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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes - Dataset

Authors: Duporge, Isla;

Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes - Dataset

Abstract

This repository contains a dataset of satellite images for African Elephant (Loxodonta africana) detection. The dataset is from “Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes” Remote Sensing in Ecology and Conservation. The dataset consists of 600x600 sub-images extracted from World-View-3 and Word-View-4 satellite images (c) Maxar Technology acquired between 2014 and 2019 in Addo Elephant National Park in South Africa. Each sub-image is manually labelled with bounding boxes around individual elephants. The data is split into train and test sets. Labels are provided in the csv files with filenames referring to the images in the corresponding image folders. How to cite: Duporge, I., Isupova, O., Reece, S., Macdonald, D.W. and Wang, T., 2021. Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes. Remote Sensing in Ecology and Conservation, 7(3), pp.369-381.

Related Organizations
Keywords

Satellite Imagery, Artificial Intelligence, Wildlife conservation, object detection, Computer vision

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average