AI-assisted mapping of for OSM

Anna Zanchetta

Open Mapping Hub Asia-Pacific

WEBINAR

12/13/2024

Introduction

fAIr

https://fair.hotosm.org/

What is affecting fAIr’s performance?

RGB from OAM








Open Aerial Map

Labels from OSM








Preprocessing in fAIr

Dataset

Urban regions	25
Countries	21
Zoom levels	19, 20, 21
N. images	8400 (~350 per region)
Images size	256x256
Resolution	cm

Locations

Degree of urbanity

Rural

Desa Kulaba

[Indonesia]

Peri-urban

Ggaba

[Uganda]

Urban

Bogota

[Colombia]

Refugee camp

Kakuma

[Kenya]

Roof cover type

Shingles

Silvania

[Brasil]

Metal

Ngaoundere

[Cameroon]

Cement

Melbourne

[Australia]

Mixed

Kutupalong

[Bangladesh]

Urban density

Dense

Montevideo

[Uruguay]

Sparse

Gornja Rijeka

[Croatia]

Grid

Quincy

[USA]

Analysis & Results

Metrics

Source: https://metrics-reloaded.dkfz.de/metric-library

Metrics

Source: https://metrics-reloaded.dkfz.de/metric-library

Training

Urban regions	all (25)
N. of epochs	20
Batch sizes	4 (2, 4, 8, 16)
Accuracy metrics	5 Categorical accuracy, Precision, Recall, F1 Score, IoU

Urbanity

Roof type

Density

Banyuwangi

[Indonesia]

Banyuwangi

[Indonesia]

Banyuwangi

[Indonesia]

Banyuwangi

[Indonesia]

Pallaby, Dhaka

[Bangladesh]

Pallaby, Dhaka

[Bangladesh]

Pallaby, Dhaka

[Bangladesh]

Pallaby, Dhaka

[Bangladesh]

Denver

[USA]

Denver

[USA]

Pergamino

[Argentina]

Pergamino

[Argentina]

Kakuma

[Kenya]

Kakuma

[Kenya]

Future

Alternative models

Other features

THANK YOU

fair.hotosm.org/
github.com/hotosm/fAIr-utilities
en.osm.town/@ciupava