Tile2Net is an end-to-end tool for automated mapping of pedestrian infrastructure from aerial imagery. We trained a semantic segmentation model to detect roads, sidewalk, crosswalk, and footpath from orthorectified imagery. The results are then converted to geo-referenced polygons and finally a topologically interconnected centerline network is generated. This work is as an important step towards a robust and open-source framework that enables comprehensive digitization of pedestrian infrastructure, which we argue to be a key missing link to more accurate and reliable pedestrian modeling and analyses. By offering low-cost solutions to create planimetric dataset describing pedestrian environment, we enable cities with a tight budget to create datasets describing pedestrian environment which otherwise would not be possible at a comparable cost and time.

The model is presented in our paper published at the Computers Environment and Urban Systems journal.

Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery
Maryam Hosseini, Andres Sevtsuk, Fabio Miranda, Roberto M. Cesar Jr, Claudio T. Silva
Computers, Environment and Urban Systems, 101 (2023) 101950

For a detailed overview of tile2net, check our Github: https://github.com/VIDA-NYU/tile2net

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