Deep learning model to detect swimming pools in 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: 9,625
Description
Swimming pools are important for property tax assessment because they impact value. Tax assessors at local government agencies often rely on expensive and infrequent surveys, leading to assessment inaccuracies. Finding pools that are not on the assessment roll (such as those recently constructed) is valuable to assessors and will ultimately mean additional revenue for the community.
This deep learning model helps automate the task of finding pools from high resolution satellite imagery. This model can also benefit swimming pool maintenance companies and help redirect their marketing efforts. Public health and mosquito control agencies can also use this model to detect pools and drive field activity and mitigation efforts.
Using the model
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 (5-30 centimeters) imagery.
Output
Feature class containing bounding boxes depicting pool locations.
Applicable geographies
The model is expected to work well in the United States.
Model architecture
The model uses the FasterRCNN model architecture implemented using ArcGIS API for Python.
Accuracy metrics
The model has an average precision score of 0.59.
Training data
This model has been trained on an Esri proprietary pool 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: 147.583 MB
ID: 0e7dffe605c24bdfadf3c376bdf2d413
Image Count: 0
Image Properties
Layer Drawing
Using tiles from a cache
Dynamically from data
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Credits (Attribution)
No acknowledgements.Esri
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