Photo Open Access

Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land use management and biodiversity conservation in semi-arid savanna under regional and global change (SAVMAP)

Reinhard, Friedrich; Parkan, Matthew; Produit, Timothée; Betschart, Sonja; Bacchilega, Beatrice; Hauptfleisch, Morgan L.; Meier, Patrick; SAVMAP, Consortium; Joost, Stéphane

To prevent aggravation of existing poverty in semi-arid savannas, a comprehensive concept for the sustainable adaptive management and use of these ecosystems under unprecedented conditions is needed. SAVMAP is an innovative, trans-, and inter-disciplinary initiative whose goal is to develop a valuable monitoring tool for both sustainable land-use management and rare species conservation (black rhinoceros) in semi-arid savanna in Namibia. SAVMAP uses near real-time ultrahigh-resolution photographic imaging (NURI) facilitated by unmanned aerial vehicles (UAVs) designed at EPFL.

The dataset proposed here was acquired in May 2014 and contains: -Raw aerial images (non-rectified) in JPEG format. Additional metadata about individual images (timestamp, latitude, longitude, altitude, etc) can be extracted from the EXIF. Each image is named with a Universally Unique Identifier. -Polygons indicating the locations of animals tagged during the Micromappers crowdsourcing campaign (please be aware that the polygons contain many false positives and should not be directly used as a "ground truth"). The coordinates of the polygons use the image reference system (i.e. column and row number). Each polygon has a Universally Unique Identifier (TAGUUID) and an associated image (IMAGEUUID). Information about the animal species in each polygon is currently not available. The polygons are provided in the ESRI shapefile and GEOJSON formats.
Files (3.5 GB)
Name Size
readme.txt
md5:4abf4636b87b486e0adcfe00ec6975b4 		860 Bytes Download
savmap_dataset_v2.zip
md5:f87bd2ace593ec742fb03fd91975d566 		3.5 GB Download

  • Ofli, F., Meier, P., Imran, M., Castillo, C., Tuia, D., Rey, N., Briant, J., Millet, P., Reinhard, F., Parkan, M., 2016. Combining human computing and machine learning to make sense of big (aerial) data for disaster response. Big data 4, 47–59.

  • Rey, N., Volpi, M., Joost, S., Tuia, D., 2017. Detecting animals in African Savanna with UAVs and the crowds. Remote Sensing of Environment 200, 341–351. https://doi.org/10.1016/j.rse.2017.08.026

1,941
726
views
downloads
All versions This version
Views 1,9411,434
Downloads 726627
Data volume 1.6 TB1.6 TB
Unique views 1,6351,283
Unique downloads 435373
More info on how stats are collected.
Indexed in
Publication date:
March 29, 2015
DOI:
10.5281/zenodo.1204408

Zenodo DOI Badge

DOI 

10.5281/zenodo.1204408

Markdown 

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1204408.svg)](https://doi.org/10.5281/zenodo.1204408)

reStructedText 

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1204408.svg
   :target: https://doi.org/10.5281/zenodo.1204408

HTML 

<a href="https://doi.org/10.5281/zenodo.1204408"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.1204408.svg" alt="DOI"></a>

Image URL 

https://zenodo.org/badge/DOI/10.5281/zenodo.1204408.svg

Target URL 

https://doi.org/10.5281/zenodo.1204408

Keyword(s):
savanna sustainable resource management ultrahigh-resolution photographic imaging conservation remote sensing animal detection
License (for files):
Academic Free License v3.0

Versions

Version 2.0 10.5281/zenodo.1204408 Mar 29, 2015
Version 1 10.5281/zenodo.16445 Mar 29, 2015
Cite all versions? You can cite all versions by using the DOI 10.5281/zenodo.609023. This DOI represents all versions, and will always resolve to the latest one. Read more.

Share

Cite as

Export