Point cloud data segmentation, filtering, classification, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR (Light Detection and Ranging) data segmentation. The DBSCAN method needs at least two parameters: The minimum number of points minPts, and the searching radius ε. However, the parameter ε is often harder to determine, which hinders the application of the DBSCAN method in point cloud segmentation. Therefore, a segmentation algorithm based on DBSCAN is proposed with a novel automatic parameter ε estimation method—Estimation Method based on the average of k nearest neighbors’ maximum distance—with which parameter ε can be calculated on the intrinsic properties of the point cloud data. The method is based on the fitting curve of k and the mean maximum distance. The method was evaluated on different types of point cloud data: Airborne, and mobile point cloud data with and without color information. The results show that the accuracy values using ε estimated by the proposed method are 75%, 74%, and 71%, which are higher than those using parameters that are smaller or greater than the estimated one. The results demonstrate that the proposed algorithm can segment different types of LiDAR point clouds with higher accuracy in a robust manner. The algorithm can be applied to airborne and mobile LiDAR point cloud data processing systems, which can reduce manual work and improve the automation of data processing.

In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI’s capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughts. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes.

Keywords: Lidar Data, Point Cloud, Hadoop, PCL, HDFS, MapReduce, GIS, Distributed Computing Light Detection and Ranging (LiDAR) is one of the most promising technologies in surveying and mapping，city management, forestry, object recognition, computer vision engineer and others. However, it is challenging to efficiently storage, query and analyze the high-resolution 3D LiDAR data due to its volume and complexity. In order to improve the productivity of Lidar data processing, this study proposes a Hadoop-based framework to efficiently manage and process LiDAR data in a distributed and parallel manner, which takes advantage of Hadoop’s storage and computing ability. At the same time, the Point Cloud Library (PCL), an open-source project for 2D/3D image and point cloud processing, is integrated with HDFS and MapReduce to conduct the Lidar data analysis algorithms provided by PCL in a parallel fashion. The experiment results show that the proposed framework can efficiently manage and process big LiDAR data.

High spatial resolution (HSR) remote sensing images have a wide range of application prospects in the fields of urban planning, agricultural planning and military training. Therefore, the research on the semantic segmentation of remote sensing images becomes extremely important. However, large data volume and the complex background of HSR remote sensing images put great pressure on the algorithm efficiency. Although the pressure on the GPU can be relieved by down-sampling the image or cropping it into small patches for separate processing, the loss of local details or global contextual information can lead to limited segmentation accuracy. In this study, we propose a multi-field context fusion network (MCFNet), which can preserve both global and local information efficiently. The method consists of three modules: a backbone network, a patch selection module (PSM), and a multi-field context fusion module (FM). Specifically, we propose a confidence-based local selection criterion in the PSM, which adaptively selects local locations in the image that are poorly segmented. Subsequently, the FM dynamically aggregates the semantic information of multiple visual fields centered on that local location to enhance the segmentation of these local locations. Since MCFNet only performs segmentation enhancement on local locations in an image, it can improve segmentation accuracy without consuming excessive GPU memory. We implement our method on two high spatial resolution remote sensing image datasets, DeepGlobe and Potsdam, and compare the proposed method with state-of-the-art methods. The results show that the MCFNet method achieves the best balance in terms of segmentation accuracy, memory efficiency, and inference speed.

Ocean monitoring depends on the widely deployed oceanographic buoys and observation stations that integrate various types of ocean sensors. Those ocean sensors often work in harsh environments, so the data collected by the sensors are sometimes abnormal, which affects the accuracy of downstream applications, e.g., the ocean data assimilation and intelligent data mining. It is not realistic to conduct abnormal data detection (quality control) on huge amounts of increasing data only rely on human-based services. Hence, current research tendency is adopting AI models to realize automatic quality control, which largely depends on the accuracy of the AI model in ocean data modeling and prediction. Hence, in order to accurately model and predict the observation data, this paper explores and compares various sequential data modeling methods, which are widely adopted in time series data forecasting. We further proposes a Transformer model for accurate real-time prediction of those data. The experimental results show that our proposed method is better than the baselines both in single-step and multiple-step prediction experimental settings. As far as we know, this is the first work that applying transformer based model in ocean observation data intelligent analysis.