The land cover classification for France in 2016 is available

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Poster_OSO_V4.png

just this once, we are ahead of time. Well, nearly. We had promised the 2016 land cover map or France before the end of first term of 2017. It exists and is available here. It's resolution is 10m, with the same 17 classes nomenclature that we used for Landsat landcover map of 2014..

The map is mainly based on the Sentinel-2 data acquired from end 2015 to end 2016, but we have also processed the LANDSAT 8 data.  We will provide some details below.

In terms of quality, the statistics indicate something quite similar to what we were getting with Landsat-8. The improvements made by Sentinel-2 are not insignificant (just look at the map), but they are not visible on the overall statistics for the whole territory.

We have also produced regularized vector maps with a minimum mapping unit of 0.1 ha, correponding to 20m resolution.

Viewing interface

The classification is displayed on a very high resolution imaging background. You can play on the opacity with the cursor under the image. If you click on a pixel, the name of the class is displayed. 

At the top right of the visualization interface you will find a button that allows you to display 2 additional layers:

  • Validity: This is the number of valid dates (no cloud, no shadow) used to recognize the class of each pixel. You will see that there is a big difference between the areas because of the satellite tracks and, of course, because of the weather.
  • Trust: This is the probability of the class chosen for each pixel. The classification algorithm makes a majority vote from 100 individual classifiers. Confidence is the percentage of classifiers who voted for the majority class. With 17 classes, this probability could be less than 6%, but overall it is well above 40%. We note that confidence is lower in the West, around the Mediterranean and in the big cities. Inversely, it is raised on the main crops of the centre of France and on the forests (one recognizes the forest of Landes on the map of confidence).

 

Below the image display, we find the validation statistics. First, there are the overall percentages:

  • OA: Overall Accuracy, that gives the percentage of well-classified pixels
  • Kappa: statistical index similar to the OA, but corrected downwards to take into account that a random classification would also have well-ranked pixels.

For each class, we also display the FScore, a metric whose optimum is 1 and which combines over-detection and under-detection.

As artificialized surface classes are very confused with each other, we have added the FScore of the four classes altogether. For annual crops (summer and winter), the classification procedure (described below) does not allow us to validate with the same set of data as for the rest of the classes. So we provide the FScore with the 2 classes together, but also measured with separate classes using a restricted dataset.

FiIe download :

To get the rasters under Geotiff format, you can use the following links :

Production process

For this first "official" production, we encountered a lot of difficulties  (regarding data availabilitycomputer ressources, or unexpected computing results) which mobilized a great research effort to find the solutions.

Using Landsat-8

We planned to jointly use the Landsat-8 and Sentinel-2 series to have a very fine characterization of the temporal evolutions of the observed surfaces. Unfortunately, we were confronted with geometrical registration differences and, above all, with loss of definition problems due to the resolution difference between Landsat (30 m) and Sentinel-2 (10 and 20 m depending on the bands).

We had to make a choice between quantity of data and geometric precision. Since the results obtained with Sentinel-2 alone are satisfactory, we decided not to use the Landsat-8 data as planned. They were still used to solve another problem described below.

CropMix

CropMix is the small name given by Arthur Vincent and Vincent Thierion to the technique used in iota2 to make the classification of annual crops without reference data for the current year. The approach was first prototyped during Dawa Derksen's graduation internship.

For the prototype products we used the Land Parcel Identification System (LPIS) which provides us with a large number of learning samples. The disadvantage of the LPISs that it is available too late for the annual update of the land cover map. As an example, we do not have the version 2015 so far. So for the 2016 land cover map, we could not rely on the LPIS 2016 or even 2015, and the last available version is that of 2014 (Edit : in November 2017, We are still waiting for the next versions!).

 

So we had to develop a new technique (domain adaptation in machine learning jargon) to use the 2014 LPIS. An additional difficulty is added to the problem because in 2014 there was no Sentinel-2 image, since the satellite was not launched yet. As a result, we used Landsat-8 images from 2014 to transfer learning to Landsat-8 imagery from 2016, and then made a new transfer operation to use this learning on 2016 Sentinel-2 data.

There is still the problem of validation of this result, because the LPISis also useful to validate our classifications. For this task, we have been able to rely on the presence of reference data which, if they were not in sufficient quantity for learning, at least allow independent validation. These are the statistics that accompany the published map. You will see that the results are quite acceptable.

Acknowledgements

Even if we are not done with all the work (we will continue to improve the map and algorithms), it is good not to forget that GeoTiff that we have deposited on a server would not be there without the work of many people.

We must obviously thank Arthur Vincent, engineer at CESBIO on a temporary position, who has been working on iota2 since September 2015. It is he almost alone who built the processing chain that can process tens of terabytes of data with a single command line. But Arthur did not start from scratch, since before him, first Benjamin Tardy and then Marcela Arias, set up the first automatic channel to make land use maps over large areas at CESBIO. Marcela has done a great job of validation and production of the first prototypes released. Still on validation and production, David Morin has helped us for a few months in 2016, and since September, it is Vincent Thierion who among other tasks has also taken in hand this activity, and this, despite a ski accident ...
iota2 is a great success, but it only exists thanks to free software of very good quality which allows us to treat satellite images as if it were something easy. So, a nod to OTB's development team, which for the last few years has taken into account our feature requests and which, with impressive responsiveness, provides us with software tools that make our lives easier. So thank you to Julien, Guillaume, Manuel, Rashad, and many others that I do not forget.

iota2 starts to be used outside CESBIO (eh, yes, it's free software). The feedback from these users, but also our CESBIO colleagues who use it (Claire Marais-Sicre, Camille Truche) also help us to improve things. And if iota2 works well it's also because we use input images that are of very good quality. This quality is achieved by treatments developed at CESBIO by Olivier Hagolle and Mireille Huc, industrialized by CNES and CS-SI in the MAJA processor and integrated into the CNES MUSCATE processing center which makes operational production for Theia. A big thank you to the CNES development and exploitation teams who have suffered to offer us these first productions.

 

And also, at CESBIO, there are colleagues who helped us a lot in the localization and collection of databases (mainly Jean-François Dejoux), in setting up the product distribution interface (Michel Le Page, Philippe Chibaudel, Laurent Drapeau), in providing field data for specific validation (Claire Marais-Sicre, Camille Truche).
Dynafor's colleagues (Donatien Dallery, David Sheeren and Mathieu Fauvel) worked with Vincent Thierion on the post-processing part which will enable us to supply you with high quality vector products. 

We must also pay tribute to the CNES IT teams who have been helping us for over a year to improve the performance of iota2 on the CNES HPC cluster.

The work we do is part of the Center of Scientific Expertise on (operational) Land Use of Theia land data centre. It is clear that our work would have lacked visibility without the existence of Theia and the associated label. It should also be noted that most of the financial resources, including Arthur Vincent's funding, come from the CNES TOSCA program.

And finally, let's emphasize that this work is done to produce maps that will be useful. We have been working with external users for almost the beginning, collecting their needs, offering them prototype products and listening to their always constructive criticisms. These interactions are valuable to us and we hope they will continue so that products continue to improve.

 

Posted under: Applications, CESBIO, In English, Land cover - Occ.des sols, Sentinel-2, THEIA

2 comments

    • Jordi Inglada on 19/02/2018 at 08:00 said:

      Hi,
      The product itself can be cited as:
      Jordi Inglada, Arthur Vincent, & Vincent Thierion. (2017). Theia OSO Land Cover Map 2106 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1048161
      The methodology is described in:
      Inglada, J.; Vincent, A.; Arias, M.; Tardy, B.; Morin, D.; Rodes, I. Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sens. 2017, 9, 95. http://doi.org/10.3390/rs9010095

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