Data acquisition in animal ecology is rapidly accelerating due to inexpensive
and accessible sensors such as smartphones, drones, satellites, audio recorders
and bio-logging devices. These new technologies and the data they generate hold
great potential for large-scale environmental monitoring and understanding, but
are limited by current data processing approaches which are inefficient in how
they ingest, digest, and distill data into relevant information. We argue that
machine learning, and especially deep learning approaches, can meet this
analytic challenge to enhance our understanding, monitoring capacity, and
conservation of wildlife species. Incorporating machine learning into
ecological workflows could improve inputs for population and behavior models
and eventually lead to integrated hybrid modeling tools, with ecological models
acting as constraints for machine learning models and the latter providing
data-supported insights. In essence, by combining new machine learning
approaches with ecological domain knowledge, animal ecologists can capitalize
on the abundance of data generated by modern sensor technologies in order to
reliably estimate population abundances, study animal behavior and mitigate
human/wildlife conflicts. To succeed, this approach will require close
collaboration and cross-disciplinary education between the computer science and
animal ecology communities in order to ensure the quality of machine learning
approaches and train a new generation of data scientists in ecology and
conservation.

利用机器学习技术，结合生态领域知识，可以提升动物生态学家们对现代传感器技术产生的大量数据的理解和对物种保护的监测能力，最终将生态模型作为机器学习模型的约束条件，从而可靠地估计种群数量、研究动物行为和减缓人类 / 野生动物冲突。

机器学习在野生动物保护中的视角：视野中的生物多样性

Seeing biodiversity: perspectives in machine learning for wildlife  conservation

Deformable templates are essential to large-scale medical image registration,
segmentation, and population analysis. Current conventional and deep
network-based methods for template construction use only regularized
registration objectives and often yield templates with blurry and/or
anatomically implausible appearance, confounding downstream biomedical
interpretation. We reformulate deformable registration and conditional template
estimation as an adversarial game wherein we encourage realism in the moved
templates with a generative adversarial registration framework conditioned on
flexible image covariates. The resulting templates exhibit significant gain in
specificity to attributes such as age and disease, better fit underlying
group-wise spatiotemporal trends, and achieve improved sharpness and
centrality. These improvements enable more accurate population modeling with
diverse covariates for standardized downstream analyses and easier anatomical
delineation for structures of interest.

通过在生成敌对式登录框架中利用灵活的图像协变量 - 使得移动的模板更真实，作者重新制定了可变形模板，以获得更好的特异性和适应性，该模板能更好地拟合潜在的群体空间 - 时间趋势，并使解剖学结构更容易划分。