Building footprint layers are useful in preparing basemaps and analysis workflows for urban planning and development. They also are used in insurance, taxation, change detection, infrastructure planning, and a variety of other applications.
Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models are highly capable of learning these complex semantics and can produce better results. Use this deep learning model to automate the manual process of extracting building footprints, reducing time and effort significantly.
To complete this workflow, the following are the license requirements:
- ArcGIS Desktop—ArcGIS Image Analyst and ArcGIS 3D Analyst extension extensions for ArcGIS Pro
- ArcGIS Enterprise—ArcGIS Image Server with raster analytics configured
- ArcGIS Online—ArcGIS Image for ArcGIS Online
This model has the following characteristics:
- Input—Raster, mosaic dataset, or image service.
- Output—Feature class containing building footprints.
- Compute—This workflow is compute intensive and a GPU with compute capability of 6.0 or higher is recommended.
- Applicable geographies—This model is expected to work well in Australia.
- Architecture—This model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.
- Accuracy metrics—This model has an average precision score of 0.794.
Access and download the model
Download the Building Footprint Extraction—Australia pretrained model from ArcGIS Living Atlas of the World. Alternatively, access the model directly from ArcGIS Pro, or consume it in ArcGIS Image for ArcGIS Online.
To download the model, complete the following steps:
- Browse to ArcGIS Living Atlas of the World.
- Sign in with your ArcGIS Online credentials.
- Search for Building Footprint Extraction—Australia and open the item page from the search results.
- Click the Download button to download the model.
You can use the downloaded .dlpk file directly in ArcGIS Pro, or upload and use it in ArcGIS Enterprise. Additionally, you can fine-tune the pretrained model if necessary.
The following are the release notes: