High Definition (HD) Maps are highly accurate 3D maps that contain features on or nearby the road that assist with navigation in Autonomous Vehicles (AVs). One of the main challenges when making such maps is the automatic extraction and classification of road markings from mobile mapping data. In this paper, a methodology is proposed to use transfer learning to extract and classify road markings from mobile LiDAR. The data procedure includes preprocessing, training, class extraction and accuracy assessment. Initially, point clouds were filtered and converted to intensity-based images using several grid-cell sizes. Then, it was manually annotated and split to create the training and testing datasets. The training dataset has undergone augmentation before serving as input for evaluating multiple openly available pre-trained neural network models. The models were then applied to the testing dataset and assessed based on their precision, recall, and F1 scores for extraction as well as their error rates for classification. Further processing generated classified point clouds and polygonal vector shapefiles. The results indicate that the best model is the pre-trained U-Net model trained from the intensity-based images with a 5 cm resolution among the other models and training sets that were used. It was able to achieve F1 scores that are comparable with recent work and error rates that are below 15%. However, the classification results are still around two to four times greater than those of recent work and as such, it is recommended to separate the extraction and classification procedures, having a step in between to remove misclassifications.

This study developed a novel semi-automatic ground control point (GCPs) matching model, which can resolve the problem of GCPs matching when carrying out geometric correction for two UAV images. This research methods utilized the concept of the Biological Sequence Algorithms (BSA) to present image matching procedures. More specifically, the Needleman-Wunsch algorithm (NWA) was first used as a global object alignment technique to match objects in the two images (corrected image and uncorrected image). After identifying the successfully matched objects, the Smith-Waterman Algorithm (SWA) was used as a local features alignment technique to extract the GCPs from matched objects. Finally, the polynomial model method was applied for geometric correction and assessment of the proposed model. Finally, the results of this case showed that appropriate GCPs were automatically extracted from the images used in this study. After the geometric correction, the RMSE (Root-Mean-Square Error) value was 0.52418, indicating the method of this study is appropriate for the application on high-resolution images.

Recently, the issue of maintenance and preservation of cultural heritage has been widely given attention. However, physical cultural heritage in wilderness areas would suffer from the threat of destruction due to the long-term terrain and environmental influences. This paper considered the flexibility of a multispectral sensor mounted by an Unmanned Aerial Vehicle (UAV) for small-scale monitoring to acquire the multi-temporal UAV images for the two national historic sites (i.e., the Chen-Jian and Chen-Zhen monuments) in the wilderness areas of Kinmen and propose an environmental risk assessment model. In the model, meteorology and soil moisture are regarded as the hazard factors, while the slope of terrains is regarded as the vulnerability factor. By an inter-quartile range statistic for the analyses of the monitoring dataset, the environment risk was described into four grades to produce the thematic maps of environment risk. The experimental result demonstrates that the produced multi-temporal thematic maps of environment risk can present the environment risk grades borne by the different portions of the two monuments in detail. Thus, the thematic maps not only can serve as references for installing the mitigation facilities while performing a restoration plan, but it can also effectively indicate the seasonal differences in the environment risk grades between the Chen-Jian monument and the Chen-Zhen monument.

Presently, coastal features of Taiwan are threatened under the environment of climate change and the growth of the marine waste. The issue of coastal waste cannot be delayed any more. The most traditional method of disposing of coastal waste is to organize beach cleaning activities by coastal government agencies. That is, the private environmental groups using a large amount of manpower to manage the coast. The comparison of time cost and implementation efficiency is not ideal. In view of the development of GIS, the ability of remote sensing technology can capture a wide range of data in a short period of time. On the other hands, we can see many practical applications that replace traditional survey methods. This research is based on the application of remote sensing technology combined with machine learning to display the observation of our seashore. In this study, the Ensemble learning model is used to implement object classification. More specifically, Random Forest (RF) algorithm and Extra Trees (ET) algorithm are applied to explore different algorithms. The classification effects of the model constructed by established process are different. The data for the machine learning classification is visualized by the thematic map. The contributions can be applied to coastal waste related environmental studies effectively and practically.