Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation

Abstract--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 multiscale 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. However, it comes at the cost of large amounts of training data that need to contain I. Since Worldwide efforts are being made to protect forests, slow manual annotation is time-consuming, researchers have turned the rate of deforestation, and reduce the negative impacts toward video game engines that are able to emulate real world of environmental degradation. Researchers have successfully scenarios, from urban cities to natural habitats.