Earth. Find the exact date when Google Street View cars captured those images of your neighbourhood.
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