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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.
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readme.txt
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savmap_dataset_v2.zip
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  • 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
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