Identification of Tree Species in Japanese Forests based on Aerial Photography and Deep Learning

Natural Disaster Classification using Aerial Photography Explainable for Typhoon Damaged Feature

Recent years, typhoon damages has become social problem owing to climate change. In 9 September 2019, Typhoon Faxai passed on the Chiba in Japan, whose damages included with electric provision stop because of strong wind recorded on the maximum 45 meter per second. A large amount of tree fell down, and the neighbor electric poles also fell down at the same time. These disaster features have caused that it took 18 days for recovery longer than past ones. Immediate responses are important for faster recovery. As long as we can, aerial survey for global screening of devastated region would be req…

利用档案航空摄影和深度学习分析纳米比亚数十年的环境变化

Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning

本研究提出了一种基于深度语义分割模型的工作流程，通过类别加权、伪标签和p值过滤等策略分析纳米比亚历史航空图像，成功识别水坑、宅院和大树等关键目标并揭示其长期环境变化。

基于无人机点云与航空影像的树木检测方法综述

A Comprehensive Review on Tree Detection Methods Using Point Cloud and Aerial Imagery from Unmanned Aerial Vehicles

本文综述了基于无人机点云与航空影像的树木检测方法，按LiDAR/DAP和是否使用深度学习分类，统计发现DL方法已成为主流（占45%），为相关研究和农业应用提供指导。

Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment

Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the aesthetics of aerial videos that can provide valuable information for improving the aesthetic quality of aerial photography. In this article, we present a method of deep multimodality learning for UAV video aesthetic quality assessment. More specifically, a multistream framework is designed to exploit aesthetic attributes from multiple modalities, including spatial appearance, drone camera motion, and scene structure. A novel specially designed motion stream network is…

Automatic classification of trees using a UAV onboard camera and deep learning

Automatic classification of trees using remotely sensed data has been a dream of many scientists and land use managers. Recently, Unmanned aerial vehicles (UAV) has been expected to be an easy-to-use, cost-effective tool for remote sensing of forests, and deep learning has attracted attention for its ability concerning machine vision. In this study, using a commercially available UAV and a publicly available package for deep learning, we constructed a machine vision system for the automatic classification of trees. In our method, we segmented a UAV photography image of forest into individual t…

CedarCypress3D：人工林杉树和柏树中单株树木的带标注无人机激光雷达数据集

CedarCypress3D: an annotated UAV-LiDAR dataset of individual trees in planted cedar and cypress forests

本文提出CedarCypress3D，为温带人工林提供含1627棵树标注的无人机激光雷达数据集，可支撑单株树木分割等相关研究，且部分样地附带地面激光雷达数据。