Since machine learning algorithms are hungry for data to process, I contributed to build up various datasets. Some are listed below, other will be added soon.

[2020] MiniFrance Dataset

With Javiera Castillo-Navarro et al., we released the first benchmark for semi-supervised learning in Earth observation: MiniFrance.

[2020] SEN12-FLOOD Dataset

With Clément Rambour et al., we released SEN12-FLOOD, a SAR-Multispectral dataset for classification of flood events in image time-series.

[2019] High-Res. Semantic Change Dataset (HRSCD)

With Rodrigo Daudt et a., we also released HRSCD, a large-scale dataset for semantic change detection at high-resolution (0.5m/pixel). [ HRSCD website @ Rodrigo / HRSCD website @ DataPort ].

[2019] Data Fusion Contest 2019 (DFC2019)

The DFC2019 organised by IADF TC (Myself, Naoto Yokoya and Ronny Hänsch) and Johns Hopkins University (Myron Brown) was a benchmark about Large-Scale Semantic 3D Reconstruction, and involved 3D reconstruction, 3D prediction, and semantic segmentation in 2D and 3D. [ DFC2019 @ IEEE GRSS / DFC2019 @ DataPort ]

[2018] Onera Satellite Change Detection (OSCD) Dataset DOI

image-left

Onera Satellite Change Detection (OSCD) Dataset

With Rodrigo Daudt, we released the first dataset for training deep learning models for pixelwise change detection over Sentinel-2 data. It comprises 24 registered pairs of multispectral images from 2015 and 2018, all over the world. [ OSCD paper @ IGARSS’18 / Prime OSCD website @ Rodrigo / Alternate OSCD website @ DataPort / Related: CNNs for Change Detection / Evaluation @ DASE ]

This dataset contains modified Copernicus data from 2015-2018. Original Copernicus Sentinel Data available from the European Space Agency (https://sentinel.esa.int). Change label maps are released under Creative-Commons BY-NC-SA. If using this dataset, please cite: Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks R. Caye Daudt, B. Le Saux, A. Boulch, and Y. Gousseau IEEE IGARSS Valencia, Spain, July 2018

@inproceedings{daudt2018urban,
  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 Int. Geoscience and Remote Sensing Symposium (IGARSS)},
  address =  {Valencia, Spain},
  month = {July},
  year = {2018},
}

[2013] Christchurch Aerial Semantic Dataset (CASD) DOI

image-left

Christchurch Aerial Semantic Dataset (CASD)

Hicham Randrianarivo and I annotated images from Land Information New Zealand (LINZ) with urban semantic classes: buildings, vehicles and vegetation. Annotations come at object level (shapefiles) and semantic maps (raster masks). All data (images and annotations) are under License CC BY 4.0. [ Report: Cristchurch CASD / Related: Deformable Part Models for remote sensing / DtMM for Vehicle Detection / Segment-before-detect paper ]

If using this dataset, please cite: Man-made structure detection with deformable part-based models H. Randrianarivo, B. Le Saux, and M. Ferecatu IEEE IGARSS Melbourne, Australia, July 2013

@inproceedings{randrianarivo-13igarss-DPM,
author = {Randrianarivo, H. and {Le Saux}, B. and Ferecatu, M.},
title = {Man-made structure detection with deformable part-based models},
booktitle = {IEEE Int. Geoscience and Remote Sensing Symposium (IGARSS)},
year = {2013},
month = {July},
address = {Melbourne, Australia},
}