Deep learning model to extract building footprints in Australia from high-resolution aerial or satellite imagery. A brief summary of the item is not available. Add a brief summary about the item.
Deep learning package
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Item created: Dec 7, 2021 Item updated: Dec 30, 2024 Number of downloads: 6,485
Description
This deep learning model is used to extract building footprints from high-resolution (10–40 cm) imagery. Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development, 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 have a high capacity to learn these complex workflow semantics and can produce superior results. Use this deep learning model to automate this process and reduce the time and effort required for acquiring building footprints.
Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.
Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.
Input
8-bit, 3-band high-resolution (10–40 cm) imagery.
Note: Imagery has to be analyzed at 30 cm resolution for best results.
Output
Feature class containing building footprints.
Applicable geographies
The model is expected to work in Australia.
Model architecture
The model uses the MaskRCNN model architecture implemented using ArcGIS API for Python.
Accuracy metrics
The model has an average precision score of 79.4 percent.
Training data
This model has been trained on an Esri proprietary building footprint extraction dataset.
Limitations
• A random shift between footprints and imagery (around 3-7 meter) has been observed in some areas.
• The model does not work well with highly oblique (off nadir) imagery, especially when delineating footprints of high rise buildings.
Sample results
Here are a few results from the model. To view more, see this story.
An in-depth description of the item is not available.
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Dashboard views: Desktop
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Applicable: 2d
Size: 157.546 MB
ID: 4e38dec1577b4b7da5365294d8a66534
Image Count: 0
Image Properties
Layer Drawing
Using tiles from a cache
Dynamically from data
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Credits (Attribution)
No acknowledgements.Esri, Microsoft
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