10m resolution for building footprints is optimistic at best. Most buildings aren't that all that big (20m by 20m is a fairly big house), and they are also very inconsistently shaped and coloured. All in all, you chose a fairly difficult target and a dataset not entirely suited for the application.

The whole problem that you are looking at is much better suited for higher resolution imagery, and even then, it is not very easy - see for example the attempt at DigitalGlobe did with their very-high resolution data and the cloud platform that they have designed: https://platform.digitalglobe.com/gbdx/gbdx-solutions/
If you look at the results in their example, you'll notice that even a large company, with a fair bit of resources and a much more suitable dataset doesn't really perform that well either in the same task as the one you undertook. I'm not saying that it is impossible, but the real world is a much more difficult dataset than the audited and controlled training datasets that you have practiced on.

10m resolution for building footprints is optimistic at best. Most buildings aren't that all that big (20m by 20m is a fairly big house), and they are also very inconsistently shaped and coloured. All in all, you chose a fairly difficult target and a dataset not entirely suited for the application.

The whole problem that you are looking at is much better suited for higher resolution imagery, and even then, it is not very easy - see for example the attempt that DigitalGlobe did with their very-high resolution data and the cloud platform that they have designed: https://platform.digitalglobe.com/gbdx/gbdx-solutions/
If you look at the results in their example, you'll notice that even a large company, with a fair bit of resources and a much more suitable dataset doesn't really perform that well either in the same task as the one you undertook. I'm not saying that it is impossible, but the real world is a much more difficult dataset than the audited and controlled training datasets that you have practiced on.

10m resolution for building footprints is optimistic at best. Most buildings aren't that all that big (20m by 20m is a fairly big house), and they are also very inconsistently shaped and coloured. All in all, you chose a fairly difficult target and a dataset not entirely suited for the application.

The whole problem that you are looking at is much better suited for higher resolution imagery, and even then, it is not very easy - see for example the attempt at DigitalGlobe did with their very-high resolution data and the cloud platform that they have designed: https://platform.digitalglobe.com/gbdx/gbdx-solutions/
If you look at the results you on their example, you'll notice that even a large company, with a fair bit of resources and a much more suitable dataset doesn't really perform that well either in the same task as the one you undertook. I'm not saying that it is impossible, but the real world is a much more difficult dataset than the audited and controlled training datasets that you have practiced on.

10m resolution for building footprints is optimistic at best. Most buildings aren't that all that big (20m by 20m is a fairly big house), and they are also very inconsistently shaped and coloured. All in all, you chose a fairly difficult target and a dataset not entirely suited for the application.

The whole problem that you are looking at is much better suited for higher resolution imagery, and even then, it is not very easy - see for example the attempt at DigitalGlobe did with their very-high resolution data and the cloud platform that they have designed: https://platform.digitalglobe.com/gbdx/gbdx-solutions/
If you look at the results in their example, you'll notice that even a large company, with a fair bit of resources and a much more suitable dataset doesn't really perform that well either in the same task as the one you undertook. I'm not saying that it is impossible, but the real world is a much more difficult dataset than the audited and controlled training datasets that you have practiced on.