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Datasets:
fadel841
/
savmap

Tasks:
Object Detection
Modalities:
Image
Formats:
parquet
Size:
1K - 10K
Tags:
aerial-images
savmap
wildlife
savanna
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files Community
1
Dataset Viewer
Auto-converted to Parquet
Search is not available for this dataset
image
imagewidth (px)
2k
2k
width
int64
2k
2k
height
int64
2k
2k
objects
dict
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2,000
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Dataset Card for SAVMAP (Enhanced Annotations)

Dataset Summary

The SAVMAP dataset comprises ultrahigh-resolution aerial imagery captured by unmanned aerial vehicles (UAVs) over semi-arid savanna landscapes in Namibia. Originally released in 2015, the dataset includes raw JPEG images with embedded EXIF metadata (timestamp, latitude, longitude, altitude) and annotations identifying animals in the imagery.

⚠️ This version of the dataset includes improved annotations to address issues found in the original Micromappers campaign, which was known to include many false positives. The updated annotations were curated and validated by Paul Allin, a PhD candidate at Stellenbosch University (South Africa), providing a more accurate basis for training and evaluating computer vision models in ecological monitoring tasks.

⚠️ Disclaimer: this dataset is not identical to the "version 2" hosted on Zenodo.org. The images have been cropped to 2000*2000 and only a portion of the negative samples (i.e. empty images) have been selected. The dataset comprises 3545 negative samples and 379 positive samples.

Supported Tasks and Use Cases

  • Object Detection: Training and evaluation of animal detection models in ultrahigh-resolution aerial imagery.
  • Remote Sensing for Ecology: Analyzing habitat use and wildlife distributions in savanna environments.
  • Biodiversity Conservation: Informing sustainable land-use strategies and conservation interventions.

Languages

Non-linguistic (imagery and geospatial data); metadata and documentation are provided in English.

Dataset Creation

Curation Rationale

The original SAVMAP dataset was developed to demonstrate the use of UAV imaging for near real-time ecological monitoring and sustainable land-use planning. However, the reliance on crowdsourced annotations introduced quality issues. To make the dataset more useful for machine learning and conservation efforts, a new set of expert-curated annotations has been developed.

Source Data

  • Imagery: Captured in May 2014 over a Namibian savanna reserve using EPFL-designed UAVs.
  • Expert Annotations: Created by Paul Allin (Stellenbosch University), focusing on accuracy, removing false positives, and validating animal locations.

Annotation Process

  • Enhanced (Paul Allin): Bounding boxes were created and reviewed manually by an ecologist with domain expertise, ensuring significantly improved data quality. The dataset has 379 positive samples (image with annotations) and 3545 negative samples (images without annotations).

Licensing Information

The dataset is released under the Academic Free License v3.0. Please cite the original dataset creators and acknowledge the enhanced annotations as described below.

Citation Information

Please cite both the original creators and the updated annotation contributor as follows:

Original Dataset:

Reinhard, F., Parkan, M., Produit, T., Betschart, S., Bacchilega, B., Hauptfleisch, M. L., Meier, P., SAVMAP Consortium, & Joost, S. (2015). 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) (Version 2.0). Zenodo. https://doi.org/10.5281/zenodo.1204408

Enhanced Annotations:

Enhanced annotations curated by Paul Allin, PhD candidate, Stellenbosch University, South Africa (2025). For correspondence regarding expert annotations, contact [email protected].

Contributions

  • Original Dataset Team:
    Friedrich Reinhard, Matthew Parkan, Timothée Produit, Sonja Betschart, Morgan L. Hauptfleisch, Patrick Meier, Beatrice Bacchilega, Stéphane Joost, SAVMAP Consortium.

  • Enhanced Annotations:
    Paul Allin, PhD candidate, Stellenbosch University, South Africa.

  • Dataset Engineering and Integration:
    Fadel Mamar Seydou, Independent ML researcher – contributed to dataset cleanup, reformatting annotations for ML pipelines, and preparing the release for Hugging Face compatibility.

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