Humans use UAVs to monitor changes in forest environments since they are
lightweight and provide a large variety of surveillance data. However, their
information does not present enough details for understanding the scene which
is needed to assess the degree of deforestation. Deep learning algorithms must
be trained on large amounts of data to output accurate interpretations, but
ground truth recordings of annotated forest imagery are not available. To solve
this problem, we introduce a new large aerial dataset for forest inspection
which contains both real-world and virtual recordings of natural environments,
with densely annotated semantic segmentation labels and depth maps, taken in
different illumination conditions, at various altitudes and recording angles.
We test the performance of two multi-scale neural networks for solving the
semantic segmentation task (HRNet and PointFlow network), studying the impact
of the various acquisition conditions and the capabilities of transfer learning
from virtual to real data. Our results showcase that the best results are
obtained when the training is done on a dataset containing a large variety of
scenarios, rather than separating the data into specific categories. We also
develop a framework to assess the deforestation degree of an area.

通过使用 UAV 监测森林环境的变化，本研究介绍了一个新的大型航拍数据集，用于森林巡视任务，该数据集包含了真实和虚拟记录的自然环境，密集标注的语义分割标签和深度图，并研究了不同采集条件下多尺度神经网络的性能以及从虚拟数据到真实数据的迁移学习能力，结果表明，最佳结果来自于包含多种情景的数据集，而非将数据按特定类别分离。还开发了一个评估地区森林破坏程度的框架。

航空语义分割和深度估计的森林巡查数据集

Forest Inspection Dataset for Aerial Semantic Segmentation and Depth  Estimation

A great deal of research has demonstrated recently that multi-view stereo
(MVS) matching can be solved with deep learning methods. However, these efforts
were focused on close-range objects and only a very few of the deep
learning-based methods were specifically designed for large-scale 3D urban
reconstruction due to the lack of multi-view aerial image benchmarks. In this
paper, we present a synthetic aerial dataset, called the WHU dataset, we
created for MVS tasks, which, to our knowledge, is the first large-scale
multi-view aerial dataset. It was generated from a highly accurate 3D digital
surface model produced from thousands of real aerial images with precise camera
parameters. We also introduce in this paper a novel network, called RED-Net,
for wide-range depth inference, which we developed from a recurrent
encoder-decoder structure to regularize cost maps across depths and a 2D fully
convolutional network as framework. RED-Net's low memory requirements and high
performance make it suitable for large-scale and highly accurate 3D Earth
surface reconstruction. Our experiments confirmed that not only did our method
exceed the current state-of-the-art MVS methods by more than 50% mean absolute
error (MAE) with less memory and computational cost, but its efficiency as
well. It outperformed one of the best commercial software programs based on
conventional methods, improving their efficiency 16 times over. Moreover, we
proved that our RED-Net model pre-trained on the synthetic WHU dataset can be
efficiently transferred to very different multi-view aerial image datasets
without any fine-tuning. Dataset are available at this http URL

本研究提出了一个名为 WHU 的合成航空数据集，该数据集是首个大规模的多视角航空数据集，并介绍了一种名为 RED-Net 的新型神经网络，专门用于高精度的三维地球表面重建。通过实验，我们证实了该方法的性能和效率，在减少内存和计算成本的同时，使 MAE 提高了 50％以上，并且比传统方法的一些最佳商业软件的效率提高了 16 倍。