This dataset contains 11,842,186 computer generated building footprints in all Canadian provinces and territories. This data is freely available for download and use.

This data is licensed by Microsoft under the Open Data Commons Open Database License (ODbL)

11,842,186 building footprint polygon geometries from all Canadian provinces and territories in GeoJSON format.

GeoJSON is a format for encoding a variety of geographic data structures. For Intensive Documentation and Tutorials, Refer to GeoJson Blog

The building extraction is done in two stages:

1. Semantic Segmentation – Recognizing building pixels on the aerial image using DNNs
2. Polygonization – Converting building pixel blobs into polygons

The network foundation is ResNet34 which can be found here. In order to produce pixel prediction output, we have appended RefineNet upsampling layers described in this paper. The model is fully-convolutional, meaning that the model can be applied on an image of any size (constrained by GPU memory, 4096x4096 in our case).

The training set consists of 3 million labeled images. Majority of the satellite images cover diverse residential areas in Canada. For the sake of good set representation, we have enriched the set with samples from various areas covering mountains, glaciers, forests, beaches, coasts, etc. Images in the set are of 256x256 pixel size with 1 ft/pixel resolution. The training is done with CNTK toolkit using 32 GPUs.

We developed a method that approximates the prediction pixels into polygons making decisions based on the whole prediction feature space. This is very different from standard approaches, e.g. Douglas-Peucker algorithm, which are greedy in nature. The method tries to impose some of a priori building properties, which is, at the moment, manually defined and automatically tuned.

Building matching metrics on our evaluation set:

False positive ratio across the board is less than 0.5%.

We track various metrics to measure the quality of the output:

1. Intersection over Union – This is the standard metric measuring the overlap quality against the labels
2. Shape distance – With this metric we measure the polygon outline similarity
3. Dominant angle rotation error – This measures the polygon rotation deviation

On our evaluation set contains ~45k building. The metrics on the set are:

• IoU is 0.76, Shape distance is 0.43, Average rotation error is 3.7 degrees

The vintage of the footprints depends on the vintage of the underlying imagery. Because Bing Imagery is a composite of multiple sources it is difficult to know the exact dates for individual pieces of data.

Our metrics show that in the vast majority of cases the quality is at least as good as data hand digitized buildings in OpenStreetMap. It is not perfect, and false positives exist, but most areas look awesome.

EPSG: 4326

Maybe. This is a work in progress.

Microsoft has a continued interest in supporting the open data ecosystem.

Maybe. Never overwrite the hard work of other contributors or blindly import data into OSM without first checking the local quality. While our metrics show that this data meets or exceeds the quality of hand drawn building footprints, the data does vary in quality from place to place, between rural and urban, mountains and plains, and so on. Inspect quality locally and discuss an import plan with the community. Always follow the OSM import community guidelines.

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