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Deep learning model to extract building footprints in Africa from high-resolution aerial or satellite imagery. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: May 27, 2021 Item updated: Dec 30, 2024 Number of downloads: 12,282

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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.

Output
Feature class containing building footprints.

Applicable geographies
The model is expected to work in Africa and gives the best results in Uganda and Tanzania.

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 0.786.

Sample results
Here are a few results from the model. To view more, see this story.





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Terms of Use

https://downloads.esri.com/blogs/arcgisonline/esrilogo_new.png This work is licensed under the Esri Master License Agreement.

No special restrictions or limitations on using the item's content have been provided.

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          Size: 157.69 MB

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          ID: 979cb0cf938946bfb8bb2f41cf9f9795

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          dlpk, building footprint, deep learning, living atlas, urban planning

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          Esri, Microsoft

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