Semantic segmentation is a fundamental but challenging problem of pixel-level remote sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite images play an important role in a wide range of applications. Recently, with the successful applications of deep learning (DL) in the computer vision (CV) field, more and more researchers have introduced and improved DL methods to the task of RS data semantic segmentation and achieved excellent results. Although there are a large number of DL methods, there remains a deficiency in the evaluation and advancement of semantic segmentation techniques for RS data. To solve the problem, this paper surveys more than 100 papers in this field in the past 5 years and elaborates in detail on the aspects of technical framework classification discussion, datasets, experimental evaluation, research challenges, and future research directions. Different from several previously published surveys, this paper first focuses on comprehensively summarizing the advantages and disadvantages of techniques and models based on the important and difficult points. This research will help beginners quickly establish research ideas and processes in this field, allowing them to focus on algorithm innovation without paying too much attention to datasets, evaluation indicators, and research frameworks.

In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations.

In the Internet of Things (IoT) era, various devices generate massive videos containing rich human relations. However, the long-distance transmission of huge videos may cause congestion and delays, and the large gap between the visual and relation spaces brings about difficulties for relation analysis. Hence, this study explores an edge-cloud intelligence framework and two algorithms for cooperative relation extraction and analysis from videos based on an IoT system. First, we exploit a cooperative mechanism on the edges and cloud, which can schedule the relation recognition and analysis subtasks from massive video streams. Second, we propose a Multi-Granularity relation recognition Model (MGM) based on coarse and fined granularity features. This means that better mapping is established for identifying relations more accurately. Specifically, we propose an entity graph based on Graph Convolutional Networks (GCN) with an attention mechanism, which can support comprehensive relationship reasoning. Third, we develop a Community Detection based on the Ensemble Learning model (CDEL), which leverages a heterogeneous skip-gram model to perform node embedding and detect communities. Experiments on SRIV datasets and four movie videos validate that our solution outperforms several competitive baselines.