The interaction between elephants and their environment has profound
implications for both ecology and conservation strategies. This study presents
an analytical approach to decipher the intricate patterns of elephant movement
in Sub-Saharan Africa, concentrating on key ecological drivers such as seasonal
variations and rainfall patterns. Despite the complexities surrounding these
influential factors, our analysis provides a holistic view of elephant
migratory behavior in the context of the dynamic African landscape. Our
comprehensive approach enables us to predict the potential impact of these
ecological determinants on elephant migration, a critical step in establishing
informed conservation strategies. This projection is particularly crucial given
the impacts of global climate change on seasonal and rainfall patterns, which
could substantially influence elephant movements in the future. The findings of
our work aim to not only advance the understanding of movement ecology but also
foster a sustainable coexistence of humans and elephants in Sub-Saharan Africa.
By predicting potential elephant routes, our work can inform strategies to
minimize human-elephant conflict, effectively manage land use, and enhance
anti-poaching efforts. This research underscores the importance of integrating
movement ecology and climatic variables for effective wildlife management and
conservation planning.

通过分析大象在非洲撒哈拉以南地区的迁徙行为，特别关注季节变化和降雨模式等关键生态驱动因子，我们综合的研究方法能够预测这些生态决定因素对大象迁徙的潜在影响，从而为制定明智的保护策略提供重要依据。这项研究旨在推进运动生态学的理解，并促进撒哈拉以南非洲人类与大象之间的可持续共存之道。

撒哈拉以南非洲大象行为分析：生态、气候和保护视角

Analysis of Elephant Movement in Sub-Saharan Africa: Ecological,  Climatic, and Conservation Perspectives

Artificial intelligence (AI) and machine learning (ML) present revolutionary
opportunities to enhance our understanding of animal behavior and conservation
strategies. Using elephants, a crucial species in Africa's protected areas, as
our focal point, we delve into the role of AI and ML in their conservation.
Given the increasing amounts of data gathered from a variety of sensors like
cameras, microphones, geophones, drones, and satellites, the challenge lies in
managing and interpreting this vast data. New AI and ML techniques offer
solutions to streamline this process, helping us extract vital information that
might otherwise be overlooked. This paper focuses on the different AI-driven
monitoring methods and their potential for improving elephant conservation.
Collaborative efforts between AI experts and ecological researchers are
essential in leveraging these innovative technologies for enhanced wildlife
conservation, setting a precedent for numerous other species.

运用人工智能和机器学习技术，本文聚焦于不同的 AI 驱动监测方法及其提高大象保护效果的潜力。

大象与算法：人工智能在大象监测中的现有和未来作用综述

Elephants and Algorithms: A Review of the Current and Future Role of AI  in Elephant Monitoring

Identifying animals from a large group of possible individuals is very
important for biodiversity monitoring and especially for collecting data on a
small number of particularly interesting individuals, as these have to be
identified first before this can be done. Identifying them can be a very
time-consuming task. This is especially true, if the animals look very similar
and have only a small number of distinctive features, like elephants do. In
most cases the animals stay at one place only for a short period of time during
which the animal needs to be identified for knowing whether it is important to
collect new data on it. For this reason, a system supporting the researchers in
identifying elephants to speed up this process would be of great benefit. In
this paper, we present such a system for identifying elephants in the face of a
large number of individuals with only few training images per individual. For
that purpose, we combine object part localization, off-the-shelf CNN features,
and support vector machine classification to provide field researches with
proposals of possible individuals given new images of an elephant. The
performance of our system is demonstrated on a dataset comprising a total of
2078 images of 276 individual elephants, where we achieve 56% top-1 test
accuracy and 80% top-10 accuracy. To deal with occlusion, varying viewpoints,
and different poses present in the dataset, we furthermore enable the analysts
to provide the system with multiple images of the same elephant to be
identified and aggregate confidence values generated by the classifier. With
that, our system achieves a top-1 accuracy of 74% and a top-10 accuracy of 88%
on the held-out test dataset.

本文结合目标部位定位、CNN 特征和支持向量机分类，建立了一个系统，通过少量的图片来识别大量的大象。系统在含有 2078 张 276 只大象的数据集上测试，最高测试准确率达到 56％和 80％，采用多张图片技术后测试准确率为 74％和 88％。