Individual tree crown detection and delineation (ITCD) mainly depend on high-resolution aerial photos and satellite images or LiDAR, and these data can be costly to obtain. The advent of unmanned aerial vehicle (UAV) remote sensing technology provides an economic and effective method for data acquisition. Therefore, the research of ITCD based on UAV high-resolution images is of significance to improve the efficiency and accuracy of forest resource inventory and remote sensing validation. However, in very high-resolution (defined here with the pixel size smaller than 10 cm) images, the inhomogeneities in the canopy texture can be detrimental to the correct detection of individual trees by computer processing. We applied the bias field estimation, which is used in medical image segmentation, to reduce the within-canopy spectral heterogeneity in the very high-resolution UAV-derived orthophoto. By selecting young Osmanthus and Podocarpus trees that grow in a nursery as the study objects, we tested our method in an orthophoto (with a ground resolution of 2.5 cm) generated from overlapping UAV images. A local intensity clustering was first applied to produce a smoothed bias field image; then, by using morphological operation of opening and closing, the fine texture of the canopy was further smoothed. Finally, individual tree crowns were extracted by applying the marker-controlled watershed segmentation algorithm. The segmentation results were validated by comparing to the manually drawn individual tree crown polygons. The $F$-scores of the detection rate for Osmanthus and Podocarpus were 98.2% and 93.1%, respectively. The result is considerably better than those achieved with similar processing steps but without the bias field treatment. Our study proves that it is feasible and effective to detect and delineate individual tree crowns based on the bias field and marker-controlled watershed segmentation in very-high-resolution images obtained from UAV.

Considering the problem of rapid collection and reliable transmission of emergency information, as well as the emergency response missions and natural environment characteristics of emergency sites, this paper proposes a novel UAV path planning framework that considers waypoint and motion. First, the waypoint planning improves the reliability of the uplink and downlink data transmission to the greatest extent by jointly optimizing unmanned aerial vehicles’ (UAV) 3D position and transmit power of UAV and users’ equipment (UE). Second, the motion planning allows the UAVs to traverse all waypoints in the shortest amount of time, which allows more time for data collection by maximizing the acceleration of each UAV. Our results show that the proposed path planning algorithm can significantly improve the normalized Quality of Experience (QoE) index and effectively shorten the flight time of each UAV for different assignments. Therefore, the method proposed in this paper can effectively solve the key problems in the technical system of emergency response and provide a scientific decision-making basis for post-disaster emergency response.

Rapid acquisition of environmental parameters of emergency sites is crucial for emergency response. However, in areas where public ground networks do not provide sufficient coverage or are destroyed by disasters, the rapid backhaul of monitoring data from emergency sites is difficult. Due to the urgent demand for data collection in such areas, we propose an unmanned aerial vehicle (UAV)-aided emergency environmental monitoring system using a LoRa mesh networking approach, which can quickly build a large range of wireless communication networks suitable for small data transmission. First, we designed a LoRa-based mesh network protocol by using a custom slotted ALOHA medium access mechanism. Second, we designed and implemented a custom software and hardware platform of the monitoring system. The system adopts a communication mode, including multiple sensor nodes (SNs), multiple relay nodes (RNs), a gateway (GW), and a network server. Finally, we demonstrated the performance of the protocol and the system by in-lab and outdoor tests. The results show that when data rate is 0.293 kb/s, the line-of-sight 1-hop transmission distance is more than 10 km, and the 1-hop transmission delay is less than 6 s; the end-to-end delay has an upper bound, which can meet the real-time requirement of emergency response; the least upper bound of throughput per slot of our mesh protocol is much higher than that of LoRaWAN. Evaluation results also demonstrate the network robustness to environmental changes.

Cultural heritage is closely linked with individual historical figures, who become a key focus for cultural tourism. Confucianism laid the foundation for much of Chinese civilization, and Confucius and Mencius have been studied extensively and have been influential in many parts of the world. Zhu Xi, the founder and master of Neo-Confucianism (an important part of the Mount Wuyi world cultural heritage), has received less research attention. With the big bang of the metaverse, research on the immersive cultural heritage digital documentation and information service for historical figures has become a new perspective. This paper presents a metaverse-based digital documentation framework for historical figures. This framework addresses the digitization of multimodal data for cultural heritage and adapts to the needs of metaverse presentations. For cultural research and cultural preservation, the paper has explored a combined contactless virtual and real cultural heritage experience system. For tourists, we intend to develop an immersive and holistic cultural tourism information service before, during and after the tour. In particular, we have also developed the Zhu Xi metaverse system. This is a step forward in the construction of a metaverse of historical figures.

Semantic segmentation is one of the fundamental tasks in understanding high-resolution aerial images. Recently, convolutional neural network (CNN) and fully convolutional network (FCN) have achieved excellent performance in general images’ semantic segmentation tasks and have been introduced to the field of aerial images. In this paper, we propose a novel deep FCN with channel attention mechanism (CAM-DFCN) for high-resolution aerial images’ semantic segmentation. The CAM-DFCN architecture follows the mode of encoder–decoder. In the encoder, two identical deep residual networks are both divided into multiple levels and acted on spectral images and auxiliary data, respectively. Then, the feature map concatenation is carried out at each level. In the decoder, the channel attention mechanism (CAM) is introduced to automatically weigh the channels of feature maps to perform feature selection. On the one hand, the CAM follows the concatenated feature maps at each level to select more discriminative features for classification. On the other hand, the CAM is used to further weigh the semantic information and spatial location information in the adjacent-level concatenated feature maps for more accurate predictions. We evaluate the proposed CAM-DFCN by using two benchmarks (the Potsdam set and the Vaihingen set) provided by the International Society for Photogrammetry and Remote Sensing. Experimental results show that the proposed method has considerable improvement.