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Deep learning model to detect and classify parking spots in high resolution imagery. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: Mar 29, 2023 Item updated: Jan 2, 2025 Number of downloads: 4,684

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Description

This deep learning model is used to detect and classify parking spots in high resolution drone or aerial imagery.

Due to the increase in the number of private vehicles, finding a vacant parking spot is very exhausting. It consumes a lot of time and money. This is also responsible for the increase in toxic vehicular emissions which contribute to air pollution. This is especially a big problem in metropolitan cities. Traditionally, parking lots are manually digitized and classified, which is a very labour and time-intensive task. Automating the task of detecting and classifying parking lots using a deep learning model can be a good alternative to this manual process. This model uses high-resolution aerial and drone imagery due to its high spatio-temporal coverage, which is required for such tasks.

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 (7-10 centimeters) imagery.

Output

Feature class representing classified parking spots. 

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 0.64 for vacant parking spots and 0.69 for occupied parking spots class.

Training data
This model has been trained on an Esri proprietary parking spot detection dataset.

Sample results

Here are a few results from the model.




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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.

Comments (3)

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Marvin.liedl_dhbw Item Owner commented a month ago Delete Reply

In the documentation its stated that "Fine-tuning a model requires less training data, computational resources, and time compared to training a new model" but how much training data is 'Less training data'? Is there a rule of thumb or estimate to that?

raven.schmidt@gmx.de Item Owner commented a year ago Delete Reply

Do I get that right, that I need all three Licenses to use to tool? ArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS Pro ArcGIS Enterprise – ArcGIS Image Server with raster analytics configured ArcGIS Online – ArcGIS Image for ArcGIS Onlin

spathak_deldev Item Owner commented a year ago Delete

Hi, we have three products named ArcGIS Desktop, ArcGIS Enterprise and ArcGIS Online. If you want to run the tool on ArcGIS Desktop (ArcGIS Pro) you need ArcGIS Image Analyst extension for ArcGIS Pro, for using the tool on ArcGIS Enterprise you need ArcGIS Image Server with raster analytics and for using the tool on ArcGIS Online you need ArcGIS Image for ArcGIS Online.

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    Dashboard views: Desktop

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          Size: 157.846 MB

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          ID: 5b8e047f11324775832550c6bab19d2b

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          dlpk, deep, deep lea, pretrained model, Parking Spot Detection, LivingAtlasDLPK

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