Methods

For this work, we developed a multitemporal and multimodal neural network trained on the Grand-Est region with the MultiSenGE dataset. Further explanations will be made after the publication of the research paper associated with this work. The main idea of this presentation is to evaluate the generalization capacity of a network trained on the Grand-Est for other cities in France. Also, we are going to produce classification products for the Grand-Est region for different years to evaluate if it is possible to perform change detection between two classifications. The products can be downloaded through this webpage.

The classification results of the model for 10 land use/land cover classes for several cities in France can be viewed below.

The figure below represents the legend of MultiSenGE. In this project, we resample the classes to delete the less represented ones. We merged Orchards (8) with Vineyards (7), Groves, Hedges (9) and Open Spaces, Mineral (12) with Forests (10), Wetlands (13) with Water Surfaces (14).

References

This work is the result of several current and previous research projects which are listed below:

Romain Wenger, Anne Puissant, Jonathan Weber, Lhassane Idoumghar & Germain Forestier (2022) U-Net feature fusion for multi-class semantic segmentation of urban fabrics from Sentinel-2 imagery: an application on Grand Est Region, France, International Journal of Remote Sensing, 43:6, 1983-2011, DOI: 10.1080/01431161.2022.2054295

Wenger, R., Puissant, A., Weber, J., Idoumghar, L., and Forestier, G.: MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 635–640, https://doi.org/10.5194/isprs-annals-V-3-2022-635-2022, 2022.

• You can directly view/read the paper following this link.

MultiSenGE dataset can be download on Zenodo:

Romain Wenger, Anne Puissant, Jonathan Weber, Lhassane Idoumghar, & Germain Forestier. (2022). A new remote sensing benchmark dataset for machine learning applications : MultiSenGE (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6375466

Grand-Est 2018-2022

To produce the large scale classification, each tile has been classified for Sentinel-1 and Sentinel-2 imagery and reprojected to Lambert-93 using the gdal command below:

              $  gdalwarp -ot Byte -of GTiff -co COMPRESS=LZW -t_srs EPSG:2154 [input_file] [output_file]
            

Then, each tile reprojected had been merged. No Data values are mapped with 0 :

              $  gdal_merge.py -ot Byte -of GTiff -n 0 -a_nodata 0 -co COMPRESS=LZW -o [output_file] [input_files ...]
            

Finally, merging results has been cut using the shape of the Grand-Est region :

              $  gdalwarp -tr 10.0 10.0 -ot Byte -of GTiff -co COMPRESS=LZW -cutline [Grand_Est.shp] -crop_to_cutline [input_file] [Grand_Est_2018.tif]
            

Grand-Est classification for 2018 :

Grand-Est classification for 2022 :

You can download style file for both classification.

We produced change detection maps for some cities in the Grand-Est region :

Here are the dates for each Sentinel-2 images selected per tile to perform 2018 classification:

Toulouse

Toulouse is a city in the southwest of France with more than 400,000 inhabitants and an area of 118 km2.

Here are the Sentinel-2 dates used to perfom the classification:

Dijon

Dijon is a French city, prefecture of the Côte-d'Or department and capital of the Bourgogne-Franche-Comté region. It has more than 160,000 inhabitants and covers 40 km2.

Here are the Sentinel-2 dates used to perfom the classification:

Orléans

Orléans is a city in the Center-Northwest of France on the banks of the Loire, prefecture of the Loiret department and capital of the Centre-Val de Loire region. It has more than 116,000 inhabitants and covers 27 km2.

Here are the Sentinel-2 dates used to perfom the classification:

Lille

Lille is a city in the north of France, prefecture of the Nord department and capital of the Hauts-de-France region. It has more than 235,000 inhabitants and covers 34 km2.

Here are the Sentinel-2 dates used to perfom the classification:

Rennes

Rennes is a city in the North-West of France, capital of the Ille-et-Vilaine department and of the Brittany region. It has more than 220,000 inhabitants and covers 50 km2.

Here are the Sentinel-2 dates used to perfom the classification:

Breme

Breme is a city in the North of Germany not far from the North Sea. It has more than 550,000 inhabitants and covers 326 km2.

Here are the Sentinel-2 dates used to perfom the classification:

Seville

Seville is a city in the South of Spain. It has more than 680,000 inhabitants and covers 140 km2.

Here are the Sentinel-2 dates used to perfom the classification: