Construction stage detection using satellite or aerial imagery

1. A method comprising:

receiving a satellite or aerial image, or portion thereof, of a geographic area comprising one or more houses under construction as input into a machine learned model trained with a set of satellite or aerial images having houses representing a plurality of different stages of construction and corresponding labels representing such stages;

predicting which houses in the satellite or aerial image or portion thereof have completed which stages of construction by way of the machine learned model; and

sending an output from the machine learned model comprising the predictions, wherein the output comprises text comprising:

a feature name;

a label for the feature representing a prediction chosen from slab, foundation, under construction, and completed;

a decimal probability that the label for the feature is correct; and

a centroid and polygonal boundary of the feature, both expressed as geographical coordinates;

wherein the text is formatted and standardized for input into a Geographic Information System application;

wherein the different stages of construction are slab, foundation, under construction, and completed.

2. The method of claim 1 , wherein the machine learned model is a trained Convolutional Neural Network (CNN).

3. The method of claim 1 , wherein the machine learned model is stored on at least one non-transitory computer-readable memory.

4. The method of claim 1 , wherein the receiving, predicting, and sending are performed by one or more processor.

5. The method of claim 1 , further comprising storing the satellite or aerial image, or portion thereof, of a geographic area on at least one non-transitory computer-readable memory.

6. The method of claim 1 , wherein the satellite or aerial image or portion thereof is received from a satellite or aerial imagery provider service.

7. The method of claim 1 , wherein the output comprises a probability that the predicted stage of construction is correct.

8. The method of claim 1 , further comprising converting pixel locations in the satellite or aerial image or portion thereof to geographic coordinates, and outputting the geographic coordinates.

9. The method of claim 1 , wherein the output is displayed on the satellite or aerial image or portion thereof provided as input to the machine learned model.

10. The method of claim 9 , wherein the output comprises a center point, boundary, geographic location, and stage of construction for each of the houses in the satellite or aerial image or portion thereof.

11. The method of claim 1 , wherein the image and set of images are satellite images.

12. One or more non-transitory, computer-readable storage media having instructions for execution by the one or more processors, the instructions programmed to cause the one or more processors to:

receive a satellite or aerial image, or portion thereof, of a geographic area comprising one or more houses under construction as input into a machine learned model trained with a set of satellite or aerial images having houses representing a plurality of different stages of construction and corresponding labels representing such stages;

predict which houses in the satellite or aerial image or portion thereof have completed which stages of construction by way of the machine learned model; and

send an output from the machine learned model comprising the predictions, wherein the output comprises text comprising:

a feature name;

a label for the feature representing a prediction chosen from slab, foundation, under construction, and completed;

a decimal probability that the label for the feature is correct; and

a centroid and polygonal boundary of the feature, both expressed as geographical coordinates;

wherein the text is formatted and standardized for input into a Geographic Information System application;

wherein the different stages of construction are slab, foundation, under construction, and completed.

13. The one or more non-transitory, computer readable storage media of claim 12 , wherein the machine learned model is a trained Convolutional Neural Network (CNN) stored thereon.

14. A computer or computer system, comprising:

one or more processors designed to execute instructions;

one or more non-transitory, computer-readable memories storing program instructions for execution by the one or more processors, the instructions programmed to cause the one or more processors to:

receive a satellite or aerial image, or portion thereof, of a geographic area comprising one or more houses under construction as input into a machine learned model trained with a set of satellite or aerial images having houses representing a plurality of different stages of construction and corresponding labels representing such stages;

predict which houses in the satellite or aerial image or portion thereof have completed which stages of construction by way of the machine learned model; and

send an output from the machine learned model comprising the predictions,

wherein the output comprises text comprising:

a feature name;

a label for the feature representing a prediction chosen from slab, foundation, under construction, and completed;

a decimal probability that the label for the feature is correct and

a centroid and polygonal boundary of the feature, both expressed as geographical coordinates;

wherein the text is formatted and standardized for input into a Geographic Information System application;

wherein the different stages of construction are slab, foundation, under construction, and completed.

15. The computer or computer system of claim 14 , wherein the machine learned model is a trained Convolutional Neural Network (CNN) stored on one or more of the non-transitory computer readable memories.