Deep learning model to detect cars in high resolution 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: May 28, 2021 Item updated: Dec 27, 2024 Number of downloads: 18,208
Description
This deep learning model is used to detect cars in high
resolution drone or aerial imagery. Car detection can be used for applications such as traffic
management and analysis, parking lot utilization, urban planning, etc. It can also be used as a proxy for deriving economic indicators and estimating retail sales. High
resolution aerial and drone imagery can be used for car detection due to its
high spatio-temporal coverage.
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
High resolution RGB imagery
(5 - 20 centimeter spatial resolution).
Output
Feature class containing detected cars.
Applicable geographies
The model is expected to work well in the United States.
Model architecture
This model uses the MaskRCNN model architecture implemented in ArcGIS API
for Python.
Accuracy metrics
This model has an average precision score of 0.81.
Training data
This model has been trained on an Esri proprietary car detection dataset.
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: 156.568 MB
ID: cfc57b507f914d1593f5871bf0d52999
Image Count: 0
Image Properties
Layer Drawing
Using tiles from a cache
Dynamically from data
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Credits (Attribution)
No acknowledgements.Esri
Comments (3)
huh
Thanks for this impressive work. I was wondering if you have the input RGB 3-band image to use as an input for practice?
You could download imagery from https://openaerialmap.org/ to try running this model. Additionally, you could find some sample imagery from our notebooks for example: https://developers.arcgis.com/python/latest/samples/count-cars-in-aerial-imagery-using-deep-learning/