Deep learning model to classify crosswalks, sidewalks, and roads. 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 10, 2024 Item updated: Jan 2, 2025 Number of downloads: 1,314
Description
Pedestrian infrastructure includes sidewalks, crosswalks, and other features that facilitate safe and accessible walking routes. As urban areas continue to expand, the quality and availability of such infrastructure directly impact pedestrian safety, mobility, and overall urban liveability. The Pedestrian Infrastructure Classification model can be used to create detailed maps of sidewalks, crosswalks, and road networks which can aid in urban planning. Such data can also help in smart city initiatives, disaster response, and sustainable and economic development by enabling the creation of more walkable and inclusive urban environments.
The classifications from the model can be used to create pedestrian networks, which can support applications such as pedestrian routing, flow analysis, and the planning of step-free access and vision zero initiatives. It can also be used by planners to enhance urban safety by identifying hazardous areas and improve accessibility for vulnerable users like the elderly and disabled. The model can provide precise data that can assist in optimizing public transportation and reduce CO2 emissions. As cities prioritize pedestrian-friendly environments to address climate change, public health, and economic competitiveness, this model offers a scalable, cost-effective solution to drive sustainable urban development.
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 cannot be fine-tuned using ArcGIS tools.
Input
8-bit, RGB high-resolution (10 - 30 centimeters) imagery.
Output
Classified raster layer representing crosswalks, sidewalks, and roads. Everything else is classified as background class.
Applicable geographies
This model is expected to work well in the following states of the United States of America, as described here.
|
State |
Entire State |
|
CA |
False |
|
DC |
True |
|
MA |
True |
|
NY |
True |
|
NJ |
True |
|
OR |
True |
|
TN |
False |
|
VA |
True |
|
WA |
False |
Model architecture
The implementation is based on the Tile2Net model architecture.
Accuracy metrics
The model has the following metrics on the test data from the cities of Boston, Cambridge, Washington DC, and Manhattan.
|
Label |
IoU (%) |
Precision |
Recall |
|
Sidewalk |
82.67 |
0.90 |
0.92 |
|
Road |
86.04 |
0.91 |
0.94 |
|
Crosswalk |
75.42 |
0.86 |
0.86 |
|
Background |
93.94 |
0.97 |
0.96 |
|
mIoU (%) |
84.51 |
|
|
Here are a few results from the model.
An in-depth description of the item is not available.
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Dashboard views: Desktop
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Dependent items in the recycle bin
Applicable: 2d
Size: 787.202 MB
ID: c0d520baa30d4b47ab36232231c17875
Image Count: 0
Image Properties
Layer Drawing
Using tiles from a cache
Dynamically from data
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Credits (Attribution)
No acknowledgements.Hosseini, M., Sevtsuk, A., Miranda, F., Cesar, R. M., & Silva, C. T. (2023). Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery. Computers Environment and Urban Systems, 101, 101950. https://doi.org/10.1016/j.compenvurbsys.2023.101950
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