Software Access

Description

FOTOTEX introduces a unique, fast and unsupervised method to characterize urban areas at three nested scales: macro-scale (urban footprint), meso-scale (“neighbourhoods”) and micro-scale (urban objects). FOTOTEX combines Fourier’s Transform and Principal Component Analysis to convert the spatial signal (patterns in the image) into a frequency signal (number of times the pattern is repeated in a defined area). 

[Figure 1] The Fototex method enables the characterization of urban areas at three nested scales.

The results on Bouaké (Ivory Coast) [Figure 2] and Brasília (Brazil) have shown that a single Sentinel-2 image delineates the urban footprint (macro scale) better than some global products based on time series. Pleiades images identify different neighbourhoods and urban objects (meso- and micro-scales) using the texture signal, which is correlated with parameters such as building density, proportions of buildings and vegetation.

Figure 2. Résultats issus de l'algorithme FOTOTEX sur la ville de
Bouaké (Côte d’Ivoire) à trois échelles différentes : (a) délimitation de la tâche urbaine (b) identification d'unités urbaines, (c) densité urbaine et (d) extraction des contours d'objets urbains.
[Figure 2] Results from the FOTOTEX algorithm for the city of Bouaké (Ivory Coast) at three different scales: (a) delimitation of the urban footprint, (b) identification of urban units, (c) urban density and (d) extraction of urban object contours.

Research work based on FOTOTEX has been carried out in the framework of the TOSCA CNES APUREZA and DELICIOSA projects and is linked to the activities of the UMR Espace-Dev in the framework of Theia Risks Associated with Infectious Disease Risks SEC and Urban SEC.

Contact

Thibault Catry

Thibault Catry
IRD | Espace DEV
@T.Catry

Reference

Teillet, C.; Pillot, B.; Catry, T.; Demagistri, L.; Lyszczarz, D.; Lang, M.; Couteron, P.; Barbier, N.; Adou Kouassi, A.; Gunther, Q. ; et al. Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data. Remote Sens. 2021,13, 2398. doi.org/10.3390/rs13122398.