Detection and replacement of transient obstructions from high elevation digital images
Implementations relate to detecting/replacing transient obstructions from high-elevation digital images. A digital image of a geographic area includes pixels that align spatially with respective geographic units of the geographic area. Analysis of the digital image may uncover obscured pixel(s) that align spatially with geographic unit(s) of the geographic area that are obscured by transient obstruction(s). Domain fingerprint(s) of the obscured geographic unit(s) may be determined across pixels of a corpus of digital images that align spatially with the one or more obscured geographic units. Unobscured pixel(s) of the same/different digital image may be identified that align spatially with unobscured geographic unit(s) of the geographic area. The unobscured geographic unit(s) also may have domain fingerprint(s) that match the domain fingerprint(s) of the obscured geographic unit(s). Replacement pixel data may be calculated based on the unobscured pixels and used to generate a transient-obstruction-free version of the digital image.
1. A method implemented using one or more processors, comprising:
obtaining a digital image of a geographic area captured from an elevated vantage point, wherein one or more obscured portions of the geographic area are obscured by one or more transient obstructions depicted in the digital image; and
generating a synthetic digital image based on the digital image, wherein in the synthetic digital image, pixels of one or more of the transient obstructions are replaced with predicted pixels representing underlying terrain, wherein the generating includes applying the digital image of the geographic area as input across a generator model, wherein the generator model is trained as part of a generative adversarial network that also included a discriminator model that is trained in conjunction with the generator model;
wherein the generator model comprises a memory network, the digital image is one of a temporal sequence of digital images of the geographic area, and the applying includes sequentially applying the temporal sequence of digital images as inputs across the generator model.
2. The method of claim 1 , wherein the digital image is captured by a satellite, and the elevated vantage point lies on a trajectory of the satellite.
3. The method of claim 1 , wherein the generator model comprises a recurrent neural network.
4. The method of claim 1 , wherein the generator model comprises a long short-term memory (LSTM) network.
5. A method implemented using one or more processors, comprising:
obtaining a digital image of a geographic area captured from an elevated vantage point, wherein one or more obscured portions of the geographic area are obscured by one or more transient obstructions depicted in the digital image; and
generating a synthetic digital image based on the digital image, wherein in the synthetic digital image, pixels of one or more of the transient obstructions are replaced with predicted pixels representing underlying terrain, wherein the generating includes applying the digital image of the geographic area as input across a generator model, wherein the generator model is trained as part of a generative adversarial network that also included a discriminator model that is trained in conjunction with the generator model;
wherein the generator model and the discriminator model are trained using at least one training example comprising a high elevation digital image with synthetic transient obstructions added, wherein the synthetic transient obstructions are added by a transient obstruction generation model that is trained as part of another generative adversarial network.
6. A system comprising one or more processors and memory storing instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to:
obtain a digital image of a geographic area captured from an elevated vantage point, wherein one or more obscured portions of the geographic area are obscured by one or more transient obstructions depicted in the digital image; and
generate a synthetic digital image based on the digital image, wherein in the synthetic digital image, pixels of one or more of the transient obstructions are replaced with predicted pixels representing underlying terrain, wherein to generate the synthetic image, the digital image of the geographic area is applied as input across a generator model, wherein the generator model is trained as part of a generative adversarial network that also included a discriminator model that is trained in conjunction with the generator model;
wherein the generator model comprises a memory network, the digital image is one of a temporal sequence of digital images of the geographic area, and the generating includes applying the temporal sequence of digital images as inputs across the generator model.
7. The system of claim 6 , wherein the digital image is captured by a satellite, and the elevated vantage point lies on a trajectory of the satellite.
8. The system of claim 6 , wherein the generator model comprises a recurrent neural network.
9. The system of claim 6 , wherein the generator model comprises a long short-term memory (LSTM) network.
10. A system comprising one or more processors and memory storing instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to:
obtain a digital image of a geographic area captured from an elevated vantage point, wherein one or more obscured portions of the geographic area are obscured by one or more transient obstructions depicted in the digital image; and
generate a synthetic digital image based on the digital image, wherein in the synthetic digital image, pixels of one or more of the transient obstructions are replaced with predicted pixels representing underlying terrain, wherein to generate the synthetic image, the digital image of the geographic area is applied as input across a generator model, wherein the generator model is trained as part of a generative adversarial network that also included a discriminator model that is trained in conjunction with the generator model;
wherein the generator model and the discriminator model are trained using at least one training example comprising a high elevation digital image with synthetic transient obstructions added, wherein the synthetic transient obstructions are added by a transient obstruction generation model that is trained as part of another generative adversarial network.