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.

[2022] 2022 Data Fusion Contest: Semi-supervised Learning for Land Cover Classification

With Javiera Castillo-Navarro, Ronny Haensch and others, we held a competition for semi-supervised learning in Earth observation based on MiniFrance data, in the frame of the IEEE GRSS Data Fusion Contests: the DFC2022. Along with VHR EO imagery and landcover classes, we added digital elevation models to the new MiniFrance-DFC22 data. Full description in the GRSM Paper announcement.

[2022] Hyperview Challenge: Estimating Soil Parameters from Hyperspectral Images

With Jakub Nalepa, Nicolas Longépé, and colleagues from New Space company KP Labs and ESA organised the HyperView challenge for geology and agriculture from space, leveraging hyperspectral imagery (check the video). Hyperview “Seeing beyond the visible” was powered by the ai4eo.eu platform and held as a Grand Challenge at ICIP 2022 (Hyperview description in the ICIP paper).

@INPROCEEDINGS{9897443,
  author={Nalepa, Jakub and {Le Saux}, Bertrand and {Longépé}, Nicolas and Tulczyjew, Lukasz and Myller, Michal and Kawulok, Michal and Smykala, Krzysztof and Gumiela, Michal},
  booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
  title={The Hyperview Challenge: Estimating Soil Parameters from Hyperspectral Images},
  year={2022},
  pages={4268-4272},
  doi={10.1109/ICIP46576.2022.9897443}
}

[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. And the dataset is also available on Radiant Earth platform: MLHub.earth

[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

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 ]

@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},
}

[2018] Data Fusion Contest 2018 (DFC2018)

The DFC2018 organised by IADF TC (Myself, Naoto Yokoya and Ronny Hänsch) and Houston University (Saurabh Prasad) was a benchmark about urban land use and land cover classification (or semantic segmentation). It used multispectral LiDAR point cloud data (intensity rasters and digital surface models), hyperspectral data, and very high-resolution RGB imagery. As such, it still is a relevant becnhmark for hyperspectral classification and data fusion. [ DFC2018 @ IEEE GRSS / DFC2018 @ DataPort ]

[2013] Christchurch Aerial Semantic Dataset (CASD)

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 ]

@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},
}