The Dataset

We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite.

Instructions: 

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The MiniFrance Suite

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Authors:

Javiera Castillo Navarro, javiera.castillo_navarro@onera.fr

Bertrand Le Saux, bls@ieee.org

Alexandre Boulch, alexandre.boulch@valeo.com

Nicolas Audebert, nicolas.audebert@cnam.fr

Sébastien Lefèvre, sebastien.lefevre@irisa.fr

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About:

This dataset contains very high resolution RGB aerial images over 16 cities and their surroundings from different regions in France, obtained from IGN's BD ORTHO database (images from 2012 to 2014). Pixel-level land use and land cover annotations are provided, generated by rasterizing Urban Atlas 2012.

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This dataset is partitioned in three parts, defined by conurbations:

1. Labeled training data: data over Nice and Nantes/Saint Nazaire.

2. Unlabeled training data: data over Le Mans, Brest, Lorient, Caen, Calais/Dunkerque and Saint-Brieuc.

3. Test data: data over Marseille/Martigues, Rennes, Angers, Quimper, Vannes, Clermont-Ferrand, Cherbourg, Lille.

Due to the large-scale nature of the dataset, it is divided in several files to download:

- Images for the labeled training partition: contains RGB aerial images for french departments in the labeled training partition.

- Images for the unlabeled training partition (parts 1, 2 and 3): contain RGB aerial images for french departments in the unlabeled training partition.

- Images for the test partition (parts 1, 2, 3 and 4): contain RGB aerial images for french departments in the partition reserved for evaluation.

- Labels for the labeled partition

- Lists of files by conurbation and partition: contain .txt files that list all images included by city.

Land use maps are available for all images in the labeled training partition of the dataset. We consider here Urban Atlas classes at the second hierarchical level. Available classes are:

- 0: No information

- 1: Urban fabric

- 2: Industrial, commercial, public, military, private and transport units

- 3: Mine, dump and contruction sites

- 4: Artificial non-agricultural vegetated areas

- 5: Arable land (annual crops)

- 6: Permanent crops

- 7: Pastures

- 8: Complex and mixed cultivation patterns

- 9: Orchards at the fringe of urban classes

- 10: Forests

- 11: Herbaceous vegetation associations

- 12: Open spaces with little or no vegetation

- 13: Wetlands

- 14: Water

- 15: Clouds and shadows

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Citation: If you use this dataset for your work, please use the following citation:

@article{castillo2020minifrance,
title={{Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study}},
author={Castillo-Navarro, Javiera and Audebert, Nicolas and Boulch, Alexandre and {Le Saux}, Bertrand and Lef{\`e}vre, S{\'e}bastien},
journal={Under review.},
year={2020}
}

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Copyright:

The images in this dataset are released under IGN's "licence ouverte". More information can be found at http://www.ign.fr/institut/activites/lign-lopen-data

The maps used to generate the labels in this dataset come from the Copernicus program, and as such are subject to the terms described here: https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012?tab=metadata

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The Dataset

The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.

Instructions: 

Onera Satellite Change Detection dataset

 

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Authors: Rodrigo Caye Daudt, rodrigo.daudt@onera.fr

Bertrand Le Saux, bls@ieee.org

Alexandre Boulch, alexandre.boulch@valeo.ai

Yann Gousseau, yann.gousseau@telecom-paristech.fr

 

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About: This dataset contains registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites of the Copernicus program. Pixel-level urban change groundtruth is provided. In case of discrepancies in image size, the older images with resolution of 10m per pixel is used. Images vary in spatial resolution between 10m, 20m and 60m. For more information, please refer to Sentinel-2 documentation.

 

For each location, folders imgs_1_rect and imgs_2_rect contain the same images as imgs_1 and imgs_2 resampled at 10m resolution and cropped accordingly for ease of use. The proposed split into train and test images is contained in the train.txt and test.txt files.

For downloading and cropping the images, the Medusa toolbox was used: https://github.com/aboulch/medusa_tb

For precise registration of the images, the GeFolki toolbox was used. https://github.com/aplyer/gefolki

 

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Labels: The train labels are available in two formats, a .png visualization image and a .tif label image. In the png image, 0 means no change and 255 means change. In the tif image, 0 means no change and 1 means change.

<ROOT_DIR>//cm/ contains: - cm.png - -cm.tif

Please note that prediction images should be formated as the -cm.tif rasters for upload and evaluation on DASE (http://dase.grss-ieee.org/).

(Update June 2020) Alternatively, you can use the test labels which are now provided in a separate archive, and compute standard metrics using the python notebook provided in this repo, along with a fulll script to train and classify fully-convolutional networks for change detection: https://github.com/rcdaudt/fully_convolutional_change_detection 

 

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Citation: If you use this dataset for your work, please use the following citation:

@inproceedings{daudt-igarss18,

author = {{Caye Daudt}, R. and {Le Saux}, B. and Boulch, A. and Gousseau, Y.},

title = {Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks},

booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS'2018)},

venue = {Valencia, Spain},

month = {July},

year = {2018},

}

 

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Copyright: Sentinel Images: This dataset contains modified Copernicus data from 2015-2018. Original Copernicus Sentinel Data available from the European Space Agency (https://sentinel.esa.int).

Change labels: Change maps are released under Creative-Commons BY-NC-SA. For commercial purposes, please contact the authors.

 

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This database is a image set of a strongest glint-affected region of inland water Capivara reservoir, Brazil. We carried out a flight survey in September 2016 on the confluence region of the Tibagi and Paranapanema Rivers. We use the hyperspectral camera manufactured by Rikola, model FPI2014, wich collect 25 spectral bands at following intervals and full widths at half maximum (FWHM), both expressed in nanometers (nm):

 

Instructions: 

Each folder have specific resources generated on the processing steps. The generated resources, step by step, are:

 

1-roi_target.zip: ROI and river target shapefiles to delimit the process; 

2-roitarget.zip: intersection of the ROI and river target;

3-imgs_roi.zip: Images clipped by the ROI target;

4-virtual_bands_None.zip: Virtual bandset generated using GDAL;

5ra-pixel_refs.zip: CSV file of mode values of each image band;

5rb-img_ref_fast_None.zip: Multscale image references generated by the author method proposes;

5rc-img_ref_gaussianmedian_None.zip: Local image references generated with Gaussian filter;

6ra-mosaics_refmodaNone.zip: Global reference mosaic;

6rb-mosaics_refDQNone.zip: Multiscale reference mosaic;

6rc-mosaics_ref_gaussianNone.zip: Local reference mosaic;

6sa-mosaic_first_None.zip: First value mosaic

6sb-mosaic_last_None.zip: Last value mosaic;

6sc-mosaic_mean_None.zip: Mean value mosaic;

6sd-mosaic_median_None.zip: Median value mosaic;

6se-mosaic_maximum_None.zip: Maximum value mosaic;

6sf-mosaic_minimum_None.zip: Minimum value mosaic;

 

Details and descriptions about the full process steps are in the oficial paper (under journal review).

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Emergency  managers  of  today  grapple  with  post-hurricane damage assessment that is often labor-intensive, slow,costly,   and   error-prone.   As   an   important   first   step   towards addressing  the   challenge,   this   paper   presents   the   development of  benchmark  datasets  to  enable  the  automatic  detection  ofdamaged buildings from post-hurricane remote sensing imagerytaken  from  both  airborne  and  satellite  sensors.  Our  work  has two  major  contributions:  (1)  we  propose  a  scalable  framework to  create  benchmark  datasets  of  hurricane-damaged  buildings

Instructions: 

Data can be used for object detection algorithms to properly annotate post disaster buildings as either damaged or non-damaged, aiding disaster response. This dataset contains ESRI Shapefiles of bounding boxes of buildings labeled as either damaged or non-damaged. Those labeled as damaged also have four degrees of damage from minor to catastrophic. Importantly, each bounding box is also indexed to one of the images in the NOAA post-Hurricane Harvey imagery dataset allowing users to match the bounding boxes with the correct imagery for training the algorithm. 

To make the NOAA imagery more manageable, images were processed and tiled into smaller 2048x2048 pixel ones. To obtain the same images please follow the steps below:

  1. Download the images from the NOAA page

  2. Tile the images using the tileTiff.py script (make sure the size is set to 2048 x 2048). All tiles will be in a subdirectory named “1”.

  3. This then creates the tiles that correspond to the image indexed in the shape files.

Important note: Not all bounding boxes in the shape file will map to an image. One will have to filter out bounding boxes which do not map to the images before feeding the data into any model.

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