Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Models for paper "From crowd to herd counting: How to precisely detect and count African mammals using aerial imagery and deep learning?"

Domain:

geospatialagriculture

Record type:

model
Creator:
DelFouThéBus
Editor:
Del
Publisher:
ULi
Host:avatar
HerdNet architecture parameters (stored in '.pth' files), trained separately on : nadir aerial images, containing 6 African wildlife species, from the general dataset of Delplanque et al. (2022), oblique aerial images, containing 3 African livestock species, from the Ennedi dataset of Delplanque et al. (2023). These files have been obtained using the HerdNet code (v0.1.0), published on Github: Alexandre-Delplanque/HerdNet These pre-trained models follow the CC BY-NC-SA-4.0 license and are available for academic research purposes only, no commercial use is permitted.

Visit

doi.orgdataverse.uliege.be

Tasks

computer visionimage classification

Similar

Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapesUsing very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes - DatasetLivestock Detection and Counting in Kenyan Rangelands Using Aerial Imagery and Deep Learning TechniquesDataset Code for paper: "Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks"Object Counting from Aerial Remote Sensing Images: Application to Wildlife and Marine MammalsCounting Sea Lions and Elephants from Aerial Photography using Deep Learning with Density Maps

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

(Uploaded by Plazi for the Bat Literature Project) Satellites allow large-scale surveys to be conduc

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

This repository contains a dataset of satellite images for African Elephant (Loxodonta africana) det

Livestock Detection and Counting in Kenyan Rangelands Using Aerial Imagery and Deep Learning Techniques

Accurate livestock counts are essential for effective pastureland management. High spatial resolutio

Dataset Code for paper: "Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks"

This dataset contains aerial images, model result files and the code used in the paper: Multispecies

Object Counting from Aerial Remote Sensing Images: Application to Wildlife and Marine Mammals

International audience Anthropogenic activities pose threats to wildlife and marine f

Counting Sea Lions and Elephants from Aerial Photography using Deep Learning with Density Maps

The ability to automatically count animals is important to design appropriate environmental