Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Millet vegetation on path-loss between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for millet crop monitoring from period 03/06/2020 to 04/10/2020.

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This dataset consists of orthorectified aerial photographs, LiDAR derived digital elevation models and segmentation maps with 10 classes, acquired through the open data program of the German state North Rhine-Westphalia (https://www.opengeodata.nrw.de/produkte/) and refined with OpenStreeMap. Please check the license information (http://www.govdata.de/dl-de/by-2-0).

Instructions: 

Dataset description

The data was mostly acquired over urban areas in North-Rhine Westphalia, Germany. Since the acquisition dates for the aerial photographs and LiDAR do not match exactly, there can be discrepancies in what they show and in which season, e.g., trees change their leaves or lose them in autumn. In our experience, these differences are not drastic but should be kept in mind.

We have included two Python scripts. plot_examples.py creates the example image used on this website. calc_and_plot_stats.py calculates and plots the class statistics. Furthermore, we published the code to create the dataset at https://github.com/gbaier/geonrw, which makes it easy to extend the dataset with other areas in North-Rhine Westphalia. The repository also contains a PyTorch data loader.

This multimodal dataset should be useful for a variety of tasks. Image segmentation using multiple inputs, height estimation from the aerial photographs, or semantic image synthesis.

Organization

Similar to the original source of the data (https://www.opengeodata.nrw.de/produkte/geobasis/lbi/dop/dop_jp2_f10_paketiert/), we organize all samples by the city they were acquired over. Their filenames, e.g., 345_5668_rgb.jp2 consists of the UTM zone 32N coordinates and the datatype (RGB, DEM or seg for land cover).

File formats

All data is geocoded and can be opened using QGIS (https://www.qgis.org/). The aerial photographs are stored as JPEG2000 files, the land cover maps and digital elevation models both as GeoTIFFs. The accompanying scripts show how to read the data into Python.

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The simulated InSAR building dataset contains 312 simulated SAR image pairs generated from 39 different building models. Each building model is simulated at 8 viewing-angles. The sample number is 216 of the train set and is 96 of the test set. Each simulated InSAR sample contains three channels: master SAR image, slave SAR image, and interferometric phase image. This dataset serves the CVCMFF Net for building semantic segmentation of InSAR images.

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The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at-sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, we present a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks.

Instructions: 

This repository contains the AURORA dataset, a multi sensor dataset for robotic ocean exploration.

It is accompanied by the report "AURORA, A multi sensor dataset for robotic ocean exploration", by Marco Bernardi, Brett Hosking, Chiara Petrioli, Brian J. Bett, Daniel Jones, Veerle Huvenne, Rachel Marlow, Maaten Furlong, Steve McPhail and Andrea Munafo.

Exemplar python code is provided at https://github.com/noc-mars/aurora.

 

The dataset provided in this repository includes data collected during cruise James Cook 125 (JC125) of the National Oceanography Centre, using the Autonomous Underwater Vehicle Autosub 6000. It is composed of two AUV missions: M86 and M86.

  • M86 contains a sample of multi-beam echosounder data in .all format. It also contains CTD and navigation data in .csv format.

  • M87 contains a sample of the camera and side-scan sonar data. The camera data contains 8 of 45320 images of the original dataset. The camera data are provided in .raw format (pixels are ordered in Bayer format). The size of each image is of size 2448x2048. The side-scan sonar folder contains a one ping sample of side-scan data provided in .xtf format.

  • The AUV navigation file is provided as part of the data available in each mission in .csv form.

 

The dataset is approximately 200GB in size. A smaller sample is provided at https://github.com/noc-mars/aurora_dataset_sample and contains a sample of about 200MB.

Each individual group of data (CTD, multibeam, side scan sonar, vertical camera) for each mission (M86, M87) is also available to be downloaded as a separate file. 

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The files here support the analysis presented in the paper in IEEE Transactions on Geoscience and Remote Sensing, "Snow Property Inversion from Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach with Examples from MODIS and Landsat 8 OLI" Spectral mixture analysis has a history in mapping snow, especially where mixed pixels prevail. Using multiple spectral bands rather than band ratios or band indices, retrievals of snow properties that affect its albedo lead to more accurate estimates than widely used age-based models of albedo evolution.

Instructions: 

These HDF5 files contain snow cover over the Sierra Nevada USA from water year 2001-2019 using the Snow Property Inversion from Remote Sensing (SPIRES) approach. Each file covers one water year (October through September). They are stored with block compression so individual days can be read without reading the whole file. The method is described by E.H. Bair, T. Stillinger, and J. Dozier, "Snow Property Inversion from Remote Sensing (SPIReS): A generalized multispectral unmixing approach with examples from MODIS and Landsat 8 OLI," IEEE Trans. Geosci. Remote Sens., 2020 (manuscript number TGRS-2020-02003). Source code is at https://github.com/edwardbair/SPIRES The projection is the Albers equaconic (also called the California Teale projection) with WGS84 datum and 500 m square pixels. The standard meridian for the projection is 120 W; the standard parallels are 34 N and 40.5 N; False Northing is -40,000,000. The h5 files can be read with several software packages. We use MATLAB. They contain: MATLAB date numbers, ISO dates in format YYYYDDD, geographic information, spacetime cubes of snow fraction, raw (unadjusted) snow fraction, grain size (um), and dust (ppmw). The spacetime cubes have a slice for each day, begin on October 1 and end on September 30.

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These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover.

Instructions: 

The dataset is composed of 336 sequences corresponding to areas in West and South-East Africa, Middle-East, and Australia. Each time series is located in a given folder named with the sequence ID (0001... 0336).

Two json files, S1list.json and S2list.json are provided to describe respectively the Sentinel-1 and Sentinel-2 images.The keys are the total number of images in the sequence, the folder name, the geography of the observed area, and the description of each image in the series. The SAR images description contains also the URLs to download the images.Each image is described by its acquisition date, its label (FLOODING: boolean), a boolean (FULL-DATA-COVERAGE: boolean) indicating if the area is fully or partially imaged, and the file prefix. For SAR images the orbit (ASCENDING or DESCENDING) is also indicated.

The Sentinel-2 images were obtained from the Mediaeval 2019 Multimedia Satellite Task [1] and are provided with Level 2A atmospheric correction. For one acquisition, there are 12 single-channel raster images provided corresponding to the different spectral bands.

The Sentinel-1 images were added to the dataset. The images are provided with radiometric calibration and range doppler terrain correction based on the SRTM digital elevation model. For one acquisition, two raster images are available corresponding to the polarimetry channels VV and VH.

The original dataset was split into 269 sequences for the train and 68 sequences for the test. Here all sequences are in the same folder.

 

To use this dataset please cite the following papers:

Flood Detection in Time Series of Optical and SAR Images, C. Rambour,N. Audebert,E. Koeniguer,B. Le Saux,  and M. Datcu, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 1343--1346

The Multimedia Satellite Task at MediaEval2019, Bischke, B., Helber, P., Schulze, C., Srinivasan, V., Dengel, A.,Borth, D., 2019, In Proc. of the MediaEval 2019 Workshop

 

This dataset contains modified Copernicus Sentinel data [2018-2019], processed by ESA.

[1] The Multimedia Satellite Task at MediaEval2019, Bischke, B., Helber, P., Schulze, C., Srinivasan, V., Dengel, A.,Borth, D., 2019, In Proc. of the MediaEval 2019 Workshop

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The dataset contains two sets of planetary models used in the Reproducibility Challenge Student Cluster Competition at the SC19 conference. During this challenge the competitors reproduced parts of the SC18 paper: "Computing planetary interior normal modes with a highly parallel polynomial filtering eigensolver." by Shi, Jia, et al. (https://doi.org/10.1109/SC.2018.00074)

 

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This multispectral remote sensing image data contained pixels of size (1024 x 1024) for the region around Kolkata city in India and was obtained with LISS-III sensor. There are four spectral bands, i.e., two from visible spectrum (green and red) and two from the infrared spectrum (near-infrared and shortwave infrared). The spatial resolution and spectral variation over the wavelength are 23.5m and 0.52 - 1.7 μm, respectively.

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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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