kauevestena/deep_pavements_project

This repository is meant to be the main one to organize the project Deep Pavements, which is meant to leverage AI tools to do pavement surface segmentation in CC-Compatible street-level imagery, with categories types complying with OpenStreetMap Tags.

Deep Pavements Project

[1]

About

Deep Pavements is a framework to leverage open-source tools to produce data about pathway pavements using CC-compatible street-level Imagery. As a project highlight, the pavement classes are compliant with the OpenStreetMap surface=* tag values.

The main products that are meant to be created are with the provided tools are:

• I Regionally optimized Deep Learning models to do segmentation of road pavements and objects that may interfere with them.
• Datasets to train semantic segmentation models.
• A surface-patch dataset to train image classification models.

Modules

The modules that made up the project are each one in a separate repository:

• Sample Picker: to generate samples from randomly selected images from Mapillary.
• Sample Labeler: To manually check the samples.
• Surface Patch Dataset: A manually curated dataset of surface patches.
• Model Trainer: To train semantic segmentation models.

A module "Data Processer" is also planned. This module will be used to generate the surface patches for an specified location.

References:

[1] : The logo was produced using Microsoft Copilot, we plan to replace it with in the future with a CC one.