Aerial imagery is increasingly used in Earth science and natural resource
management as a complement to labor-intensive ground-based surveys. Aerial
systems can collect overlapping images that provide multiple views of each
location from different perspectives. However, most prediction approaches (e.g.
for tree species classification) use a single, synthesized top-down
"orthomosaic" image as input that contains little to no information about the
vertical aspects of objects and may include processing artifacts. We propose an
alternate approach that generates predictions directly on the raw images and
accurately maps these predictions into geospatial coordinates using semantic
meshes. This method$\unicode{x2013}$released as a user-friendly open-source
toolkit$\unicode{x2013}$enables analysts to use the highest quality data for
predictions, capture information about the sides of objects, and leverage
multiple viewpoints of each location for added robustness. We demonstrate the
value of this approach on a new benchmark dataset of four forest sites in the
western U.S. that consists of drone images, photogrammetry results, predicted
tree locations, and species classification data derived from manual surveys. We
show that our proposed multiview method improves classification accuracy from
53% to 75% relative to an orthomosaic baseline on a challenging cross-site tree
species classification task.

利用多视角方法，通过地面测量、无人机图像、摄影测量结果和手动调查的物种分类数据，将航拍图像直接生成预测，并准确地映射到地理空间坐标中，在树种分类任务中相对于传统地平面合成图像基准，将分类准确率从 53% 提高到 75%。

使用语义网格从多视角航空影像分类地理空间对象

Classifying geospatial objects from multiview aerial imagery using  semantic meshes

Urban trees help regulate temperature, reduce energy consumption, improve
urban air quality, reduce wind speeds, and mitigating the urban heat island
effect. Urban trees also play a key role in climate change mitigation and
global warming by capturing and storing atmospheric carbon-dioxide which is the
largest contributor to greenhouse gases. Automated tree detection and species
classification using aerial imagery can be a powerful tool for sustainable
forest and urban tree management. Hence, This study first offers a pipeline for
generating labelled dataset of urban trees using Google Map's aerial images and
then investigates how state of the art deep Convolutional Neural Network models
such as VGG and ResNet handle the classification problem of urban tree aerial
images under different parameters. Experimental results show our best model
achieves an average accuracy of 60% over 6 tree species.

研究使用谷歌地图的航空影像生成标记的城市树木数据集，并探讨现代深度卷积神经网络模型如 VGG 和 ResNet 在不同参数下处理城市树木航拍图像分类问题的方法和效果，研究结果表明，最佳模型在 6 种树种上平均准确率达到了 60％以上。