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iota2 classification

Assuming iota2 is fully installed, this chapter presents the main usage of iota2: the production of a land cover map using satellite images time series.

Introduction to data

iota2 handles several sensors images :

  • Landsat 5 and 8 (old and new THEIA format)

  • Sentinel 1, Sentinel 2 L2A(THEIA and Sen2cor), Sentinel L3A (Theia Format)

  • Various other images already processed, with the userFeat sensor

In this chapter, only the use of Sentinel 2 L2A will be illustrated. To use other sensor, it is necessary to adapt the inputs parameters according to parameters descriptions.

iota2 uses machine learning algorithm to produce land cover map. It requires, among others inputs, images and related reference data”

Get the data-set

Two data-set are available, containing minimal data required to run Iota2 builders:

  • A entire Sentinel 2 tile, with two dates (8.8 Go)

  • An extraction of Sentinel 2 data, with three dates over different eco-climatic region (Soon)

The archive contains:

  • /XXXX/IOTA2_TEST_S2
    archive content

    content of the tutorial archive after content extraction

    • ! external_code
      python code folder

      user custom python code used for external features / feature maps

      • external_code.py
        python code file

        contains user python code used to produce the spectral indices. Rules for creating user code are explained in external features page.

    • ! IOTA2_Outputs
      output folder

      folder used for iota2 output folders

      • (empty)

    • sensor_data
      input raster data

      the directory which contains Sentinel-2 data. These data must be stored by tiles as in the archive.

      • T31TCJ
        • ! SENTINEL2A_20180511-105804-037_L2A_T31TCJ_D_V1-7
          • MASKS

            • SENTINEL2A_20180511-105804-037_L2A_T31TCJ_D_V1-7_*.tif

          • SENTINEL2A_20180511-105804-037_L2A_T31TCJ_D_V1-7_FRE_B*.tif

          • SENTINEL2A_20180511-105804-037_L2A_T31TCJ_D_V1-7_FRE_STACK.tif

        • ! SENTINEL2A_20180521-105702-711_L2A_T31TCJ_D_V1-7
          • MASKS

            • SENTINEL2A_20180521-105702-711_L2A_T31TCJ_D_V1-7_*.tif

          • SENTINEL2A_20180521-105702-711_L2A_T31TCJ_D_V1-7_FRE_B*.tif

          • SENTINEL2A_20180521-105702-711_L2A_T31TCJ_D_V1-7_FRE_STACK.tif

      • T31TDJ
        • ! SENTINEL2A_20180511-105804-037_L2A_T31TDJ_D_V1-7
          • MASKS

            • SENTINEL2A_20180511-105804-037_L2A_T31TDJ_D_V1-7_*.tif

          • SENTINEL2A_20180511-105804-037_L2A_T31TDJ_D_V1-7_ATB_R1.tif

          • SENTINEL2A_20180511-105804-037_L2A_T31TDJ_D_V1-7_FRE_B*.tif

          • SENTINEL2A_20180511-105804-037_L2A_T31TDJ_D_V1-7_FRE_STACK.tif

          • SENTINEL2A_20180511-105804-037_L2A_T31TDJ_D_V1-7_MTD_ALL.xml

          • SENTINEL2A_20180511-105804-037_L2A_T31TDJ_D_V1-7_QKL_ALL.jpg

        • ! SENTINEL2A_20180521-105702-711_L2A_T31TDJ_D_V1-7
          • MASKS

            • SENTINEL2A_20180521-105702-711_L2A_T31TDJ_D_V1-7_*.tif

          • SENTINEL2A_20180521-105702-711_L2A_T31TDJ_D_V1-7_ATB_R1.tif

          • SENTINEL2A_20180521-105702-711_L2A_T31TDJ_D_V1-7_FRE_B*.tif

          • SENTINEL2A_20180521-105702-711_L2A_T31TDJ_D_V1-7_FRE_STACK.tif

          • SENTINEL2A_20180521-105702-711_L2A_T31TDJ_D_V1-7_MTD_ALL.xml

          • SENTINEL2A_20180521-105702-711_L2A_T31TDJ_D_V1-7_QKL_ALL.jpg

    • vector_data
      input vector data

      directory containing input vector data

      • reference_data.cpg

      • reference_data.dbf

      • reference_data.prj

      • reference_data.shp
        shapefile

        the shapeFile containing geo-referenced and labelled polygons (no multi-polygons, no overlapping) used to train a classifier.

      • reference_data.shx

      • EcoRegion.dbf

      • EcoRegion.prj

      • EcoRegion.qpj

      • EcoRegion.shp
        shapefile

        shapeFile containing two geo-referenced polygons representing a spatial stratification (eco-climatic areas, for instance).

      • EcoRegion.shx

    • colorFile.txt
      color table

      colors used in classification map

      $ cat colorFile.txt
...
211 255 85 0
...

      Here the class 211 has the RGB color 255 85 0.

    • IOTA2_Example.cfg
      example config file

      the file used to set iota2’s parameters such as inputs/outputs paths, classifier parameters etc.

    • i2_tutorial_classification.cfg
      sample config file

      sample config file for classification builder

    • i2_tutorial_features_map.cfg
      sample config file

      sample config file for feature map builder

    • i2_tutorial_obia.cfg
      sample config file

      sample config file for object base image analysis

    • nomenclature23.txt
      nomenclature file

      label’s name. The purpose of the file is to get a pretty results report at the end of the chain by relabeling integers labels by a more verbose type.

      & cat nomenclature.txt
...
prairie:211
...

      Here the class 211 corresponds to the class prairie

    • vecteur_23.qml

Warning

Each class must be represented in colorFile.txt and nomenclature.txt

Understand the configuration file

iota2 is configured through several of parameters, some of them are specfic to iota2 and some belong to other libraries such as scikit-learn or OTB.

These parameters allow to select the operations to be carried out and their various parameters. A documentation of all these parameters is provided here. The user defines these paramereters in a configuration file (a human readable text file) that is read by iota2 at upon start. The file is structured into sections, each section containing several fields.

To simplify the use, iota2 read a configuration file which is a simple text file containing sections and fields. A section (or group of fields) contains fields with similar purposes, for instance the section chain contain general information such as input data, and the output path and the section arg_train will contains informations about the classifier’s parameters.

The minimal configuration file contains all required fields to produce a land cover map.

chain :
{
  output_path : '/XXXX/IOTA2_TEST_S2/IOTA2_Outputs/Results'
  remove_output_path : True
  nomenclature_path : '/XXXX/IOTA2_TEST_S2/nomenclature.txt'
  list_tile : 'T31TCJ'
  s2_path : '/XXXX/IOTA2_TEST_S2/sensor_data'
  ground_truth : '/XXXX/IOTA2_TEST_S2/vector_data/groundTruth.shp'
  data_field : 'code'
  spatial_resolution : 10
  color_table : '/XXXX/IOTA2_TEST_S2/colorFile.txt'
  proj : 'EPSG:2154'
  first_step:"init"
  last_step: "validation"
}
arg_train :
{
  classifier : 'rf'
  otb_classifier_options : {'classifier.rf.min': 5,'classifier.rf.max': 25 }
}
arg_classification :
{
  classif_mode : 'separate'
}

task_retry_limits:
{
    allowed_retry : 0
    maximum_ram : 60.0
    maximum_cpu : 12
}

For an end user, launching iota2 requires to fill correctly the configuration file.

In the above example, replace the XXXX by the path where the archive has been extracted.

Running the chain

iota2 launch

The chain is launched with the following command line.

Iota2.py -config /XXXX/IOTA2_TESTS_DATA/config_tuto_classif.cfg -scheduler_type localCluster

First, the chain displays the list of all steps activated by the configuration file

Once the processing start, a large amount of information will be printed, most of them concerning the dask-scheduler.

Did it all go well?

iota2 is packed with a logging system. Each step has its has its own log folder, available in the output_path/logs directory (see logs in Output tree structure) In these directories two kind of log can be found *_out.log and *_err.log. The error are compiled in “err” file and the standard output in “out” file. With the scheduler dask, iota2 go as far as possible while the data required for the next steps is available. To simplify the error identification, an interactive graph is produced in a html page. To open it, open the index.html file in html folder. Nodes in graph can have three colors (red: error, blue: done, orange: not yielded). By clicking on graph node, the corresponding log file is openned.

If despite all this information, the errors can not be identifiyed or solved, the iota2 team can help all users. The simplest way to ask help is to create an issue on framagit by adding the archive available in log directory.

Output tree structure

In this section, the iota2 outputs available after a proper run are described.

  • /XXXX/IOTA2_TEST_S2/IOTA2_Outputs/Results
    output folder

    output folder defined in config file output_path

    • ! classif
      per tile classification maps
      Contains classification maps, for each tile and each region. They will be merged in the final directory.

      • Classif_T31TCJ_model_1_seed_0.tif

      • ! MASK

        • MASK_region_1_T31TCJ.tif

      • T31TCJ_model_1_confidence_seed_0.tif

      • tmpClassif

    • ! config_model

      empty

      • (empty)

    • ! dataAppVal
      desc
      Shapefiles obtained after spliting reference data between learning and validation set according a ratio.
      • ! bymodels

        • (empty)

      • T31TCJ_seed_0_learn.sqlite

      • T31TCJ_seed_0_val.sqlite

    • ! dataRegion
      vector data split by region
      When using eco-climatic region, contains the vector data split by region.

      • (empty)

    • ! dimRed
      desc
      Contains features after dimensionality reduction. Empty if not activated.

      • (empty)

    • ! envelope
      shapefiles
      Contains shapefiles, one for each tile.
      Used to ensure tile priority, with no overlap.

      • T31TCJ.dbf

      • T31TCJ.prj

      • T31TCJ.shp

      • T31TCJ.shx

    • ! features
      useful information
      for each tile, contains useful information
      • T31TCJ
        • ! tmp
          temporary folder
          folder created temporarily during the chain execution

          • MaskCommunSL.dbf

          • MaskCommunSL.prj

          • MaskCommunSL.shp
            common scene
            the common scene of all sensors for this tile.

          • MaskCommunSL.shx

          • MaskCommunSL.tif

          • Sentinel2L3A_T31TCJ_reference.tif
            reference image
            the image, generated by iota2, used for reprojecting data
          • Sentinel2L3A_T31TCJ_input_dates.txt
            list of dates
            the list of date detected in s2_path for the current tile.

          • Sentinel2_T31TCJ_interpolation_dates.txt

        • CloudThreshold_0.dbf

        • CloudThreshold_0.prj

        • CloudThreshold_0.shp
          database used as mask
          This database is used to mask training polygons according to a number of clear date. See cloud_threshold parameter

        • CloudThreshold_0.shx

        • nbView.tif
          number visits
          number of time a pixel is seen in the whole time series (i.e., excluding clouds, shadows, staturation and no-data)
    • final
      final producs
      This folder contains the final products of iota2.
      All final products will be generated in the final directory
      see Final products for details
      • ! simplification

        • mosaic

        • tiles

        • tmp

        • vectors

      • ! TMP

        • ClassificationResults_seed_0.txt

        • Classif_Seed_0.csv

        • T31TCJ_Cloud.tif

        • T31TCJ_GlobalConfidence_seed_0.tif

        • T31TCJ_seed_0_CompRef.tif

        • T31TCJ_seed_0.csv

        • T31TCJ_seed_0.tif

      • Classif_Seed_0_ColorIndexed.tif

      • Classif_Seed_0.tif

      • Confidence_Seed_0.tif

      • Confusion_Matrix_Classif_Seed_0.png

      • diff_seed_0.tif

      • PixelsValidity.tif

      • RESULTS.txt

      • vectors

    • ! formattingVectors
      learning samples
      The learning samples contained in each tiles.
      Shapefiles in which pixel values from time series have been extracted.
      • ! T31TCJ
        temporary directory
        This is a temporary working directory, intermediate files are (re)moved after step completion.

        • (empty)

      • T31TCJ.cpg

      • T31TCJ.dbf

      • T31TCJ.prj

      • T31TCJ.shp

      • T31TCJ.shx

    • ! learningSamples
      learning samples
      Sqlite file containing learning samples by regions.
      Also contains a CSV file containing statistics about samples balance for each seed. See tracing back samples to generate this file manually.

      • class_statistics_seed0_learn.csv

      • Samples_region_1_seed0_learn.sqlite

      • T31TCJ_region_1_seed0_Samples_learn.sqlite

    • ! logs
      logs
      output logs of iota2. See Did it all go well? section for details
      • classification

        • classification_T31TCJ_model_1_seed_0.err

        • classification_T31TCJ_model_1_seed_0.out

      • CommonMasks

        • common_mask_T31TCJ.err

        • common_mask_T31TCJ.out

      • confusionCmd

        • confusion_T31TCJ_seed_0.err

        • confusion_T31TCJ_seed_0.out

      • confusionsMerge

        • merge_confusions.err

        • merge_confusions.out

      • Envelope

        • tiles_envelopes.err

        • tiles_envelopes.out

      • genRegionVector

        • region_generation.err

        • region_generation.out

      • ! html

        • configuration_file.html

        • environment_info.html

        • genindex.html

        • index.html

        • input_files_content.html

        • objects.inv

        • output_path_content.html

        • s2_path_content.html

        • search.html

        • searchindex.js

        • source

          • classification_T31TCJ_model_1_seed_0.out

          • common_mask_T31TCJ.out

          • configuration_file.rst

          • confusion_T31TCJ_seed_0.out

          • environment_info.rst

          • extraction_T31TCJ.out

          • final_report.out

          • index.rst

          • input_files_content.rst

          • learning_model_1_seed_0.out

          • merge_confusions.out

          • merge_model_1_seed_0_usually.out

          • merge_samples_T31TCJ.out

          • mosaic.out

          • output_path_content.rst

          • preprocessing_T31TCJ.out

          • region_generation.out

          • s2_path_content.rst

          • s_sel_model_1_seed_0.out

          • stats_1_S_0_T_T31TCJ.out

          • tasks_status_1.rst

          • tasks_status_2.rst

          • tiles_envelopes.out

          • validity_raster_T31TCJ.out

          • vector_form_T31TCJ.out

        • _sources

          • configuration_file.rst.txt

          • environment_info.rst.txt

          • index.rst.txt

          • input_files_content.rst.txt

          • output_path_content.rst.txt

          • s2_path_content.rst.txt

          • tasks_status_1.rst.txt

          • tasks_status_2.rst.txt

        • _static

          • basic.css

          • css

            • badge_only.css

            • fonts

              • fontawesome-webfont.eot

              • fontawesome-webfont.svg

              • fontawesome-webfont.ttf

              • fontawesome-webfont.woff

              • fontawesome-webfont.woff2

              • lato-bold-italic.woff

              • lato-bold-italic.woff2

              • lato-bold.woff

              • lato-bold.woff2

              • lato-normal-italic.woff

              • lato-normal-italic.woff2

              • lato-normal.woff

              • lato-normal.woff2

              • Roboto-Slab-Bold.woff

              • Roboto-Slab-Bold.woff2

              • Roboto-Slab-Regular.woff

              • Roboto-Slab-Regular.woff2

            • theme.css

          • doctools.js

          • documentation_options.js

          • file.png

          • jquery-3.5.1.js

          • jquery.js

          • js

            • badge_only.js

            • html5shiv.min.js

            • html5shiv-printshiv.min.js

            • theme.js

          • language_data.js

          • minus.png

          • plus.png

          • pygments.css

          • searchtools.js

          • underscore-1.3.1.js

          • underscore.js

        • tasks_status_1.html

          • tasks_status_2.html

      • learnModel

        • learning_model_1_seed_0.err

        • learning_model_1_seed_0.out

      • mosaic

        • mosaic.err

        • mosaic.out

      • PixelValidity

        • validity_raster_T31TCJ.err

        • validity_raster_T31TCJ.out

      • reportGeneration

        • final_report.err

        • final_report.out

      • samplesByModels

        • merge_model_1_seed_0_usually.err

        • merge_model_1_seed_0_usually.out

      • samplesByTiles

        • merge_samples_T31TCJ.err

        • merge_samples_T31TCJ.out

      • samplesExtraction

        • extraction_T31TCJ.err

        • extraction_T31TCJ.out

      • samplesMerge

        • merge_model_1_seed_0.err

        • merge_model_1_seed_0.out

      • samplingLearningPolygons

        • s_sel_model_1_seed_0.err

        • s_sel_model_1_seed_0.out

      • sensorsPreprocess

        • preprocessing_T31TCJ.err

        • preprocessing_T31TCJ.out

      • statsSamplesModel

        • stats_1_S_0_T_T31TCJ.err

        • stats_1_S_0_T_T31TCJ.out

      • tasks_status_1.svg

      • tasks_status_2.svg

      • VectorFormatting

        • vector_form_T31TCJ.err

        • vector_form_T31TCJ.out

    • ! model
      desc
      The learned models

      • model_1_seed_0.txt

    • ! samplesSelection
      shapefiles
      Shapefiles containing points (or pixels coordinates) selected for training stage.
      Also contains a CSV summary of the actual number of samples per class

      • samples_region_1_seed_0.dbf

      • samples_region_1_seed_0_outrates.csv

      • samples_region_1_seed_0.prj

      • samples_region_1_seed_0_selection.sqlite

      • samples_region_1_seed_0.shp

      • samples_region_1_seed_0.shx

      • samples_region_1_seed_0.xml

      • T31TCJ_region_1_seed_0_stats.xml

      • T31TCJ_samples_region_1_seed_0_selection.sqlite

      • T31TCJ_selection_merge.sqlite

    • ! shapeRegion
      desc
      Shapefiles indicating intersection between tiles and region.

      • MyRegion_region_1_T31TCJ.dbf

      • MyRegion_region_1_T31TCJ.prj

      • MyRegion_region_1_T31TCJ.shp

      • MyRegion_region_1_T31TCJ.shx

      • MyRegion_region_1_T31TCJ.tif

    • ! stats
      statistics
      Optional xml statistics to standardize the data before learning (svm…).

      • (empty)

    • IOTA2_tasks_status.txt
      internal execution status
      iota2 keeps track of it’s execution using this pickle file (not text) to be allowed to restart from the state where it stopped.
    • logs.zip

      logs archive

    • MyRegion.dbf

    • MyRegion.prj

    • MyRegion.shp
      fake region
      When no ecoclimatic region is defined for learning step, iota2 creates this fake file with a single region.

    • MyRegion.shx

    • reference_data.dbf

    • reference_data.prj

    • reference_data.shp
      reencoded shapefile
      As OTB expects classes to be encoded as consecutive integers, which is not necessarily the case of user labels, this shapefile contains user data with reencoded labels.

    • reference_data.shx

Final products

All final products will be generated in the final directory

Land cover map

Your Classif_Seed_0_ColorIndexed.tif should look like this one:

classification map

Classif_Seed_0.tif Example

This map contains labels from the shapeFile groundTruth.shp. As you can see the classification’s quality is rather low. A possible explanation is the low number of dates used to produce it. A raster called PixelsValidity.tif gives the number of dates for which the pixel is clear (no cloud, cloud shadow, saturation)

validity map

PixelsValidity.tif Example

As only two dates are used to produce the classification map, pixels are in the [0; 2] range. iota2 also provides a confidence map: Confidence_Seed_0.tif which allows to better understand the resulting classification. This map gives for each pixel a scale between O and 100, where 0 and 100 meant that the probability membership provided by the classifier is 0 and 1, respectively. This is not a validation, just an estimate of the confidence in the decision of the classifier.

confidence map

Confidence_Seed_0.tif Example

These three maps form iota2’s main outputs: they are the minimum outputs required to analyse and understand the results.

We analyzed and produced classifications thanks to iota2. The main objective is to get the better land cover map as possible. There are many ways to achieve this purpose: researchers publish every day new methods.

The simplest method to get better results can consist in using a longer time series, improving the reference data for training, etc.

Measuring quality

confusion matrix

Confusion_Matrix_Classif_Seed_0.png Example

Confusion matrices allow us to measure the quality of a classification. In the one provided by iota2 (in the final output directory), the pixels whose labels are known (the reference) are in rows and the inferred pixels are in columns.

To go further